Enhancing post machining surface finish of titanium alloy by cutting parameter optimization using ANOVA analysis

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The machinability of titanium alloy, Ti6Al4V using statistical methods such as Analysis of Variance (ANOVA) is investigated in this paper. Ti6Al4V is the most widely used titanium alloy in aerospace and biomedical application due to its advantageous material properties. However, despite its wide-ranging applications, there is a lack of clarity concerning its ideal machining parameters. This ambiguity primarily stems from Ti6Al4V's inherent properties, notably its low thermal conductivity and high chemical reactivity. Understanding and optimizing the machining parameters to get the right combination of speed, feed, depth of cut and coolant condition is vital. To gather comprehensive insights, a series of machining trials were conducted at various combinations of cutting parameters. The effects of varying the selected parameters on a crucial machining performance indicators-surface roughness was considered. Orthogonal arrays, known for their robustness in experiment design, were chosen to structure the machining trials. Furthermore, to decipher the collected data and interpret the results, ANOVA techniques were utilized with the help of R programming. The insights garnered can lead to more streamlined machining strategies, ensuring higher productivity and efficiency. By bridging the knowledge gap, this research seeks to make machining titanium alloys simpler, cost effective and more efficient for manufacturers. Machinability Titanium alloy Ti6Al4V ANOVA Optimization Machining parameters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Titanium and its alloys are well known for their exceptional mechanical properties such as creep resistance, anti-corrosive and lightweight nature. Among the titanium alloys, Ti6Al4V (Titanium + 6% Aluminium + 4% Vanadium) stands out due to its wider utilization across various engineering sectors [ 1 ]. With an excellent strength-to-weight ratio, high-temperature fatigue resistance and biocompatibility, Ti6Al4V finds applications in aerospace, automotive and bio- medical industry [ 2 ]. In the aerospace industry, Ti6Al4V high strength-to-weight ratio makes it an ideal choice for aircraft components, reducing overall weight and enhancing fuel efficiency [ 3 ]. In the biomedical field, its biocompatibility and corrosion resistance render it suitable for surgical implants and medical devices. However, machining the material requires addressing challenges like tool wear and work hardening [ 4 ]. One significant problem in Ti6Al4V machining is tool wear and machinability [ 5 ]. The strong chemical reactivity of titanium alloys with the tool material can lead to surface hardening and high temperatures at the tool-chip interface, causing rapid tool wear. To combat this, advanced coatings like TiAlN are applied to tools, enhancing their wear resistance. The effective use of cutting fluid also helps to reduce wear resistance [ 6 ]. Another challenge in machining titanium is excessive tool wear, work hardening, and poor chip control which occurs due to the low thermal conductivity [ 7 ]. Overcoming these challenges requires the optimization of machining parameters using high-pressure coolant and employing advanced tool geometries to ensure efficient and cost-effective machining. The exploration of parametric optimization in machining titanium alloys has become a focal point of research due to its potential to develop efficient and economically viable machining processes [ 8 ]. While earlier studies primarily relied on conventional optimization techniques, the complexity of the task at hand demands a more thorough investigation and refinement [ 9 ]. The typical alpha-beta microstructure of Ti6Al4V is shown in Fig. 1 . Machining titanium alloys encompasses a variety of processes, including milling, turning, drilling, and grinding. Achieving optimal machining conditions is necessary for a deep understanding of material properties, tool characteristics and cutting parameters. The selection and optimization of machining parameters, such as depth of cut, cutting speed, feed rate, tool dimensions and presence of cutting fluid, significantly impact machining performance, productivity and surface quality [ 10 ]. To enhance Ti6Al4V machining, this project is increasingly focusing on parametric optimization techniques tailored for hard metals. By systematically varying and optimizing machining parameters, researchers can pinpoint combinations of cutting parameters that maximize material removal rates, minimize tool wear, reduce cutting forces and enhance surface finish [ 11 ]. Parametric optimization methods encompass various techniques, including experimental design, statistical analysis, response surface methodology, genetic algorithms, and AI-based optimization. These techniques shed light on the intricate relationships between machining parameters, material properties, and performance, leading to the development of optimized machining strategies [ 12 ]. In this project we made use of orthogonal arrays and ANOVA analysis to optimize the parameters. In conclusion, optimizing machining parameters and use of cutting fluids are pivotal for efficient and effective machining of Ti6Al4V alloy [ 13 ]. 1.1 Machinability Machinability is a measure of how manageable it is for a material to be cut, shaped, or formed while providing relatively good surface finish, minimum tool wear, less power consumption and better dimensional accuracy [ 14 ]. The machinability of a material is dictated by materials innate physical and mechanical properties. Some properties that can determine a materials machinability rating are its hardness, yield strength, modulus of elasticity and compressive strength [ 15 ]. Other factors that influence machinability include workpiece material properties, tool nomenclature and machining process parameters [ 16 ]. Due to the complex nature of any metal cutting operations, it is difficult to create a relationship that quantitively defines machinability. Therefore, machinability becomes a qualitative evaluation rather than quantitative in some cases [ 17 ]. 