Study on the Machining Accuracy of Soft Elastic Material Using Cryogenic Dicing

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However, as the processing dimensions continue to decrease, the machining accuracy of the workpiece becomes worse. In this work, a three-dimensional (3D) finite element model (FEM) of dicing processing is established by using finite element software ABAQUS and is compared with experimental results. Based on the established 3D dicing FEM, the influence of temperature, depth of cut, and spindle speed on the dicing process is studied. The results show that in the traditional soft elastic material dicing process, the increase in spindle speed and the decrease in processing dimensions led to the vibration of the tool, tilt and twist deformation of the workpiece, which become the main reasons for the poor machining accuracy. The application of cryogenic processing can effectively suppress the tilt and twist deformation of the workpiece. Under high spindle speed, cryogenic dicing can also somewhat mitigate the negative influence of tool vibration. FEM Soft elastic materials Cryogenic Dicing Deformation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Light-emitting diodes (LEDs) are widely used in display and lighting applications due to their low cost, long lifespan, and low power consumption [ 1 ], [ 2 ]. Among them, Mini/Micro-LED devices have become core components in emerging applications such as 4K/8K ultra-clear displays [ 3 ], vehicle displays [ 4 ], and wearable displays [ 5 ], [ 6 ] due to their advantages such as wide color gamut, high brightness, good reliability, miniaturization, and high power efficiency. In the manufacturing process of micro-LED display devices, packaging manufacturing is an important step to isolate the LED chip from the external environment, protect the chip from water, oxygen erosion, and mechanical damage, and improve the stability and lifespan of the LED device [ 7 ]. In recent years, improving the light output efficiency and high dynamic display performance of devices by introducing functional structures during the LED packaging manufacturing process has become a research hotspot [ 8 ]. However, common LED packaging materials such as silicone resin have the drawbacks of high elasticity, low hardness, and poor workability at room temperature, which are important reasons for the difficulty in manufacturing microstructures on the LED packaging surface [ 9 ], [ 10 ]. In traditional processing, soft elastic materials have increasingly serious problems, such as low processing accuracy and poor surface processing quality as the processing size decreases, a significant factor hindering the development of micro-LED devices. Using cryogenic-assisted processing methods is expected to solve the problem of insufficient machining accuracy of soft elastic packaging materials, achieving high-precision microstructures in the process of LED packaging preparation [ 11 ]. Cryogenic processing technology introduces cryogenic media into material processing [ 12 ]. It can effectively reduce the processing temperature, improve the surface quality of the processed material [ 13 ], and improve processing defects [ 14 ], achieving high-quality processing of difficult-to-machine materials. Cryogenic processing technology was initially applied to the processing of difficult-to-machine metals, and it has gradually been applied to other processing fields [ 15 ]. Wu et al. [ 16 ] applied cryogenic processing technology to process Ti-based alloy thin-walled parts and conducted comparative experimental analysis on different cutting conditions such as water cooling, micro-lubrication, and cryogenic micro-lubrication. The results showed that compared to water cooling and micro-lubrication, the maximum deformation was reduced by 41.4% and 44.7%, respectively, when the spindle speed was 4500 r/min, and the surface roughness of the workpiece was reduced by 34.5% and 45.8%, respectively. Altas et al. [ 17 ] studied the effect of cryogenic tool pre-treatment on the milling performance of NiTi shape memory alloy. They found that cryogenic treatment significantly improved the surface roughness of the processed material, averaging a reduction of about 7%. Jia et al. [ 18 ] first proposed the concept of an appropriate processing temperature range for CFRP. The results showed that the hardened matrix could better support the fibers and achieve higher surface quality at lower temperatures. When the cutting area temperature dropped below − 25°C, the surface roughness Ra reached 1.6 \(\mu m\) , showing good surface integrity. Friedrich [ 19 ] concluded that silicone and acrylic polymers are soft and difficult to process at room temperature. Their softness and adhesive properties are the main constraints for tool processing. Cryogenic processing technology can be applied to polymer processing, freezing the workpiece material and effectively enhancing its workability. This process helps to transform soft and elastic polymers into hard and brittle materials, thus significantly improving their mechanical properties and workability [ 20 ]. Song et al. [ 21 ] proposed a tool compensation method that considers the low-temperature shrinkage of PDMS to improve the geometric accuracy of processing. They also used the relationship between cutting temperature, other processing parameters, and surface roughness to generate surface microchannels with different roughnesses. Mechanical micro-machining technology has the advantages of geometric solid control and high surface smoothness, providing an effective manufacturing method for micro-machining soft polymer materials [ 22 ]. However, in the micro-machining of soft elastic materials, most studies have not discussed the deformation phenomenon during processing. A significant research challenge involves addressing the low processing accuracy frequently encountered during machining soft elastic materials. This work utilizes a three-dimensional (3D) finite element analysis to investigate the occurrence of deformation overcutting during the dicing process of soft elastic materials at varying temperatures and the influence of spindle speed on the dicing accuracy of these materials. The experimental results show that the tilt and torsion deformation of the workpiece are the reasons for the low machining accuracy in the traditional machining process, and cryogenic dicing significantly improves the machining accuracy of the soft elastic material. 2. Dicing Model and Dicing Conditions 2.1 Finite Element Model of Milling Observing and analyzing the entire machining process of soft elastic materials is a significant challenge due to the difficulty in observing the structural changes of the workpiece during micro-machining. To address this issue, a finite element model is created using the ABAQUS finite element simulation software to simulate the dicing process. This work investigates the elastic deformation rules of soft elastic materials during dicing at varying temperatures [ 23 ], [ 24 ]. A number of finite element models for continuous dicing are established in this work to examine the relationship between the dicing forces F x , F y , and workpiece deformation during the dicing process, as shown in Fig. 1 (a). The geometric model of the tool and the workpiece is created in a 1:1 ratio, as shown in Fig. 1 (b). The dicing depth is 0.25 mm, the workpiece is a single fin of 3×0.1×0.5 mm, the minimum mesh size is 0.005 mm, and the workpiece is divided into 435,400 meshes. The mesh type selected is C3D8R, and the workpiece material is an OE6650 soft elastic material. The workpiece is assigned predetermined 25°C and − 40°C, respectively. According to the mechanical properties test, the specific physical parameters of soft elastic materials at different