Sensitivity of Barkhausen Noise in Monitoring the Surface Integrity of SAE 4340 Hardened Steel After Turning Process

Sensitivity of Barkhausen Noise in Monitoring the Surface Integrity of SAE 4340 Hardened Steel After Turning Process | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sensitivity of Barkhausen Noise in Monitoring the Surface Integrity of SAE 4340 Hardened Steel After Turning Process Daniel Rodrigues Carlos, Freddy Armando Franco Grijalba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5694201/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jun, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 11 You are reading this latest preprint version Abstract This work investigates the feasibility of using the non-destructive testing technique based on the measurement of Magnetic Barkhausen Noise (MBN) to assess the surface integrity of SAE 4340 hardened steel after the turning process. The experiments were conducted using ceramic inserts and divided into two distinct experimental setups. In Experiment 1, the cutting parameters were kept within the levels recommended by the tool manufacturer. In Experiment 2, cutting parameters above these levels were applied. For the resulting samples, measurements of roughness, hardness, and residual stress were performed. MBN measurements were taken in the longitudinal direction to the cutting direction, and from the obtained signals, the MBN rms e MBN peak−position parameters were calculated and analyzed. The results showed that the MBN technique was unable to differentiate the hardness levels generated in the samples from both experiments. This behavior is attributed to the low variation in hardness observed between the samples ( < ± 7,4%). Regarding the roughness levels, it was found that, for the levels generated below Ra = 4, the MBN parameters were not influenced. However, when comparing the MBN results with the residual stress levels obtained by X-ray technique, the method demonstrated high efficiency for the analyzed conditions, showing a strong linear correlation (R² > 0,93). Magnetic Barkhausen Noise Turning Surface Characterization Surface integrity. 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 Figure 13 Figure 14 Figure 15 1 Introduction The surface integrity of mechanical components plays a crucial role in performance and durability in industrial applications, especially in cases where components are subjected to high loads or harsh environments. Rigorous control of this integrity, through optimized manufacturing processes, is essential to meet the required quality standards, ensuring the preservation of mechanical properties. This attention is even more relevant in designs that demand a high degree of surface integrity, as changes can occur both in the surface layers and the subsurface layers [ 1 ],[ 2 ]. Non-destructive testing (NDT) plays an essential role in the manufacturing of mechanical components, ensuring the quality and reliability of products without compromising their functionality. In the context of surface integrity of parts, the application of NDT is indispensable to guarantee superior performance of components. Although conventional techniques, such as X-ray diffraction for residual stress evaluation and hardness testing, are widely used for this purpose, in many cases, these methodologies are classified as destructive. New non-destructive testing (NDT) techniques have been widely studied over the years, resulting in procedures that offer higher quality and safety in the evaluation of mechanical components and industrial equipment. Among these techniques, Magnetic Barkhausen Noise (MBN), discovered in 1919 by Professor Heinrich Barkhausen [ 3 ], stands out. MBN gained practical relevance in the 1970s with the contributions of researcher Tiitto Seppo, who began exploring its applications in the field of NDT [ 4 ][ 5 ][ 6 ]. Since then, MBN has proven to be effective in various applications, such as hardness level evaluation [ 7 ][ 8 ][ 9 ][ 10 ], measurement of residual stresses on machined surfaces [ 11 ][ 12 ][ 13 ][ 14 ], plastic deformation analysis [ 15 ][ 16 ], study of anisotropy in rolled sheets [ 17 ][ 18 ][ 19 ], and detection of phase transformations [ 20 ][ 21 ][ 22 ]. MBN is generated by the movement of magnetic domain walls in ferromagnetic materials under the influence of an alternating magnetic field. This movement occurs irregularly, involving irreversible jumps and, to a lesser extent, reversible jumps, as well as rotations of the magnetic domains. As a result, MBN presents a stochastic and complex signal, covering a wide range of frequencies. The analysis of this signal requires the use of advanced signal processing techniques and statistical methods, as it reflects the interaction between microscopic events and the magnetic properties of the material [ 23 ]. The use of the MBN technique to evaluate the surface integrity of