1.2 Literature review Literature review embarks on an in- depth journey through existing research, scientific findings, and practical insights to shed light on the pivotal role of machining parameters, in tandem with appropriate use of cutting fluids. Central to this research is the acknowledgement of the multidimensional nature of machining titanium alloys, where the optimization of performance indicators must harmonize with economic feasibility [ 18 ]. This paper highlights the need for optimization strategies that seamlessly align both these objectives. The role of cutting fluids in machining titanium alloys, well known for its low thermal conductivity, has been reviewed [ 19 ]. Contemporary investigations, encompassing dry turning, minimum quantity lubrication and beyond, have unveiled their profound impact of coolant on thermal conductivity, directly influencing surface finish and machining quality [ 20 , 21 ]. Intriguingly, a specialized semi-synthetic microemulsion cutting fluid has demonstrated remarkable potential [ 22 ]. Coolant application has yielded substantial benefits, including improved surface quality, diminished tool wear, and optimized chip formation during machining [ 20 ]. The main challenges identified revolve around the alloy low thermal conductivity and heightened chemical reactivity, culminating in escalated tool wear and suboptimal surface finishes [ 23 ]. Addressing these challenges necessitates innovative solutions, such as leveraging cutting fluids and formulating optimized machining parameters that holistically account for both performance and cost-related considerations. Consequently, the current research endeavors to present a pragmatic and implementable parametric optimization strategy, aimed at surmounting existing hurdles and contributing to the progression of the field. Statistical analysis methods, including orthogonal arrays and ANOVA techniques are envisioned as pivotal tools in achieving these results [ 24 ]. Table 1 Chemical composition of titanium alloy, Ti6Al4V Elements V Al C Fe O N H Ti Percentage (by weight) 4.37 6.48 0.016 0.17 0.17 0.03 0.005 Balance 2 Experimental methods The chemical composition of titanium alloys was evaluated using spectrometry and weight percentages have been tabulated in Table 1 . The trials were accomplished using a single block of titanium of dimensions, 150x70x20mm as shown in Fig. 2 . Kistler dynamometer was used to measure the cutting force during the trials as shown in Fig. 2 . The experimental design for this paper consists of slot milling to the selected cutting parameters. The variable cutting parameters selected for the trials are shown in Table 2 . A two set of samples, trial 1–36 and 37–72 to machine with coolant on and off condition was used. Water soluble oil was used as a coolant during the trials. The trials were conducted using a combination of cutting conditions which includes three cutting speeds of 60, 80 and 100 m/min; four feed rates of 0.05, 0.1,0.15 and 0.2 mm/rev; three depth of cuts1, 1.5 and 2 mm respectively. The methodology embraces the utilization of factorial design techniques such as orthogonal array. This systematic approach facilitates the exploration of the concurrent impact of multiple parameters, thereby informing their interdependencies. A comprehensive compilation of data related to the required machining performance such as the surface roughness was collected for analysis using ANOVA. ANOVA code was written using the programming language R program. By utilising the orthogonal array techniques, we could reduce the number of trials to 72. All samples were cut along to a length of 30m along the breadth. To maintain consistency, a set of 36 tests with coolant on was completed in a single set up. The cutting tool used is a 6 mm solid carbide end mill with 30 o rake angle. The zero-reference tool wear condition was ensured by using a new tool for every twelve trials. The surface roughness measurements for each trial were evaluated using a Taylor Hobson surface profilometer as shown in Fig. 3 . The stepwise experimental design for evaluating the machinability of titanium alloy Ti6Al4V using the ANOVA analysis is listed below. Designing and conducting meticulous machining experiments, capturing relevant and comprehensive data, and meticulously analysing the results. Performing in-depth material characterization tests to gain invaluable insights into the mechanical properties and composition of the titanium alloys. Leveraging advanced measurement techniques to quantify surface roughness, dimensional accuracy, and other critical quality attributes. Deploying analytical methods to dissect intricate cutting and tool wear mechanisms, illuminating the underlying processes. Formulating empirical models grounded in the wealth of experimental data to discern relationships between machining parameters and performance indicators. Implementing sophisticated simulation models to optimize tool paths and anticipate the trajectory of machining performance. Harnessing state-of-the-art optimization algorithms to identify the most optimal combinations of machining parameters, striking a balance between stability and economic feasibility. 3 Results and discussion The results obtained from the machining trials are listed in table.2. Table 2: Machining trials-parameters and outputs S no. Speed (V) (m/ min) Feed (f) (mm/rev) DoC (d) (mm) Coolant Fz (N) Surface Roughness (Ra) (μm) Time (min) MMR (mm^3/min) 1 60 0.05 1 0 92 0.216 0.21 0.96 2 60 0.05 1.5 0 160 0.280 0.21 1.43 3 60 0.05 2 0 207 0.496 0.22 1.91 4 60 0.1 1 0 139 0.707 0.11 1.91 5 60 0.1 1.5 0 236 1.072 0.11 2.86 6 60 0.1 2 0 330 1.336 0.11 3.82 7 60 0.15 1 0 203 0.896 0.07 2.87 8 60 0.15 1.5 0 279 1.176 0.07 4.30 9 60 0.15 2 0 406 1.759 0.07 5.73 10 60 0.2 1 0 329 1.293 0.06 3.82 11 60 0.2 1.5 0 372 0.188 0.05 5.73 12 60 0.2 2 0 478 0.391 0.05 7.64 13 80 0.05 1 0 162 0.182 0.05 1.27 14 80 0.05 1.5 0 157 0.379 0.05 1.91 15 80 0.05 2 0 253 0.578 0.06 2.55 16 80 0.1 1 0 230 0.940 0.08 2.55 17 80 0.1 1.5 0 241 0.806 0.08 3.82 18 80 0.1 2 0 324 0.997 0.08 5.10 19 80 0.15 1 0 308 1.333 0.16 3.82 20 80 0.15 1.5 0 374 1.228 0.16 5.73 21 80 0.15 2 0 381 1.431 0.16 7.64 22 80 0.2 1 0 393 1.903 0.04 5.10 23 80 0.2 1.5 0 383 1.495 0.04 7.64 24 80 0.2 2 0 461 2.503 0.04 10.19 25 100 0.05 1 0 164 0.294 0.13 1.59 26 100 0.05 1.5 0 201 0.317 0.13 2.39 27 100 0.05 2 0 223 0.508 0.13 3.18 28 100 0.1 1 0 258 6.058 0.06 3.18 29 100 0.1 1.5 0 256 3.581 0.06 4.78 30 100 0.1 2 0 291 1.507 0.07 6.37 31 100 0.15 1 0 375 4.156 0.05 4.78 32 100 0.15 1.5 0 338 4.037 0.04 7.17 33 100 0.15 2 0 366 1.292 0.04 9.55 34 100 0.2 1 0 433 1.160 0.03 6.37 35 100 0.2 1.5 0 376 2.925 0.03 9.55 36 100 0.2 2 0 455 1.047 0.03 12.74 37 60 0.05 1 1 263 0.180 0.21 0.96 38 60 0.05 1.5 1 404 0.205 0.21 1.43 39 60 0.05 2 1 549 0.128 0.21 1.91 40 60 0.1 1 1 256 0.432 0.11 1.91 41 60 0.1 1.5 1 392 0.405 0.11 2.87 42 60 0.1 2 1 537 0.578 0.11 3.82 43 60 0.15 1 1 269 0.543 0.07 2.87 44 60 0.15 1.5 1 406 0.650 0.07 4.30 45 60 0.15 2 1 562 0.890 0.07 5.73 46 60 0.2 1 1 340 0.605 0.06 3.82 47 60 0.2 1.5 1 465 0.650 0.05 5.73 48 60 0.2 2 1 606 0.549 