temperatures are shown in Table 1 . The tool employed is a complete dicing wheel with an outer diameter of 28 mm, a tool thickness of 0.10 mm, a polyhedral abrasive grain, a grid seed of 0.1 mm, a grid number of 16,783, and a grid type of C3D4. During the dicing process of soft elastic materials, the tool is treated as a rigid body, and the tool material is diamond. The relevant physical parameters are provided in Table 2 . Table 1 Parameters of OE6650 in FEM Parameter Value Density 1160 kg/m 3 Young's Modulus 3.08 MPa (25℃)、73.54 MPa (-40℃) Poisson ratio 0.324 Table 2 Parameters of tool in FEM Parameter Value Density 3500kg/m 3 Young's Modulus 960000MPa Poisson ratio 0.2 2.2 Material Properties and Constitutive Model In this work, the J-C constitutive model is used to describe the dicing deformation behavior of soft elastic material OE6650. The model fully considers the relationship between stress and strain, strain rate, and temperature and is widely used in cutting simulation. J-C model equation is as follows [ 25 ] : $$\begin{array}{c}\sigma =\left(A+B{\epsilon }^{n}\right)\left(1+Cln\frac{\dot{\epsilon }}{\dot{{\epsilon }_{0}}}\right)\left[1-{\left(\frac{T-{T}_{r}}{{T}_{m}-{T}_{r}}\right)}^{m}\right]\#\left(Eq1\right)\end{array}$$ Where \(\sigma\) is the equivalent stress, A is the initial yield stress, B the is hardening modulus, ε is the equivalent strain, n is the hardening index, C is the strain rate constant, \(\dot{\epsilon }\) is the plastic strain rate, \({\dot{\epsilon }}_{0}\) is the reference plastic strain, T is the material temperature, \({T}_{r}\) is the ambient temperature, \({T}_{m}\) is the material melting point and m is the thermal softening index. According to the test results of mechanical properties, OE6650 has low dependence on strain rate, short processing time and sufficient cooling. The strain rate and temperature terms of the constitutive model will degenerate to constant 1, and the model will degenerate to : $$\begin{array}{c}\sigma =\left(A+B{\epsilon }^{n}\right)\#\left(Eq2\right)\end{array}$$ According to the stress-strain mechanical test of OE6650, the A, B, and n parameters of the Johnson-Cook model of OE6650 material at different temperatures are obtained by using the origin nonlinear curve fitting function, as shown in Table 3 . Table 3 Material constants of the Johnson-Cook model Temperature A B n 25℃ 0.72559MPa 12.034 1.84978 -40℃ 0.57639MPa 68.3844 0.91953 Dicing simulation requires the material's constitutive model and damage model. In the finite element analysis, the damage model is used as the failure criterion of the mesh in FEM, and the flexible damage is used as the damage model of the OE6650 dicing simulation [ 26 ]. The model parameters are shown in Table 4 . Table 4 OE6650 material damage model parameters Fracture strain Triaxial stress Strain rate Failure displacement 0.005 0.333 0.001 0.0001mm 3. Simulation Results and Analysis 3.1 Effect of Different Temperature on Dicing Based on the established FEM, the dicing process at 25°C was analyzed. The morphological changes in the workpiece throughout the dicing process are shown in Fig. 2 . In the initial stages of dicing, the degree of tilt deformation in the workpiece is significant. As the dicing depth increases, the dicing force approaches the bottom of the workpiece, gradually decreasing the tilt deformation. This occurrence is attributed to the low stiffness of the soft elastic material at room temperature, which possesses high ductility, deformation capacity, and deformation limits. Consequently, the workpiece experiences significant tilt deformation when subjected to dicing forces in the early stages of dicing. As the dicing process continues, the action point of dicing gradually moves towards the bottom of the workpiece, which has a higher stiffness, thereby reducing the tilt deformation of the workpiece. In addition to the tilt deformation, the workpiece also experiences torsional deformation during the dicing process, as shown in Fig. 3 . In the initial stages of dicing, the workpiece exhibits only a slight tilt. As the dicing depth increases, the torsional deformation progressively moves towards the cutting area, leading to an overcut of the workpiece. This occurrence can be attributed to the soft elasticity and low stiffness of the silicone material at room temperature. As the machining depth increases, the friction between the tool and the workpiece amplifies, resulting in a more significant torsion of the workpiece in the later stages of dicing. Consequently, the low machining accuracy of the workpiece in the small-size dicing process is due to the workpiece tilting into the cutting area in the early stage of dicing and twisting towards the tool in the later stage of dicing, as demonstrated in Fig. 4 . Based on the mechanical properties test of soft elastic material OE6650, Young's modulus is the most significant change in temperature reduction for a soft elastic material. When the temperature is lowered from 25°C to -40°C, the Young's modulus of the soft elastic material increases approximately 23-fold. Therefore, the dicing process is investigated under the condition of -40°C. Figure 5 displays the morphological changes in the workpiece throughout the dicing process. The workpiece demonstrates no tilt or torsional deformation throughout the dicing process and maintains a complete microcolumn shape. This is attributed to the fact that at -40°C, the temperature is below the material's glass transition temperature. As a result, the silicone material transitions from soft elasticity to hard brittleness, significantly enhancing its stiffness. Under the same processing conditions, the material can withstand a greater cutting force. Consequently, the workpiece deformation remains minimal and stable throughout the dicing process. 3.2 Effect of Different Cutting Depth on Dicing Force The FEM is employed to simulate the dicing process under varying depths of dicing at two distinct temperatures, 25°C and − 40°C. As illustrated in Fig. 6 , the peak values of the dicing forces, F x and F y , incrementally elevate with the increase in dicing depth. Notably, the maximum dicing peak force at -40°C is approximately 10-fold greater than that at 25°C. The disparity can be attributed to the material's mechanical properties at the two temperatures. At 25°C, the silicone material exhibits soft elasticity, resulting in a lack of precision in applying the dicing force, leading to significant deformation. In contrast, at -40°C, the low temperature renders the material hard and brittle, conferring a higher deformation resistance and the capacity to withstand a greater dicing force. Furthermore, the increase in dicing depth leads to an enlarged cutting area per unit of time, causing the dicing force to escalate concurrently with the dicing depth. 3.3 Influence of Different Spindle Speed on Dicing Force In order to further explore the processing peculiarities of materials under cryogenic dicing conditions, the dicing process at varying spindle speeds (-40°C) is simulated. As shown in Fig. 7 , the peak dicing forces, F x and F y , are significantly influenced by the spindle speed V c . When increasing the spindle speed from 3000 r/min to 12000 r/min, both dicing peak forces F x and F y exhibit a consistent decrease. This phenomenon can be attributed to the heightened centrifugal force experienced by the sand particles on the dicing wheel as the spindle speed rises. Consequently, the centripetal force acting on these particles intensifies, thereby augmenting their cutting efficacy during the dicing process. Simultaneously, the contact area between the sand particles and the workpiece diminishes, resulting in a reduced dicing force per unit area. Moreover, with prolonged dicing duration, the dicing point gradually shifts towards the stiffer bottom of the workpiece. Despite this displacement, the workpiece undergoes negligible deformation across various dicing speeds. 