machined parts has proven to be very promising, especially for the analysis of residual stresses and the detection of grinding burns. In the context of surface integrity generated by turning processes, some studies have been published. Epp and Hirsch (2009) compared the results obtained using X-ray diffraction and MBN techniques, using the FWHM parameter (Full Width at Half Maximum of the MBN envelope) to measure the residual stress generated after turning processes on samples with different geometries. The authors found that MBN showed good correlation with the X-ray technique in AISI 52100 and AISI 5210 materials that were heat-treated [ 24 ]. Rosipal et al. (2010) used the MBN technique to evaluate 100Cr6 steel with an average hardness of 62 HRC after turning and grinding processes. The analysis of the MBN signal envelope allowed for the identification of increased residual stress levels [ 25 ]. Neslušan (2020) investigated the use of MBN in monitoring microstructural changes and residual stresses induced by severe plastic deformations in duplex stainless steel subjected to turning. The results revealed that wear significantly influenced both the microstructure and the residual stress state of the parts. The MBN peak−position parameter (position of the maximum amplitude of the MBN envelope) showed a progressive increase with wear and observed microstructural changes [ 26 ]. Cizek (2014) studied the behavior of 100Cr6 steel during turning, using carbide inserts with different flank wear levels (VB). The results showed that the MBN peak−position and MBN rms parameters were affected by the dislocation density generated by tool wear [ 27 ]. Kubjatko et al. (2022) employed the MBN technique to monitor the integrity of automotive components after turning, considering different tool wear (VB) conditions and subsequent plasma nitriding. The results indicated that the MBN peak−position parameter was effective in identifying parts with compromised quality due to low nitrides density, particularly in situations of high tool wear [ 28 ]. In order to contribute to the advancement of the content available in the scientific literature, this work presents results on the use of the Magnetic Barkhausen Noise technique as a tool for monitoring the surface integrity of SAE 4340 steel hardened after turning processes. For this, two distinct experimental plans were conducted. In the first experiment, the cutting parameters were defined within the ranges recommended by the tool manufacturer. In the second experiment, more aggressive machining conditions were applied, with cutting parameters above those specified by the manufacturer. The results obtained highlight which surface integrity characteristics can be efficiently monitored using the MBN technique. 2 Materials and Methods 2.1 Material In this study, SAE 4340 steel was used, provided in the form of shafts with 1 meter in length and a diameter of 50.8 mm (2”). The chemical composition of the material is detailed in Table 1 . The samples were submitted to quenching heat treatment at 850°C for 60 minutes, and cooled in oil at 25°C. Then, the samples were tempered at 250°C (for 120 minutes) and cooled in a calm air environment. The resulting hardness value was 54 HRC. Table 1 Chemical composition of SAE 4340 material. Weight %. C Mn P S Si Ni Cr Mo 0,40 0,67 0,018 0,02 0,25 1,69 0,89 0,25 2.2 Samples The 1-meter shafts were cut into smaller sections of 250 mm, resulting in 6 bars. After the cutting process, in order to correct imperfections from the rolling process, the material was machined, removing 1 mm from the diameter. The machining was performed on a conventional Nardini lathe, model MS205, using the following cutting parameters: cutting speed (Vc) of 90 m/min, feed rate (fn) of 0.1 mm/rev, and cutting depth (ap) of 0.25 mm. Subsequently, rounded channels with a width of 3 mm were created to form tracks of 15 mm width. In each bar, 11 tracks were allocated, 9 of which were used as samples in the experiments. The tracks at the ends (first and last) were not considered for testing. Figure 1 shows the arrangement of the samples in each bar. 2.3 Experimental design For the machining of the test specimens with the cutting parameters to be evaluated, a NARDINI CNC lathe, model LOGIC 195, equipped with Fanuc Oi Mate-TC controls was used. The tool employed was a ceramic insert, model CNGA 120408T01020, mounted on a DCLNR 2525 M12 tool holder. This setup allowed the following geometry to be achieved: main position χr = 95°, inclination angle λs = 5º and exit angle γn =-6º. The experimental design of the cutting parameter combinations was divided into two stages. In experimental stage 1, light machining parameters, within the tool manufacturer's recommendations, were applied. In experimental stage 2, more severe machining parameters were used, exceeding the levels recommended by the manufacturer. In experimental