0.05 7.64 49 80 0.05 1 1 248 0.659 0.16 1.27 50 80 0.05 1.5 1 407 0.887 0.16 1.91 51 80 0.05 2 1 460 0.750 0.16 2.55 52 80 0.1 1 1 271 0.653 0.08 2.55 53 80 0.1 1.5 1 434 0.521 0.08 3.82 54 80 0.1 2 1 493 0.742 0.08 5.10 55 80 0.15 1 1 280 0.573 0.05 3.82 56 80 0.15 1.5 1 367 0.518 0.05 5.73 57 80 0.15 2 1 461 0.564 0.05 7.64 58 80 0.2 1 1 325 0.682 0.04 5.10 59 80 0.2 1.5 1 442 0.384 0.04 7.64 60 80 0.2 2 1 562 0.444 0.04 10.19 61 100 0.05 1 1 230 0.219 0.13 1.59 62 100 0.05 1.5 1 329 0.172 0.13 2.39 63 100 0.05 2 1 452 0.291 0.13 3.18 64 100 0.1 1 1 214 0.623 0.06 3.18 65 100 0.1 1.5 1 332 0.569 0.07 4.78 66 100 0.1 2 1 458 1.355 0.07 6.37 67 100 0.15 1 1 198 0.520 0.04 4.78 68 100 0.15 1.5 1 304 1.148 0.04 7.17 69 100 0.15 2 1 484 1.113 0.04 9.55 70 100 0.2 1 1 330 1.190 0.03 6.37 71 100 0.2 1.5 1 430 0.792 0.03 9.55 72 100 0.2 2 1 545 0.115 0.03 12.74 3.1 ANOVA analysis The results from the ANOVA analysis are explained in this section. As said, ANOVA results are interpreted based on two parameters, P-Value and F-Value. ANOVA functions as an indispensable tool to dissect the voluminous experimental data harvested from machining trials. This statistical technique enables the discernment of statistical significance amidst varied machinability factors and their profound impact on the response variables. Table 3 ANOVA results for the output variable: surface roughness DoF Sum square Mean square F value P Value Cutting speed 1 7.81 7.813 11.343 0.0014 Feed rate 1 3.54 3.535 5.132 0.027 Depth of Cut (DoC) 1 0.45 0.451 0.655 0.421 Coolant 1 11.82 11.817 17.155 0.0001 Cutting speed: Feed rate 1 0.2 0.202 0.293 0.590 Cutting speed: DoC 1 2.13 2.125 3.085 0.085 Feed rate: DoC 1 0.17 0.172 0.25 0.619 Cutting speed: Coolant 1 4.55 4.552 6.608 0.013 Feed rate: Coolant 1 1.45 1.453 2.109 0.152 DoC: Coolant 1 0.73 0.732 1.063 0.307 Cutting speed: Feed rate: DoC 1 0 0 0.01 0.981 Cutting speed: Feed rate: Coolant 1 0.21 0.209 0.304 0.584 Cutting speed: DoC: Coolant 1 2.06 2.06 2.991 0.089 Feed rate: DoC: Coolant 1 0.03 0.026 0.038 0.846 Cutting speed: Feed rate: DoC: Coolant 1 0.38 0.379 0.551 0.461 The output shown in Table 3 is from an Analysis of Variance (ANOVA) test, which examines if there are statistically significant differences in the dependent variable in this case: surface roughness caused by the changes in the independent variables in this case: cutting speed, feed rate, depth and coolant and their interactions. Degrees of Freedom (DoF) tells us the number of values in the final calculation of a variable with inherent variations. The provided code extracts the mean square and F-value from the ANOVA results. Mean square value quantifies the variance squared differences from the mean explained by each factor or interaction. In simple terms, it tells us how much each factor or interaction contributes to the variability in the output. The given output shows that the mean square values range from nearly zero for cutting speed, feed rate and DoC to around 11.817 for coolant respectively. Generally, larger values indicate greater contribution to the variability in the response variable, but these values should be compared with the residual mean square to determine significance, which is done using the F-test. F- value is derived from the mean square values and it corroborates the hypothesis that the group means are equal. The larger the F value, the more likely it is that the groups have different means. From the provided output, coolant has the highest F-value of 17.155, indicating that this factor has a strong effect on the surface roughness. Factors with a small F-value (0.000565) such as the cutting speed, feed rate and DoC suggest a negligible effect on the response variable. P value is associated with the F-value which provides the probability of observing a result when the null hypothesis is true. The intensity bands are classified as p < 0.001; 0.001 ≤ p < 0.01; 0.01 ≤ p < 0.05; 0.05 ≤ p < 0.1 and p ≥ 0.1. For a lower ‘p’ value, higher is the effect of one independent variable over the process outputs. 3.1.1 Cutting speed Vs surface roughness (Ra): The effect of a single factor input variable, cutting speed on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on = 0.001376. The p-value obtained is very low, indicating a significant effect on the surface roughness. Scatter plot in Fig. 4 depicts the relationship between the cutting speed and surface roughness. As the cutting speed increases, there is an increase in surface roughness as depicted by the regression line. However, it is noteworthy that for each distinct cutting speed value, there is a range of surface roughness values, suggesting that other factors not shown on this graph might be influencing the surface roughness. The linear regression line, represented in blue, indicates a general negative trend which means as the cutting speed increases, the surface roughness tends to decrease. 3.1.2 Feed rate Vs surface roughness: Figure 5 shows the effect of a single factor input variable, feed rate on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on P = 0.027375. The p-value obtained is low, indicating a significant effect on the surface roughness. The scatter plot reveals that for lower federate there is not a big effect on the surface roughness, but the effect is clear for higher speeds. 3.1.3 Depth Of Cut Vs surface roughness: Figure 6 shows the effect of a single factor input variable, DoC on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on P = 0.421615. The p-value obtained is in a high range, indicating a less significant effect on the surface roughness. The scatter plot reveals that for lower DoC there is not a big effect on the surface roughness, but the effect is clear for higher speeds. The green trendline, which remains horizontal across the range of depths, indicates that there is not a pronounced linear relationship between DoC and surface roughness within this dataset. 3.1.4 Coolant Vs surface roughness: Scatter plot in Fig. 7 depicts the relationship between the coolant use and surface roughness. From the ANOVA results, the significance of the factor is evaluated based on P = 0.000117. The p-value obtained is in an exceptionally low range, indicating a massive effect on the surface roughness. The blue trendline, signifying operations with coolant, remains consistent across the range of cutting speeds. This indicates that the application of coolant stabilizes surface roughness, mitigating the rise observed without its use. While increasing cutting speeds tend to deteriorate surface finish in the absence of coolant, the introduction of coolant effectively counters this trend, maintaining a more consistent surface roughness. The 3D plot in Fig. 6 highlights the synergistic effect of cutting speed, DoC and feed rate on surface roughness in a machining operation. Each data point color corresponds to its feed rate, as depicted by the color gradient on the right. 