4. Experimental Results and Verification 4.1 Experimental Conditions and Method In this work, we establish a cryogenic processing platform (Fig. 8 (a)). The cryogenic system consists of components such as regulating valves, flowmeters, and LN 2 self-priming pumps. LN 2 can be mixed with air in different proportions to achieve the control requirements of different jet temperatures. The mechanical processing equipment used is an automatic dicing machine (HP-6101 CETC Beijing Electronic Equipment Company Ltd.). By jetting the cryogenic fluid into the processing area, we can cryogenically treat the workpiece. During the experiment, we employed silicone material (Dow Corning OE6650 resin, A and B parts with a mass ratio 1:3) as the workpiece. The workpiece is rectangular, with dimensions of 10 × 10 × 1.3 mm (length × width × thickness). The angle between the transverse processing direction and the longitudinal processing direction is set at 90°, and a micro-column structure is machined on the surface of the workpiece. We defined the first cutting process as v 1 and the second as v 2 (Fig. 8 (b)). 4.2 The Influence of Temperature on Machining Accuracy In order to determine the optimal temperature for cryogenic dicing experiments, various dicing tests are conducted at different temperatures while maintaining constant machining parameters. The results are shown in Fig. 9 . The experimental results show that as temperature decreases, the workpiece's machining accuracy improves, and its shape changes from parallelogram to rectangle, gradually approaching the set machining shape. Notably, the shape transition temperature for the workpiece is -40°C. According to the mechanical testing of the material, when the temperature of the silicone material OE6650 is lowered from 25°C to -40°C, Young's modulus of the material increases from 3.08 MPa to 73.58 MPa, an increase of 22.89 times. Further reduction in temperature to -80°C results in Young's modulus of 177.3 MPa, which is 1.41 times higher than the value at -40°C. This indicates that the material transitions between a high-elastic state and a glassy state, with Young's modulus exhibiting the most significant change. Therefore, the glassy state transition temperature for the silicone material OE6650 can be estimated to be near − 40°C.To ensure that the temperature of the workpiece is reduced below its glass transition temperature, subsequent experiments utilize − 60°C for verification purposes. 4.3 The Influence of Spindle Speed on Machining Accuracy Spindle speed is a crucial factor influencing the machining accuracy of soft elastic materials. Experiments are conducted with varying spindle speeds to investigate the effects of spindle speed on dicing performance. The shape of the workpiece is depicted in Fig. 10 . It can be observed that with an increase in spindle speed, the dimensional accuracy of the processed microcolumn gradually deteriorates, and this effect is particularly prominent at 25°C. Low-speed conditions yield higher tool stability and better machining accuracy when manufacturing microcolumns of larger dimensions. At a speed of 45,000 r/min, the machining size at 25°C deviates significantly from the intended value. This is attributed to the amplified tool vibration and increased higher-speed friction. At room temperature, the soft elastic material exhibits poor resistance to deformation. Consequently, the workpiece is twisted into the processing area under friction with the tool's side, resulting in overcut deformation. In contrast, the cryogenic dicing workpiece demonstrates no obvious torsion, effectively mitigating the overcut effect of tool vibration. Notably, under cryogenic conditions, the chip removal efficiency during the dicing process is significantly enhanced, reducing chips in the microgrooves and effectively eliminating the issue of wheel blockage, as shown in Fig. 11 . 4.4 The Influence of Size on Machining Accuracy The machining size significantly impacts the machining accuracy of the workpiece, particularly in small-scale machining. Figure 12 displays the experimental results obtained under varying temperature conditions. As the processing size decreases, the machining accuracy of the workpiece deteriorates, with this effect being more pronounced at 25°C. Under smaller processing sizes, pronounced torsional overcutting and even damage may occur. At -60°C, although the shape of the workpiece remains regular, overcutting still occurs. This is attributed to the tool vibration induced by the high-speed rotating spindle. However, the low temperature significantly mitigates the adverse effects of this tool vibration. It is important to note that the causes of overcut deformation under 25°C and − 60°C working conditions differ. While the former is primarily attributed to the tilt and torsion of the workpiece and the overcut resulting from tool vibration, the latter is mainly due to tool vibration alone. 5. Conclusions This work investigates the deformation mechanism of soft elastic materials during processing, using silicone material OE6650 as the primary research sample. A 3D continuous dicing FEM of silicone is developed to explore the underlying deformation mechanisms.The work reveals that the main reason for the decrease of small-scale machining accuracy is the tilt overcut in the early stage of the workpiece, followed by the torsion overcut in the later stage. By reducing the temperature of the workpiece to its corresponding glass transition temperature, the occurrence of tilt and torsion during the machining process can be effectively minimized. A comparison of the 3D FEM results with experimental data reveals that the workpiece deforms significantly at room temperature during dicing. In addition to the tilting and torsional overcut, the overcut effect induced by tool vibration at higher spindle speeds is also observed. The workpiece's tilt and torsion during the machining process are effectively suppressed by subjecting the soft elastic material to cryogenic dicing, which involves cooling below its glass transition temperature. This approach can also effectively reduce the overcut effect caused by tool vibration at high speeds. As a result, the workpiece maintains a rectangular shape even during 32 \(\mu m\) size machining. In contrast, significant deformation occurs at room temperature, leading to processing damage. Declarations Conflicts of interest All authors disclosed no relevant relationships. Funding This work was supported by the National Natural Science Foundation of China (52375426), by the National Natural Science Foundation of China (52105443), by the Natural Science Foundation of Guangdong Province(2022A1515011059), and the Guangdong Provincial Key R&D Programme (2022B0101090002). Authors' contributions Jia-Sheng Li : Conceptualization (equal); formal analysis (equal); original draft preparation (equal); review and editing (equal); funding acquisition (equal); project administration (lead). Zong-Tao Li : Conceptualization (equal); formal analysis (equal); investigation (equal); review and editing (equal); funding acquisition (equal); supervision (lead). Bin-Hai Yu : Conceptualization (equal); formal analysis (equal); investigation (equal); methodology (equal); original draft preparation (equal); review and editing (equal). Chong-Hui He : Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); visualization (equal); original draft preparation (equal); review and editing (equal). Yun-Long Zhang : Methodology (equal); software (equal); validation (equal); review and editing (equal). Xin-Rui Ding : Methodology (equal); validation (equal); review and editing (equal). 