stage 1, the selection of cutting parameters was based on the range of values recommended by the tool manufacturer, distributed between the minimum, average, and maximum values for cutting speed, feed rate, and depth of cut. The experimental design for this stage is presented in Table 2 . For each machining condition, three replicas (samples) were produced. Thus, a total of 54 samples were generated (3 cutting speeds × 3 feed rates × 2 depths of cut × 3 replicas). After machining each bar (corresponding to 9 samples), the cutting edge of the tool was replaced with a new one. Table 2 Cutting parameters used in experimental stage 1. Vc (m/min) fn (mm/rpm) ap (mm) 90 0,1 0,25 108 0,17 0,50 126 0,24 In the machining of the samples from experimental step 2, more aggressive cutting parameters were used as shown in Table 3 . Four cutting speed levels and four feed rate levels were defined, keeping the depth of cut constant. Thus, a total of 48 samples were produced (4 cutting speeds × 4 feed rates × 3 replicas). Table 3 Cutting parameters used in experimental stage 2. Vc (m/min) fn (mm/rpm) ap (mm) 100 0,03 0,5 200 0,1 0,5 300 0,25 0,5 400 0,35 0,5 In the machining procedures of experimental step 2, no pre-machining was performed, as no significant geometric errors were identified in the previous step (experiment 1) that could compromise the homogeneous material removal. Additionally, considering the cutting depth (ap) of 0.5 mm used in experimental step 2, there was no overlap of material effects generated by the two experiments. 2.4 Hardness measurements. The surface hardness test was conducted using a Mitutoyo hardness tester, model Wizhard, on the HR 15N scale. To verify hardness homogeneity, four measurements were taken per sample, distributed along its perimeter. However, in the presentation of the results, the values were converted and expressed on the HRC scale. 2.5 Roughness measurement The roughness measurements were performed with a portable roughness tester from Mitutoyo, model SJ-201. For each sample, six equidistant measurements were taken along the perimeter, using a cut-off of 0,8 mm and oriented transversely to the cutting direction. 2.6 Residual stress measurement by X-ray diffraction Residual stresses were evaluated using the X-ray diffraction (XRD) method on the surface of the workpiece, in the longitudinal direction relative to the cutting direction of the tool. The residual stress profiles were obtained using a PROTO X-ray diffractometer, model LXRD, equipped with Cr K-Alpha radiation (wavelength of 2,291 Å), a 1 mm beam, and a diffraction angle of 156.41°, in the crystallographic plane {211}. 2.7 Barkhausen Magnetic Noise Measurement. Figure 2 shows the schematic diagram of the MBN measurement system used in the tests. The measurement system was developed in our laboratory. The MBN probe consists of a U-shaped iron-silicon core, which is why it is called a YOKE. Around the core, approximately 400 turns of AWG24 copper wire were wound. Due to the geometric condition of the samples, the poles of the YOKE were machined using wire electrical discharge machining to enhance its coupling with the cylindrical surface of the sample. The YOKE receives an alternating current with a sine wave pattern, with a current amplitude of 1.0 A and an excitation frequency of 20 Hz, magnetizing the sample to be analyzed. The magnetization causes the movement of the domain walls in the material, generating magnetic pulses that are converted into voltage pulses by the pick-up coil. The spool of the pick-up coil, with a diameter of 10 mm, was made from polyvinyl chloride (PVC) to accommodate about 4,000 turns of copper wire with a diameter of 0.05 mm. The generated MBN signal passes through a signal conditioner, which amplifies it by 30 dB and filters it in a frequency band of 1-150 kHz. This frequency range is chosen to ensure the capture of the most representative signal measured by the pick-up coil. After conditioning, the signal was converted from analog to digital using the NATIONAL INSTRUMENTS NI USB-6361 data acquisition board. To acquire the data, it is necessary to use a sampling frequency that is at least twice the analysis frequency (in this case, 150 kHz). Therefore, a data acquisition frequency of 600 kHz was used to improve the resolution of the recorded signal. In experiment 1, measurements were taken at 7 different positions along the perimeter of each sample's surface, spaced 52° apart. At each position, 10 MBN signals were acquired, each signal consisting of 4 MBN bursts. In experiment 2, measurements were taken at 4 positions along the perimeter of each sample's surface, spaced 45° apart. Similar to experiment 1, 10 MBN signals were acquired at each measurement position. All MBN measurements were performed by orienting the magnetization field in the cutting direction of the samples, i.e., in the tangential direction. 