4 Conclusion Machining processes are known to be complex and are affected by various parameters. From ANOVA analysis and observing the P-value and F -value, the effect of cutting parameters on the output variable surface roughness is evident. The role of coolant, having the highest P-value, is found to be crucial in determining the surface roughness. When higher speeds are used, the necessity for coolants becomes evident to ensure an acceptable surface finish. The effect of feed rate and depth of cut, on the other hand, appear to be more subdued. On the contrary, a significant relationship between feed rate and surface roughness has not been observed. The utility of coolants is found to be paramount, especially at increased speeds, to achieve a consistent surface finish. While these relationships have been identified, it is believed that the model's precision could benefit from more extensive data analysis. From these observations, it is concluded that for the optimization of any machining process, a comprehensive perspective that considers all these variables concurrently is essential. Declarations The authors hereby declare that no external funding is involved in supporting this research. There are no conflicts of interests associated with paper. The paper consists of authors own research work and not been submitted anywhere for publication. References Boyer, R., Attributes, characteristics, and applications of titanium and its alloys. Jom, 2010. 62 (5): p. 21-24. Polishetty, A., et al., Cutting force and surface finish analysis of machining additive manufactured titanium alloy Ti-6Al-4V. 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Wear, 1983. 92 (1): p. 113-123. Goldberg, M., et al. Machinability Assessment and Surface Integrity Characteristics of Austempered Ductile Iron (ADI) Using Ultra-Hard Cutting Tools . in 3rd International Conference on Machining & Grinding . 1999. Cincinati, Ohio: Society of Manufacturing Engineers. Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 02 May, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers invited by journal 15 Apr, 2024 Editor assigned by journal 15 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Polishetty","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8572-6024","institution":"AUT: Auckland University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ashwin","middleName":"","lastName":"Polishetty","suffix":""},{"id":291215315,"identity":"68b71329-07d2-4d24-9f2c-867b0a32bda4","order_by":1,"name":"Guy Littlefair","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guy","middleName":"","lastName":"Littlefair","suffix":""}],"badges":[],"createdAt":"2024-04-09 23:47:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244240/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-024-13878-0","type":"published","date":"2024-06-17T15:13:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54916586,"identity":"453b9158-b592-43e4-af90-8fe40bad25be","added_by":"auto","created_at":"2024-04-18 14:18:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":975698,"visible":true,"origin":"","legend":"\u003cp\u003eTypical microstructure of titanium alloy Ti6Al4V\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/1569c4a80e0f9234e4cdba88.png"},{"id":54916589,"identity":"125c8735-3412-4d2c-9464-6b9067d25dcb","added_by":"auto","created_at":"2024-04-18 14:18:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":425480,"visible":true,"origin":"","legend":"\u003cp\u003eMachining set up with the dynamometer\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/60056695b57c5d9b5b89b593.png"},{"id":54916587,"identity":"71b23300-11a1-4a51-a785-d78e85448a68","added_by":"auto","created_at":"2024-04-18 14:18:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":942946,"visible":true,"origin":"","legend":"\u003cp\u003eSurface roughness measurement set up using talysurf\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/5be74d2d9cbbfa6a5107e667.png"},{"id":54916582,"identity":"215aec57-f0ab-4e8c-8397-d613cc643ba3","added_by":"auto","created_at":"2024-04-18 14:18:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30810,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of cutting speed on surface roughness\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/8d465fe554fe84a06faccfaa.png"},{"id":54917105,"identity":"f404cac3-1436-43f7-bc29-7c125f5f8936","added_by":"auto","created_at":"2024-04-18 14:26:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":27962,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of feed rate on surface roughness\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/c02607d8f2bb54c24c624fcc.png"},{"id":54916584,"identity":"c74bde09-33cb-4e28-8321-b3cb527dbaed","added_by":"auto","created_at":"2024-04-18 14:18:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16303,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of DoC on surface roughness\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/07167ae7ce006bfc7e14b1cf.png"},{"id":54916585,"identity":"c863da8a-da84-4ee7-af41-29c3e74f9091","added_by":"auto","created_at":"2024-04-18 14:18:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":419538,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative effect of three factors- feed rate; depth of cut and cutting speed on surface roughness.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/b476fc90b7b0dfb31d1d99c7.png"},{"id":58823037,"identity":"fb620921-d7fb-4f22-95f2-2d464b865095","added_by":"auto","created_at":"2024-06-21 16:51:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4737866,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244240/v1/772b0299-63ed-4b16-8284-329a15d849f4.pdf"}],"financialInterests":"","formattedTitle":"Enhancing post machining surface finish of titanium alloy by cutting parameter optimization using ANOVA analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTitanium and its alloys are well known for their exceptional mechanical properties such as creep resistance, anti-corrosive and lightweight nature. Among the titanium alloys, Ti6Al4V (Titanium\u0026thinsp;+\u0026thinsp;6% Aluminium\u0026thinsp;+\u0026thinsp;4% Vanadium) stands out due to its wider utilization across various engineering sectors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With an excellent strength-to-weight ratio, high-temperature fatigue resistance and biocompatibility, Ti6Al4V finds applications in aerospace, automotive and