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Int J Crashworthiness 9(5):449–464 Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 03 Mar, 2025 Reviewers agreed at journal 05 Jul, 2024 Reviewers invited by journal 04 Jul, 2024 Editor assigned by journal 03 Jul, 2024 First submitted to journal 01 Jul, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4665731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322513082,"identity":"41e8f8c3-ceff-419e-8028-7c11ff1b9197","order_by":0,"name":"Binhai Yu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Binhai","middleName":"","lastName":"Yu","suffix":""},{"id":322513083,"identity":"0175f3e3-758a-487f-8d8e-144bd27b9d13","order_by":1,"name":"Chonghui He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chonghui","middleName":"","lastName":"He","suffix":""},{"id":322513084,"identity":"97552a13-91bd-42a6-9874-a5e2d15e739d","order_by":2,"name":"Jiasheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxmYg8cDABsplI1ZLgkEakGQmUgsYJDAcJkELczvz4w8JBeft+WfkH2D4UHaYgX92AyGHsZlJJBjcTpxxI5mBcca5wwwSdw4Q0sJgBvTL7QQDiWQGZt62wwwGEgmEtLB//pBgcM4erOUvcVp4gGoMDjBuAGlhJFJLGVBLcuKMM48NDvacS+eRuEFAi2H/8c0fPvyxs+dvT3z44EeZtRz/DEJaGpA4B4CYB796IJAnqGIUjIJRMApGAQB8dT3fPLkH1QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4486-0005","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jiasheng","middleName":"","lastName":"Li","suffix":""},{"id":322513085,"identity":"6ae8731a-be8a-4aaa-8759-397afcbc0339","order_by":3,"name":"Yunlong Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Zhang","suffix":""},{"id":322513086,"identity":"06b31a3a-6a74-41f8-9338-b6c1bef8fb11","order_by":4,"name":"Xinrui Ding","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinrui","middleName":"","lastName":"Ding","suffix":""},{"id":322513087,"identity":"547043b8-4840-403f-a054-0ca777e4e40d","order_by":5,"name":"Zongtao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zongtao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-01 06:28:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4665731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4665731/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-15431-z","type":"published","date":"2025-03-27T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61292176,"identity":"09546693-3a6b-4951-901b-db5fe2825d4e","added_by":"auto","created_at":"2024-07-29 07:33:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2241192,"visible":true,"origin":"","legend":"\u003cp\u003eDicing model; (a) Schematic of dicing force, (b) 3D FEM for dicing of OE6650\u003c/p\u003e\n\u003cp\u003eTable 1 Parameters of OE6650 in FEM\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/bf08bb1ba47dc22d9812331e.png"},{"id":61292173,"identity":"302bd962-f408-4bf3-976f-76fb9edf10b2","added_by":"auto","created_at":"2024-07-29 07:33:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1140807,"visible":true,"origin":"","legend":"\u003cp\u003eTilt deformation process in dicing; (a) Time = 1.35 ms, (b) Time = 2.15 ms, (c) Time = 2.75 ms\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/c28d7bec14544da0859cf6dd.png"},{"id":61292826,"identity":"3600b2e2-4d03-4949-be8c-9af44d7ce477","added_by":"auto","created_at":"2024-07-29 07:41:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1772561,"visible":true,"origin":"","legend":"\u003cp\u003eTorsional deformation process in dicing; (a) Time = 1.45 ms, (b) Time = 1.65 ms, (c) Time = 2.45 ms, (d) Time = 2.65 ms, (e) Time = 2.75 ms\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/57eb89b77bc5c1842bbbcaf1.png"},{"id":61293543,"identity":"0d8e4e11-fe05-46c1-a1c4-60fb8e374bd8","added_by":"auto","created_at":"2024-07-29 07:49:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":547020,"visible":true,"origin":"","legend":"\u003cp\u003eCauses of overcut in dicing process; (a) Tilt deformation overcut, (b) Torsion deformation overcut\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/3dac1069d8ee543ed35a2e9e.png"},{"id":61292183,"identity":"d6a40427-ea0e-49f9-b462-b9ded16b9cfc","added_by":"auto","created_at":"2024-07-29 07:33:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1947755,"visible":true,"origin":"","legend":"\u003cp\u003eThe shape change of the workpiece in dicing at-40 °C; (a) Time = 1.40 ms, (b) Time = 1.65 ms, (c) Time = 2.10 ms, (d) Time = 2.30 ms, (e) Time = 2.50 ms, (f) Time = 2.75 ms\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/b1013e89212bf0f17786af6a.png"},{"id":61292177,"identity":"236bc996-6dbd-4e90-b5bb-261b44f58106","added_by":"auto","created_at":"2024-07-29 07:33:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":283377,"visible":true,"origin":"","legend":"\u003cp\u003eValues of dicing force under different temperatures; (a) Mean dicing force at 25℃, (b) Mean dicing force at -40℃\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/2a0863ba0a1d55a855fc33ad.png"},{"id":61292828,"identity":"59bff454-fe39-4adf-b996-4e164de7bf2c","added_by":"auto","created_at":"2024-07-29 07:41:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":173585,"visible":true,"origin":"","legend":"\u003cp\u003ePeak dicing force under different Spindle Speed V\u003csub\u003ec\u003c/sub\u003e; (a) F\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e, (b) F\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/b84169b7e3d9d730605467f8.png"},{"id":61292181,"identity":"8f83252c-e91c-4f7d-a341-7b4cad4efdea","added_by":"auto","created_at":"2024-07-29 07:33:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8283161,"visible":true,"origin":"","legend":"\u003cp\u003eProcessing equipment; (a) Cryogenic dicing system, (b) Schematic diagram of microcolumn structure processing\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/eda0086af1d9fa424e81b943.png"},{"id":61292829,"identity":"619d084a-d8b5-4f4a-acce-794a105a8642","added_by":"auto","created_at":"2024-07-29 07:41:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2766425,"visible":true,"origin":"","legend":"\u003cp\u003eTop view of microcolumn processed at different temperatures, scale 50 μm; (a) T=25℃, (b) T=0℃, (c) T=-20℃, (d) T=-40℃, (e) T=-60℃, (f) T=-80℃\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/e76c7dc78d42394760ca66df.png"},{"id":61292831,"identity":"1919e59b-7e27-42ba-b29b-c3bf73f8e85b","added_by":"auto","created_at":"2024-07-29 07:41:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2341238,"visible":true,"origin":"","legend":"\u003cp\u003eTop view of microcolumn processed at at different temperatures and spindle speeds, scale 200 μm\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/bf24d2323203398b2c10bcde.png"},{"id":61292178,"identity":"6e21bb8d-a462-4753-80df-b73666687e23","added_by":"auto","created_at":"2024-07-29 07:33:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3611329,"visible":true,"origin":"","legend":"\u003cp\u003eDicing wheel side and dicing microgrooves at different temperatures; (a) T=25℃, (b) T=-60℃\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/251d26b16e71c9c45468295d.png"},{"id":61293544,"identity":"c307edee-780c-4175-b1a3-0d0c3cd2a847","added_by":"auto","created_at":"2024-07-29 07:49:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":6842012,"visible":true,"origin":"","legend":"\u003cp\u003eMicrocolumn top view of different processing sizes, scale 50 μm\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/ab3519952d0bcec8607e9bd9.png"},{"id":79605208,"identity":"e943c116-d0b0-449e-a74d-d7950c9291a9","added_by":"auto","created_at":"2025-03-31 16:10:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":54422136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4665731/v1/2ff7e32b-5a59-4c30-b42a-e982aee1017b.pdf"}],"financialInterests":"","formattedTitle":"Study on the Machining Accuracy of Soft Elastic Material Using Cryogenic Dicing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLight-emitting