3 Results and discussions 3.1 Experimental stage 1 3.1.1 MBN Signal Behavior. Figure 3 shows the result of the MBN signal envelope obtained for two groups of samples (replicate sets). In both groups, the same cutting speed, vc = 126 mm/min, and cutting depth, ap = 0.5 mm, were used, while the feed rate, fn, was different. In the first group (Fig. 3 a), fn = 0.1 mm/rpm, and in the second group (Fig. 3 b), fn = 0.24 mm/rpm. The envelopes, indicated by the same color (7 curves), represent the average of the 10 MBN signals measured at each position (7 positions along the perimeter of each sample). In the case of Fig. 3 a, using a lower feed rate of 0.1 mm/rpm, minimal variations are observed in the MBN envelope, both between the different positions of a single sample and between the replicas. Hypothetically, this result suggests the formation of a surface with homogeneous characteristics along the entire perimeter of each sample, with minimal variations between the replicas. On the other hand, in Fig. 3 b, with a higher feed rate of fn = 0.24 mm/rpm, a difference is observed between the signals of the replicas, which may indicate the influence of possible changes in the tool during the machining process of the replicas. In the results of experiment 2, this effect was more pronounced and is discussed in more depth. 3.1.2 Roughness and MBN. Figure 4 shows the correlation between the cutting parameters and the roughness amplitudes obtained from the samples after the turning processes. The measured roughness values are in accordance with what is expected in the literature and in line with the cutting parameters used [ 29 ][ 30 ][ 31 ]. It can be observed that feed rate was the cutting parameter with the greatest influence on roughness. The feed rate is directly related to the formation of the geometric profile of the machined surface. As the feed increases, the spacing between the grooves generated by the cutting edge of the tool also increases, resulting in a more irregular surface and, consequently, higher Ra values. [ 32 ] [ 33 ]. Figure 5 shows the correlation between the measured roughness amplitudes Ra and the MBN rms parameter of the MBN signals. It can be observed that there was no significant correlation between roughness and the MBN rms parameter. The roughness amplitudes found in the samples, below 2 Ra, did not generate a noticeable effect on the MBN signal Srivastava and Nahak (2023) [ 39 ] found that the Barkhausen signal was sensitive to roughness, especially in the analysis of the MBN rms parameters and the peak amplitude of the MBN envelope. This result was justified by the decrease in magnetic coupling, which directly influenced the MBN signals due to the reduction in magnetic flux. In the tests conducted in this work, the samples showed roughness values ranging from Ra = 6 µm to Ra = 9 µm, whereas in the present study, these roughness variations were significantly lower, ranging from Ra = 0.9 µm Ra = 2 µm. Therefore, it is believed that the low roughness levels obtained in this study did not significantly affect the probe's coupling with the surface of the sample or the magnetic flux. 3.1.3 Surface hardness and MBN The variation in the hardness levels measured in the samples from experiment 1 was low, with a fluctuation of ± 7.4%. Figure 6 shows the correlation between the cutting parameters and the surface hardness measurements made on the samples. The relatively high deviations shown in the figures are a consequence of the low variation obtained in the hardness levels. It can be seen that the feed rate (fn) (Fig. 6 c) and cutting speed (Vc) (Fig. 6 b) did not exert a significant influence on the hardness, as indicated by the overlap of deviations in the graphs. On the other hand, the depth of cut (ap) (Fig. 6 a) showed a slight influence, although considered negligible. When the output variable, in this case the measured hardness levels, shows low variation, it is concluded that the input parameters do not significantly influence the response. Thus, the cutting parameters did not significantly influence the hardness of the samples. This result can be explained by the characteristics of the test: materials with relatively high hardness, such as those that are quenched, do not show significant changes in hardness levels due to the machining process, especially when it is performed within the parameters specified by the tool manufacturer [ 35 ][ 36 ]. It is worth noting that the cutting parameters used in experiment 1 were within the range recommended by the manufacturer, and these machining conditions are considered ideal, without significantly affecting the material's hardness. Figure 7 shows the correlations between MBN parameters (MBN rms and MBN peak−position ) and the hardness levels obtained in the samples from experiment 1. It can be observed that neither of the MBN parameters showed correlation with the surface hardness levels