bio- medical industry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the aerospace industry, Ti6Al4V high strength-to-weight ratio makes it an ideal choice for aircraft components, reducing overall weight and enhancing fuel efficiency [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the biomedical field, its biocompatibility and corrosion resistance render it suitable for surgical implants and medical devices. However, machining the material requires addressing challenges like tool wear and work hardening [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. One significant problem in Ti6Al4V machining is tool wear and machinability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The strong chemical reactivity of titanium alloys with the tool material can lead to surface hardening and high temperatures at the tool-chip interface, causing rapid tool wear. To combat this, advanced coatings like TiAlN are applied to tools, enhancing their wear resistance. The effective use of cutting fluid also helps to reduce wear resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Another challenge in machining titanium is excessive tool wear, work hardening, and poor chip control which occurs due to the low thermal conductivity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Overcoming these challenges requires the optimization of machining parameters using high-pressure coolant and employing advanced tool geometries to ensure efficient and cost-effective machining. The exploration of parametric optimization in machining titanium alloys has become a focal point of research due to its potential to develop efficient and economically viable machining processes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile earlier studies primarily relied on conventional optimization techniques, the complexity of the task at hand demands a more thorough investigation and refinement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The typical alpha-beta microstructure of Ti6Al4V is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Machining titanium alloys encompasses a variety of processes, including milling, turning, drilling, and grinding. Achieving optimal machining conditions is necessary for a deep understanding of material properties, tool characteristics and cutting parameters. The selection and optimization of machining parameters, such as depth of cut, cutting speed, feed rate, tool dimensions and presence of cutting fluid, significantly impact machining performance, productivity and surface quality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To enhance Ti6Al4V machining, this project is increasingly focusing on parametric optimization techniques tailored for hard metals. By systematically varying and optimizing machining parameters, researchers can pinpoint combinations of cutting parameters that maximize material removal rates, minimize tool wear, reduce cutting forces and enhance surface finish [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParametric optimization methods encompass various techniques, including experimental design, statistical analysis, response surface methodology, genetic algorithms, and AI-based optimization. These techniques shed light on the intricate relationships between machining parameters, material properties, and performance, leading to the development of optimized machining strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this project we made use of orthogonal arrays and ANOVA analysis to optimize the parameters. In conclusion, optimizing machining parameters and use of cutting fluids are pivotal for efficient and effective machining of Ti6Al4V alloy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Machinability\u003c/h2\u003e \u003cp\u003eMachinability is a measure of how manageable it is for a material to be cut, shaped, or formed while providing relatively good surface finish, minimum tool wear, less power consumption and better dimensional accuracy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The machinability of a material is dictated by materials innate physical and mechanical properties. Some properties that can determine a materials machinability rating are its hardness, yield strength, modulus of elasticity and compressive strength [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other factors that influence machinability include workpiece material properties, tool nomenclature and machining process parameters [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Due to the complex nature of any metal cutting operations, it is difficult to create a relationship that quantitively defines machinability. Therefore, machinability becomes a qualitative evaluation rather than quantitative in some cases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Literature review\u003c/h2\u003e \u003cp\u003eLiterature review embarks on an in- depth journey through existing research, scientific findings, and practical insights to shed light on the pivotal role of machining parameters, in tandem with appropriate use of cutting fluids. Central to this research is the acknowledgement of the multidimensional nature of machining titanium alloys, where the optimization of performance indicators must harmonize with economic feasibility [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This paper highlights the need for optimization strategies that seamlessly align both these objectives. The role of cutting fluids in machining titanium alloys, well known for its low thermal conductivity, has been reviewed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Contemporary investigations, encompassing dry turning, minimum quantity lubrication and beyond, have unveiled their profound impact of coolant on thermal conductivity, directly influencing surface finish and machining quality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Intriguingly, a specialized semi-synthetic microemulsion cutting fluid has demonstrated remarkable potential [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Coolant application has yielded substantial benefits, including improved surface quality, diminished tool wear, and optimized chip formation during machining [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The main challenges identified revolve around the alloy low thermal conductivity and heightened chemical reactivity, culminating in escalated tool wear and suboptimal surface finishes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Addressing these challenges necessitates innovative solutions, such as leveraging cutting fluids and formulating optimized machining parameters that holistically account for both performance and