diodes (LEDs) are widely used in display and lighting applications due to their low cost, long lifespan, and low power consumption [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among them, Mini/Micro-LED devices have become core components in emerging applications such as 4K/8K ultra-clear displays [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], vehicle displays [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and wearable displays [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] due to their advantages such as wide color gamut, high brightness, good reliability, miniaturization, and high power efficiency. In the manufacturing process of micro-LED display devices, packaging manufacturing is an important step to isolate the LED chip from the external environment, protect the chip from water, oxygen erosion, and mechanical damage, and improve the stability and lifespan of the LED device [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In recent years, improving the light output efficiency and high dynamic display performance of devices by introducing functional structures during the LED packaging manufacturing process has become a research hotspot [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, common LED packaging materials such as silicone resin have the drawbacks of high elasticity, low hardness, and poor workability at room temperature, which are important reasons for the difficulty in manufacturing microstructures on the LED packaging surface [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In traditional processing, soft elastic materials have increasingly serious problems, such as low processing accuracy and poor surface processing quality as the processing size decreases, a significant factor hindering the development of micro-LED devices. Using cryogenic-assisted processing methods is expected to solve the problem of insufficient machining accuracy of soft elastic packaging materials, achieving high-precision microstructures in the process of LED packaging preparation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCryogenic processing technology introduces cryogenic media into material processing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It can effectively reduce the processing temperature, improve the surface quality of the processed material [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and improve processing defects [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], achieving high-quality processing of difficult-to-machine materials. Cryogenic processing technology was initially applied to the processing of difficult-to-machine metals, and it has gradually been applied to other processing fields [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Wu et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] applied cryogenic processing technology to process Ti-based alloy thin-walled parts and conducted comparative experimental analysis on different cutting conditions such as water cooling, micro-lubrication, and cryogenic micro-lubrication. The results showed that compared to water cooling and micro-lubrication, the maximum deformation was reduced by 41.4% and 44.7%, respectively, when the spindle speed was 4500 r/min, and the surface roughness of the workpiece was reduced by 34.5% and 45.8%, respectively. Altas et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] studied the effect of cryogenic tool pre-treatment on the milling performance of NiTi shape memory alloy. They found that cryogenic treatment significantly improved the surface roughness of the processed material, averaging a reduction of about 7%. Jia et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] first proposed the concept of an appropriate processing temperature range for CFRP. The results showed that the hardened matrix could better support the fibers and achieve higher surface quality at lower temperatures. When the cutting area temperature dropped below \u0026minus;\u0026thinsp;25\u0026deg;C, the surface roughness Ra reached 1.6 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu m\\)\u003c/span\u003e\u003c/span\u003e, showing good surface integrity. Friedrich [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] concluded that silicone and acrylic polymers are soft and difficult to process at room temperature. Their softness and adhesive properties are the main constraints for tool processing. Cryogenic processing technology can be applied to polymer processing, freezing the workpiece material and effectively enhancing its workability. This process helps to transform soft and elastic polymers into hard and brittle materials, thus significantly improving their mechanical properties and workability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Song et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] proposed a tool compensation method that considers the low-temperature shrinkage of PDMS to improve the geometric accuracy of processing. They also used the relationship between cutting temperature, other processing parameters, and surface roughness to generate surface microchannels with different roughnesses. Mechanical micro-machining technology has the advantages of geometric solid control and high surface smoothness, providing an effective manufacturing method for micro-machining soft polymer materials [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, in the micro-machining of soft elastic materials, most studies have not discussed the deformation phenomenon during processing.\u003c/p\u003e \u003cp\u003eA significant research challenge involves addressing the low processing accuracy frequently encountered during machining soft elastic materials. This work utilizes a three-dimensional (3D) finite element analysis to investigate the occurrence of deformation overcutting during the dicing process of soft elastic materials at varying temperatures and the influence of spindle speed on the dicing accuracy of these materials. The experimental results show that the tilt and torsion deformation of the workpiece are the reasons for the low machining accuracy in the traditional machining process, and cryogenic dicing significantly improves the machining accuracy of the soft elastic material.\u003c/p\u003e"},{"header":"2. Dicing Model and Dicing Conditions","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Finite Element Model of Milling\u003c/h2\u003e \u003cp\u003eObserving and analyzing the entire machining process of soft elastic materials is a significant challenge due to the difficulty in observing the structural changes of the workpiece during micro-machining. To address this issue, a finite element model is created using the ABAQUS finite element simulation software to simulate the dicing process. This work investigates the elastic deformation rules of soft elastic materials during dicing at varying temperatures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA number of finite element models for continuous dicing are established in this work to examine the relationship between the dicing forces F\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e, F\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, and workpiece deformation during the dicing process, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a). The geometric model of the tool and the workpiece is created in a 1:1 ratio, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b). The dicing depth is 0.25 mm, the workpiece is a single fin of 3\u0026times;0.1\u0026times;0.5 mm, the minimum mesh size is 0.005 mm, and the workpiece is divided into 435,400 meshes. The mesh type selected is C3D8R, and the workpiece material is an OE6650 soft elastic material. The workpiece is assigned predetermined 25\u0026deg;C and \u0026minus;\u0026thinsp;40\u0026deg;C, respectively. According to the mechanical properties test, the specific physical parameters of soft elastic materials at different temperatures are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The tool employed is a complete dicing wheel with an outer diameter of 28 mm, a tool thickness of 0.10 mm, a polyhedral abrasive grain, a grid seed of 0.1 mm, a grid number of 16,783, and a grid type of C3D4. During the dicing process of soft elastic materials, the tool is treated as a rigid body, and the tool material is diamond. The relevant physical parameters are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eParameters of OE6650 in FEM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1160 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung's Modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 MPa (25℃)、73.54 MPa (-40℃)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoisson ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of tool in FEM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3500kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung's Modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e960000MPa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoisson ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\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 \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Material Properties and Constitutive Model\u003c/h2\u003e \u003cp\u003eIn this work, the J-C constitutive model is used to describe the dicing deformation behavior of soft elastic material OE6650. The model fully considers the relationship between stress and strain, strain rate, and temperature and is widely used in cutting simulation. J-C model equation is as follows [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] :\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\sigma =\\left(A+B{\\epsilon }^{n}\\right)\\left(1+Cln\\frac{\\dot{\\epsilon }}{\\dot{{\\epsilon }_{0}}}\\right)\\left[1-{\\left(\\frac{T-{T}_{r}}{{T}_{m}-{T}_{r}}\\right)}^{m}\\right]\\#\\left(Eq1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma\\)\u003c/span\u003e\u003c/span\u003e is the equivalent stress, \u003cem\u003eA\u003c/em\u003e is the initial yield stress, \u003cem\u003eB\u003c/em\u003e the is hardening modulus, \u003cb\u003eε\u003c/b\u003e is the equivalent strain, n is the hardening index, \u003cem\u003eC\u003c/em\u003e is the strain rate constant, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\epsilon }\\)\u003c/span\u003e\u003c/span\u003e is the plastic strain rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\dot{\\epsilon }}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the reference plastic strain, \u003cem\u003eT\u003c/em\u003e is the material temperature, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{r}\\)\u003c/span\u003e\u003c/span\u003e is the ambient temperature, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{m}\\)\u003c/span\u003e\u003c/span\u003e is the material melting point and \u003cem\u003em\u003c/em\u003e is the thermal softening index.\u003c/p\u003e \u003cp\u003eAccording to the test results of mechanical properties, OE6650 has low dependence on strain rate, short processing time and sufficient cooling. The strain rate and temperature terms of the constitutive model will degenerate to constant 1, and the model will degenerate to :\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\sigma =\\left(A+B{\\epsilon }^{n}\\right)\\#\\left(Eq2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccording to the stress-strain mechanical test of OE6650, the A, B, and n parameters of the Johnson-Cook model of OE6650 material at different temperatures are obtained by using the origin nonlinear curve fitting function, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterial constants of the Johnson-Cook model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72559MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-40℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57639MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.3844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDicing simulation requires the material's constitutive model and damage model. In the finite element analysis, the damage model is used as the failure criterion of the mesh in FEM, and the flexible damage is used as the damage model of the OE6650 dicing simulation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The model parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOE6650 material damage model parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture strain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriaxial stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrain rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFailure displacement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001mm\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":"3. Simulation Results and Analysis","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effect of Different Temperature on Dicing\u003c/h2\u003e \u003cp\u003eBased on the established FEM, the dicing process at 25\u0026deg;C was analyzed. The morphological changes in the workpiece throughout the dicing process are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the initial stages of dicing, the degree of tilt deformation in the workpiece is significant. As the dicing depth increases, the dicing force approaches the bottom of the workpiece, gradually decreasing the tilt deformation. This occurrence is attributed to the low stiffness of the soft elastic material at room temperature, which possesses high ductility, deformation capacity, and deformation limits. Consequently, the workpiece experiences significant tilt deformation when subjected to dicing forces in the early stages of dicing. As the dicing process continues, the action point of dicing gradually moves towards the bottom of the workpiece, which has a higher stiffness, thereby reducing the tilt deformation of the workpiece.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the tilt deformation, the workpiece also experiences torsional deformation during the dicing process, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the initial stages of dicing, the workpiece exhibits only a slight tilt. As the dicing depth increases, the torsional deformation progressively moves towards the cutting area, leading to an overcut of the workpiece. This occurrence can be attributed to the soft elasticity and low stiffness of the silicone material at room temperature. As the machining depth increases, the friction between the tool and the workpiece amplifies, resulting in a more significant torsion of the workpiece in the later stages of dicing. Consequently, the low machining accuracy of the workpiece in the small-size dicing process is due to the workpiece tilting into the cutting area in the early stage of dicing and twisting towards the tool in the later stage of dicing, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the mechanical properties test of soft elastic material OE6650, Young's modulus is the most significant change in temperature reduction for a soft elastic material. When the temperature is lowered from 25\u0026deg;C to -40\u0026deg;C, the Young's modulus of the soft elastic material increases approximately 23-fold. Therefore, the dicing process is investigated under the condition of -40\u0026deg;C. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the morphological changes in the workpiece throughout the dicing process. The workpiece demonstrates no tilt or torsional deformation throughout the dicing process and maintains a complete microcolumn shape. This is attributed to the fact that at -40\u0026deg;C, the temperature is below the material's glass transition temperature. As a result, the silicone material transitions from soft elasticity to hard brittleness, significantly enhancing its stiffness. Under the same processing conditions, the material can withstand a greater cutting force. Consequently, the workpiece deformation remains minimal and stable throughout the dicing process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effect of Different Cutting Depth on Dicing Force\u003c/h2\u003e \u003cp\u003eThe FEM is employed to simulate the dicing process under varying depths of dicing at two distinct temperatures, 25\u0026deg;C and \u0026minus;\u0026thinsp;40\u0026deg;C. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the peak values of the dicing forces, F\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and F\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, incrementally elevate with the increase in dicing depth. Notably, the maximum dicing peak force at -40\u0026deg;C is approximately 10-fold greater than that at 25\u0026deg;C. The disparity can be attributed to the material's mechanical properties at the two temperatures. At 25\u0026deg;C, the silicone material exhibits soft elasticity, resulting in a lack of precision in applying the dicing force, leading to significant deformation. In contrast, at -40\u0026deg;C, the low temperature renders the material hard and brittle, conferring a higher deformation resistance and the capacity to withstand a greater dicing force. Furthermore, the increase in dicing depth leads to an enlarged cutting area per unit of time, causing the dicing force to escalate concurrently with the dicing depth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Influence of Different Spindle Speed on Dicing Force\u003c/h2\u003e \u003cp\u003eIn order to further explore the processing peculiarities of materials under cryogenic dicing conditions, the dicing process at varying spindle speeds (-40\u0026deg;C) is simulated. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the peak dicing forces, F\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and F\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, are significantly influenced by the spindle speed V\u003csub\u003ec\u003c/sub\u003e. When increasing the spindle speed from 3000 r/min to 12000 r/min, both dicing peak forces F\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and F\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e exhibit a consistent decrease. This phenomenon can be attributed to the heightened centrifugal force experienced by the sand particles on the dicing wheel as the spindle speed rises. Consequently, the centripetal force acting on these particles intensifies, thereby augmenting their cutting efficacy during the dicing process. Simultaneously, the contact area between the sand particles and the workpiece diminishes, resulting in a reduced dicing force per unit area. Moreover, with prolonged dicing duration, the dicing point gradually shifts towards the stiffer bottom of the workpiece. Despite this displacement, the workpiece undergoes negligible deformation across various dicing speeds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Experimental Results and Verification","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Experimental Conditions and Method\u003c/h2\u003e \u003cp\u003eIn this work, we establish a cryogenic processing platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a)). The cryogenic system consists of components such as regulating valves, flowmeters, and LN\u003csub\u003e2\u003c/sub\u003e self-priming pumps. LN\u003csub\u003e2\u003c/sub\u003e can be mixed with air in different proportions to achieve the control requirements of different jet temperatures. The mechanical processing equipment used is an automatic dicing machine (HP-6101 CETC Beijing Electronic Equipment Company Ltd.). By jetting the cryogenic fluid into the processing area, we can cryogenically treat the workpiece.\u003c/p\u003e \u003cp\u003eDuring the experiment, we employed silicone material (Dow Corning OE6650 resin, A and B parts with a mass ratio 1:3) as the workpiece. The workpiece is rectangular, with dimensions of 10 \u0026times; 10 \u0026times; 1.3 mm (length \u0026times; width \u0026times; thickness). The angle between the transverse processing direction and the longitudinal processing direction is set at 90\u0026deg;, and a micro-column structure is machined on the surface of the workpiece. We defined the first cutting process as v\u003csub\u003e1\u003c/sub\u003e and the second as v\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(b)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 The Influence of Temperature on Machining Accuracy\u003c/h2\u003e \u003cp\u003eIn order to determine the optimal temperature for cryogenic dicing experiments, various dicing tests are conducted at different temperatures while maintaining constant machining parameters. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The experimental results show that as temperature decreases, the workpiece's machining accuracy improves, and its shape changes from parallelogram to rectangle, gradually approaching the set machining shape. Notably, the shape transition temperature for the workpiece is -40\u0026deg;C. According to the mechanical testing of the material, when the temperature of the silicone material OE6650 is lowered from 25\u0026deg;C to -40\u0026deg;C, Young's modulus of the material increases from 3.08 MPa to 73.58 MPa, an increase of 22.89 times. Further reduction in temperature to -80\u0026deg;C results in Young's modulus of 177.3 MPa, which is 1.41 times higher than the value at -40\u0026deg;C. This indicates that the material transitions between a high-elastic state and a glassy state, with Young's modulus exhibiting the most significant change. Therefore, the glassy state transition temperature for the silicone material OE6650 can be estimated to be near \u0026minus;\u0026thinsp;40\u0026deg;C.To ensure that the temperature of the workpiece is reduced below its glass transition temperature, subsequent experiments utilize \u0026minus;\u0026thinsp;60\u0026deg;C for verification purposes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The Influence of Spindle Speed on Machining Accuracy\u003c/h2\u003e \u003cp\u003eSpindle speed is a crucial factor influencing the machining accuracy of soft elastic materials. Experiments are conducted with varying spindle speeds to investigate the effects of spindle speed on dicing performance. The shape of the workpiece is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. It can be observed that with an increase in spindle speed, the dimensional accuracy of the processed microcolumn gradually deteriorates, and this effect is particularly prominent at 25\u0026deg;C. Low-speed conditions yield higher tool stability and better machining accuracy when manufacturing microcolumns of larger dimensions. At a speed of 45,000 r/min, the machining size at 25\u0026deg;C deviates significantly from the intended value. This is attributed to the amplified tool vibration and increased higher-speed friction. At room temperature, the soft elastic material exhibits poor resistance to deformation. Consequently, the workpiece is twisted into the processing area under friction with the tool's side, resulting in overcut deformation. In contrast, the cryogenic dicing workpiece demonstrates no obvious torsion, effectively mitigating the overcut effect of tool vibration. Notably, under cryogenic conditions, the chip removal efficiency during the dicing process is significantly enhanced, reducing chips in the microgrooves and effectively eliminating the issue of wheel blockage, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The Influence of Size on Machining Accuracy\u003c/h2\u003e \u003cp\u003eThe machining size significantly impacts the machining accuracy of the workpiece, particularly in small-scale machining. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e displays the experimental results obtained under varying temperature conditions. As the processing size decreases, the machining accuracy of the workpiece deteriorates, with this effect being more pronounced at 25\u0026deg;C. Under smaller processing sizes, pronounced torsional overcutting and even damage may occur. At -60\u0026deg;C, although the shape of the workpiece remains regular, overcutting still occurs. This is attributed to the tool vibration induced by the high-speed rotating spindle. However, the low temperature significantly mitigates the adverse effects of this tool vibration. It is important to note that the causes of overcut deformation under 25\u0026deg;C and \u0026minus;\u0026thinsp;60\u0026deg;C working conditions differ. While the former is primarily attributed to the tilt and torsion of the workpiece and the overcut resulting from tool vibration, the latter is mainly due to tool vibration alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis work investigates the deformation mechanism of soft elastic materials during processing, using silicone material OE6650 as the primary research sample. A 3D continuous dicing FEM of silicone is developed to explore the underlying deformation mechanisms.The work reveals that the main reason for the decrease of small-scale machining accuracy is the tilt overcut in the early stage of the workpiece, followed by the torsion overcut in the later stage. By reducing the temperature of the workpiece to its corresponding glass transition temperature, the occurrence of tilt and torsion during the machining process can be effectively minimized. A comparison of the 3D FEM results with experimental data reveals that the workpiece deforms significantly at room temperature during dicing. In addition to the tilting and torsional overcut, the overcut effect induced by tool vibration at higher spindle speeds is also observed. The workpiece's tilt and torsion during the machining process are effectively suppressed by subjecting the soft elastic material to cryogenic dicing, which involves cooling below its glass transition temperature. This approach can also effectively reduce the overcut effect caused by tool vibration at high speeds. As a result, the workpiece maintains a rectangular shape even during 32 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu m\\)\u003c/span\u003e\u003c/span\u003e size machining. In contrast, significant deformation occurs at room temperature, leading to processing damage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eAll authors disclosed no relevant relationships.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (52375426), by the National Natural Science Foundation of China (52105443), by the Natural Science Foundation of Guangdong Province(2022A1515011059), and the Guangdong Provincial Key R\u0026amp;D Programme (2022B0101090002).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eJia-Sheng Li\u003c/b\u003e: Conceptualization (equal); formal analysis (equal); original draft preparation (equal); review and editing (equal); funding acquisition (equal); project administration (lead). \u003cb\u003eZong-Tao Li\u003c/b\u003e: Conceptualization (equal); formal analysis (equal); investigation (equal); review and editing (equal); funding acquisition (equal); supervision (lead). \u003cb\u003eBin-Hai Yu\u003c/b\u003e: Conceptualization (equal); formal analysis (equal); investigation (equal); methodology (equal); original draft preparation (equal); review and editing (equal). \u003cb\u003eChong-Hui He\u003c/b\u003e: Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); visualization (equal); original draft preparation (equal); review and editing (equal). \u003cb\u003eYun-Long Zhang\u003c/b\u003e: Methodology (equal); software (equal); validation (equal); review and editing (equal). \u003cb\u003eXin-Rui Ding\u003c/b\u003e: Methodology (equal); validation (equal); review and editing (equal). All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJang HJ, Lee JY, Baek GW, Kwak J, Park J-H (2022) Progress in the development of the display performance of AR, VR, QLED and OLED devices in recent years. 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J Manuf Process 85:323\u0026ndash;344\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta MK, Korkmaz ME, Sarıkaya M, Krolczyk GM, G\u0026uuml;nay M (2022) In-process detection of cutting forces and cutting temperature signals in cryogenic assisted turning of titanium alloys: An analytical approach and experimental study. Mech Syst Signal Process 169:108772\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorkmaz ME, G\u0026uuml;nay M, Verleysen P (2019) Investigation of tensile Johnson-Cook model parameters for Nimonic 80A superalloy. J Alloys Compd 801:542\u0026ndash;549\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooputra H, Gese H, Dell H, Werner H (2004) A comprehensive failure model for crashworthiness simulation of aluminium extrusions. Int J Crashworthiness 9(5):449\u0026ndash;464\u003c/span\u003e\u003c/li\u003e\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":false,"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":"FEM, Soft elastic materials, Cryogenic, Dicing, Deformation","lastPublishedDoi":"10.21203/rs.3.rs-4665731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4665731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDicing is one of the common processing methods for soft elastic materials. However, as the processing dimensions continue to decrease, the machining accuracy of the workpiece becomes worse. In this work, a three-dimensional (3D) finite element model (FEM) of dicing processing is established by using finite element software ABAQUS and is compared with experimental results. Based on the established 3D dicing FEM, the influence of temperature, depth of cut, and spindle speed on the dicing process is studied. The results show that in the traditional soft elastic material dicing process, the increase in spindle speed and the decrease in processing dimensions led to the vibration of the tool, tilt and twist deformation of the workpiece, which become the main reasons for the poor machining accuracy. The application of cryogenic processing can effectively suppress the tilt and twist deformation of the workpiece. Under high spindle speed, cryogenic dicing can also somewhat mitigate the negative influence of tool vibration.\u003c/p\u003e","manuscriptTitle":"Study on the Machining Accuracy of Soft Elastic Material Using Cryogenic Dicing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 07:32:59","doi":"10.21203/rs.3.rs-4665731/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2025-03-03T21:02:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-05T10:38:41+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-04T05:27:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T00:41:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2024-07-02T03:10:49+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":"fcdc91b4-4085-487a-abb9-9258e826b59f","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:06:15+00:00","versionOfRecord":{"articleIdentity":"rs-4665731","link":"https://doi.org/10.1007/s00170-025-15431-z","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2025-03-27 15:57:52","publishedOnDateReadable":"March 27th, 2025"},"versionCreatedAt":"2024-07-29 07:32:59","video":"","vorDoi":"10.1007/s00170-025-15431-z","vorDoiUrl":"https://doi.org/10.1007/s00170-025-15431-z","workflowStages":[]},"version":"v1","identity":"rs-4665731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4665731","identity":"rs-4665731","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}