cost-related considerations. Consequently, the current research endeavors to present a pragmatic and implementable parametric optimization strategy, aimed at surmounting existing hurdles and contributing to the progression of the field. Statistical analysis methods, including orthogonal arrays and ANOVA techniques are envisioned as pivotal tools in achieving these results [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical composition of titanium alloy, Ti6Al4V\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercentage (by weight)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2 Experimental methods","content":"\u003cp\u003eThe chemical composition of titanium alloys was evaluated using spectrometry and weight percentages have been tabulated in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The trials were accomplished using a single block of titanium of dimensions, 150x70x20mm as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Kistler dynamometer was used to measure the cutting force during the trials as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe experimental design for this paper consists of slot milling to the selected cutting parameters. The variable cutting parameters selected for the trials are shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A two set of samples, trial 1\u0026ndash;36 and 37\u0026ndash;72 to machine with coolant on and off condition was used. Water soluble oil was used as a coolant during the trials. The trials were conducted using a combination of cutting conditions which includes three cutting speeds of 60, 80 and 100 m/min; four feed rates of 0.05, 0.1,0.15 and 0.2 mm/rev; three depth of cuts1, 1.5 and 2 mm respectively. The methodology embraces the utilization of factorial design techniques such as orthogonal array. This systematic approach facilitates the exploration of the concurrent impact of multiple parameters, thereby informing their interdependencies. A comprehensive compilation of data related to the required machining performance such as the surface roughness was collected for analysis using ANOVA. ANOVA code was written using the programming language R program. By utilising the orthogonal array techniques, we could reduce the number of trials to 72. All samples were cut along to a length of 30m along the breadth. To maintain consistency, a set of 36 tests with coolant on was completed in a single set up. The cutting tool used is a 6 mm solid carbide end mill with 30\u003csup\u003eo\u003c/sup\u003e rake angle. The zero-reference tool wear condition was ensured by using a new tool for every twelve trials. The surface roughness measurements for each trial were evaluated using a Taylor Hobson surface profilometer as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The stepwise experimental design for evaluating the machinability of titanium alloy Ti6Al4V using the ANOVA analysis is listed below.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDesigning and conducting meticulous machining experiments, capturing relevant and comprehensive data, and meticulously analysing the results.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerforming in-depth material characterization tests to gain invaluable insights into the mechanical properties and composition of the titanium alloys.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLeveraging advanced measurement techniques to quantify surface roughness, dimensional accuracy, and other critical quality attributes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeploying analytical methods to dissect intricate cutting and tool wear mechanisms, illuminating the underlying processes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFormulating empirical models grounded in the wealth of experimental data to discern relationships between machining parameters and performance indicators.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImplementing sophisticated simulation models to optimize tool paths and anticipate the trajectory of machining performance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHarnessing state-of-the-art optimization algorithms to identify the most optimal combinations of machining parameters, striking a balance between stability and economic feasibility.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"3 Results and discussion","content":"\u003cp\u003eThe results obtained from the machining trials are listed in table.2.\u003c/p\u003e\n\u003cp\u003eTable 2: Machining trials-parameters and outputs\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eS no.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpeed (V) (m/ min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed (f) (mm/rev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoC (d) (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFz (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurface Roughness (Ra) (\u0026mu;m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR (mm^3/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"56\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"4\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e2.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e6.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e3.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e4.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e4.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e2.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.484641638225256%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.675767918088738%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.433447098976108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.139931740614335%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.313993174061434%\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.552901023890787%\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.726962457337883%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.945392491467576%\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n 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\u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n 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\u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n 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width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n 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width=\"12.095400340715502%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n 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width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e1.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e1.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.6439522998296425%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.095400340715502%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.798977853492334%\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073253833049403%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.584327086882453%\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.1 ANOVA analysis\u003c/h2\u003e\n \u003cp\u003eThe results from the ANOVA analysis are explained in this section. As said, ANOVA results are interpreted based on two parameters, P-Value and F-Value. ANOVA functions as an indispensable tool to dissect the voluminous experimental data harvested from machining trials. This statistical technique enables the discernment of statistical significance amidst varied machinability factors and their profound impact on the response variables.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eANOVA results for the output variable: surface roughness\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDoF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth of Cut (DoC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: Feed rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: DoC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed rate: DoC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed rate: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoC: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: Feed rate: DoC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: Feed rate: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: DoC: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed rate: DoC: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed: Feed rate: DoC: Coolant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe output shown in Table \u003cspan\u003e3\u003c/span\u003e is from an Analysis of Variance (ANOVA) test, which examines if there are statistically significant differences in the dependent variable in this case: surface roughness caused by the changes in the independent variables in this case: cutting speed, feed rate, depth and coolant and their interactions. Degrees of Freedom (DoF) tells us the number of values in the final calculation of a variable with inherent variations. The provided code extracts the mean square and F-value from the ANOVA results. Mean square value quantifies the variance squared differences from the mean explained by each factor or interaction. In simple terms, it tells us how much each factor or interaction contributes to the variability in the output. The given output shows that the mean square values range from nearly zero for cutting speed, feed rate and DoC to around 11.817 for coolant respectively. Generally, larger values indicate greater contribution to the variability in the response variable, but these values should be compared with the residual mean square to determine significance, which is done using the F-test. F- value is derived from the mean square values and it corroborates the hypothesis that the group means are equal. The larger the F value, the more likely it is that the groups have different means. From the provided output, coolant has the highest F-value of 17.155, indicating that this factor has a strong effect on the surface roughness. Factors with a small F-value (0.000565) such as the cutting speed, feed rate and DoC suggest a negligible effect on the response variable. P value is associated with the F-value which provides the probability of observing a result when the null hypothesis is true. The intensity bands are classified as p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 0.001\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; 0.01\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and p\u0026thinsp;\u0026ge;\u0026thinsp;0.1. For a lower \u0026lsquo;p\u0026rsquo; value, higher is the effect of one independent variable over the process outputs.\u003c/p\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1.1 Cutting speed Vs surface roughness (Ra):\u003c/h2\u003e\n \u003cp\u003eThe effect of a single factor input variable, cutting speed on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on =\u0026thinsp;0.001376. The p-value obtained is very low, indicating a significant effect on the surface roughness.\u003c/p\u003e\n \u003cp\u003eScatter plot in Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e depicts the relationship between the cutting speed and surface roughness. As the cutting speed increases, there is an increase in surface roughness as depicted by the regression line. However, it is noteworthy that for each distinct cutting speed value, there is a range of surface roughness values, suggesting that other factors not shown on this graph might be influencing the surface roughness. The linear regression line, represented in blue, indicates a general negative trend which means as the cutting speed increases, the surface roughness tends to decrease.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1.2 Feed rate Vs surface roughness:\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan\u003e5\u003c/span\u003e shows the effect of a single factor input variable, feed rate on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on P\u0026thinsp;=\u0026thinsp;0.027375. The p-value obtained is low, indicating a significant effect on the surface roughness. The scatter plot reveals that for lower federate there is not a big effect on the surface roughness, but the effect is clear for higher speeds.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1.3 Depth Of Cut Vs surface roughness:\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan\u003e6\u003c/span\u003e shows the effect of a single factor input variable, DoC on the output variable, surface roughness is described in this section. From the ANOVA results, the significance of the factor is evaluated based on P\u0026thinsp;=\u0026thinsp;0.421615. The p-value obtained is in a high range, indicating a less significant effect on the surface roughness. The scatter plot reveals that for lower DoC there is not a big effect on the surface roughness, but the effect is clear for higher speeds. The green trendline, which remains horizontal across the range of depths, indicates that there is not a pronounced linear relationship between DoC and surface roughness within this dataset.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.1.4 Coolant Vs surface roughness:\u003c/h2\u003e\n \u003cp\u003eScatter plot in Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e depicts the relationship between the coolant use and surface roughness. From the ANOVA results, the significance of the factor is evaluated based on P\u0026thinsp;=\u0026thinsp;0.000117. The p-value obtained is in an exceptionally low range, indicating a massive effect on the surface roughness. The blue trendline, signifying operations with coolant, remains consistent across the range of cutting speeds. This indicates that the application of coolant stabilizes surface roughness, mitigating the rise observed without its use. While increasing cutting speeds tend to deteriorate surface finish in the absence of coolant, the introduction of coolant effectively counters this trend, maintaining a more consistent surface roughness. The 3D plot in Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e highlights the synergistic effect of cutting speed, DoC and feed rate on surface roughness in a machining operation. Each data point color corresponds to its feed rate, as depicted by the color gradient on the right.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eMachining processes are known to be complex and are affected by various parameters. From ANOVA analysis and observing the P-value and F -value, the effect of cutting parameters on the output variable surface roughness is evident. The role of coolant, having the highest P-value, is found to be crucial in determining the surface roughness. When higher speeds are used, the necessity for coolants becomes evident to ensure an acceptable surface finish. The effect of feed rate and depth of cut, on the other hand, appear to be more subdued. On the contrary, a significant relationship between feed rate and surface roughness has not been observed. The utility of coolants is found to be paramount, especially at increased speeds, to achieve a consistent surface finish. While these relationships have been identified, it is believed that the model's precision could benefit from more extensive data analysis. From these observations, it is concluded that for the optimization of any machining process, a comprehensive perspective that considers all these variables concurrently is essential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors hereby declare that no external funding is involved in supporting this research. There are no conflicts of interests associated with paper. The paper consists of authors own research work and not been submitted anywhere for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBoyer, R., \u003cem\u003eAttributes, characteristics, and applications of titanium and its alloys.\u003c/em\u003e Jom, 2010. \u003cstrong\u003e62\u003c/strong\u003e(5): p. 21-24.\u003c/li\u003e\n\u003cli\u003ePolishetty, A., et al., \u003cem\u003eCutting force and surface finish analysis of machining additive manufactured titanium alloy Ti-6Al-4V.\u003c/em\u003e Procedia Manufacturing, 2017. \u003cstrong\u003e7\u003c/strong\u003e: p. 284-289.\u003c/li\u003e\n\u003cli\u003eHenriques, V.A.R., et al., \u003cem\u003eProduction of titanium alloys for advanced aerospace systems by powder metallurgy.\u003c/em\u003e Materials Research, 2005. \u003cstrong\u003e8\u003c/strong\u003e: p. 443-446.\u003c/li\u003e\n\u003cli\u003ePradhan, S., et al., \u003cem\u003eInvestigation of machining characteristics of hard-to-machine Ti-6Al-4V-ELI alloy for biomedical applications.\u003c/em\u003e Journal of Materials Research and Technology, 2019. \u003cstrong\u003e8\u003c/strong\u003e(5): p. 4849-4862.\u003c/li\u003e\n\u003cli\u003eShunmugavel, M., \u003cem\u003eMachinability studies of selective laser melted titanium alloy Ti-6Al-4V\u003c/em\u003e. 2017, Deakin University.\u003c/li\u003e\n\u003cli\u003ePolishetty, A., B. 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Cincinati, Ohio: Society of Manufacturing Engineers.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Machinability, Titanium alloy Ti6Al4V, ANOVA, Optimization, Machining parameters","lastPublishedDoi":"10.21203/rs.3.rs-4244240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTitanium alloys are categorised under difficult to machine materials. The machinability of titanium alloy, Ti6Al4V using statistical methods such as Analysis of Variance (ANOVA) is investigated in this paper. Ti6Al4V is the most widely used titanium alloy in aerospace and biomedical application due to its advantageous material properties. However, despite its wide-ranging applications, there is a lack of clarity concerning its ideal machining parameters. This ambiguity primarily stems from Ti6Al4V's inherent properties, notably its low thermal conductivity and high chemical reactivity. Understanding and optimizing the machining parameters to get the right combination of speed, feed, depth of cut and coolant condition is vital. To gather comprehensive insights, a series of machining trials were conducted at various combinations of cutting parameters. The effects of varying the selected parameters on a crucial machining performance indicators-surface roughness was considered. Orthogonal arrays, known for their robustness in experiment design, were chosen to structure the machining trials. Furthermore, to decipher the collected data and interpret the results, ANOVA techniques were utilized with the help of R programming. The insights garnered can lead to more streamlined machining strategies, ensuring higher productivity and efficiency. By bridging the knowledge gap, this research seeks to make machining titanium alloys simpler, cost effective and more efficient for manufacturers.\u003c/p\u003e","manuscriptTitle":"Enhancing post machining surface finish of titanium alloy by cutting parameter optimization using ANOVA analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 14:18:45","doi":"10.21203/rs.3.rs-4244240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-05-03T01:36:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-15T07:05:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-15T06:57:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-15T06:49:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2024-04-11T19:27:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9171a60-d9a1-4097-9130-8796ed83b2a9","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:13:14+00:00","versionOfRecord":{"articleIdentity":"rs-4244240","link":"https://doi.org/10.1007/s00170-024-13878-0","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2024-06-17 15:13:13","publishedOnDateReadable":"June 17th, 2024"},"versionCreatedAt":"2024-04-18 14:18:45","video":"","vorDoi":"10.1007/s00170-024-13878-0","vorDoiUrl":"https://doi.org/10.1007/s00170-024-13878-0","workflowStages":[]},"version":"v1","identity":"rs-4244240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244240","identity":"rs-4244240","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}