of the samples. In the vast majority of studies found in the scientific literature, where the Barkhausen method has been effective in monitoring hardness, the variation in hardness was significant. Trillon et al. (2012) measured hardness levels with a variation of ± 26% [ 10 ], Davut et al. (2007) with a variation of ± 28% [ 8 ], Kaplan (2007) with a variation of ± 23% [ 20 ], and Grijalba et al. with a variation of ± 19% [ 7 ]. In this context, the relatively high hardness variations have a more pronounced effect on the MBN signal compared to other potential changes generated in the material, such as residual stresses, plastic deformations, or phase transformations. Under these conditions, the analysis of a simple parameter like MBN rms or MBN peak−position shows good sensitivity. In the results of this work, in experiment 1, the hardness variations were relatively low, around ± 7.4%. Since the machining conditions were within the range recommended by the tool manufacturer, it is believed that other changes in the material, such as plastic deformations and residual stresses, were also relatively low and influenced the MBN signal in an equivalent manner. Thus, the MBN rms or MBN peak−position parameters did not show sensitivity in measuring low hardness levels. The application of more sophisticated methods for analyzing MBN signals could help improve the measurement of small hardness variations. As of the submission date of this article, no published works with these characteristics have been found. 3.2 Experimental stage 2 3.2.1 Behavior of MBN signals. Figure 8 shows the MBN signal envelopes for two sets (replicates) of samples where only the cutting speed was changed. The wear levels generated on the corresponding tools after machining the third replicate are also displayed. In Fig. 8 a, the results obtained with a cutting speed of vc = 300 m/min are shown, while in Fig. 8 b, the results for vc = 400 m/min are presented. It is important to highlight that the cutting parameters used in experiment 2 were adjusted above the manufacturer's recommended range, characterizing severe machining conditions. Additionally, as shown in Fig. 8 , significant tool wear was observed after machining the third replicate. In the MBN envelopes, the black lines represent the signal measured in the first replicate machined with the tool in a 'new' condition (without wear), while the red lines correspond to the signal of the third replicate machined. The tool wear levels, presented in Fig. 8 , were measured after machining the third replicate. This evolution in tool wear inevitably resulted in distinct changes in the material's microstructure between the replicates, changes that are evident in the MBN envelopes. It is observed that the envelopes of the replicates showed more noticeable differences compared to experiment 1, which was conducted under lighter machining conditions. When comparing the MBN signal envelopes in Fig. 8 a (vc = 300 m/min) with those in Fig. 8 b (vc = 400 m/min), it is observed that, with a higher cutting speed, the difference between the replicates becomes even more evident. This behavior highlights that cutting speed has a significant influence on the quality of the replicates. Increasing the cutting speed raises the temperature during the machining process, accelerating tool wear. According to Dubec [ 37 ], severe tool wear can induce structural transformations in the layers near the surface, compromising both the surface integrity and the magnetic behavior of the material. The MBN envelopes obtained at vc = 400 m/min, which showed more pronounced differences between replicas compared to those at vc = 300 m/min, indicate more intense microstructural changes due to the higher tool wear. The sensitivity of the MBN technique makes it a promising tool for quality control in continuous monitoring of tool wear. This application is particularly relevant in high-productivity scenarios, where mass production of low-complexity parts requires quick and effective control. The use of MBN can allow for early identification of problems in the machining process, reducing cutting downtime and ensuring the dimensional consistency of the manufactured parts. 3.2.2 Roughness and MBN. Figure 9 presents the correlation between the cutting parameters and the roughness amplitudes obtained in the samples after the turning processes carried out in experiment 2. In this experiment, the samples reached roughness levels of up to 4 Ra, which represents twice the highest level observed in experiment 1. It is important to highlight that, even when using cutting parameters above the values recommended by the tool manufacturer, the feed rate continues to be the parameter with the greatest influence on roughness. It can be observed, in some conditions, that the deviations between the replicas show relatively high values, especially from a feed rate of 0.25 mm/rev, at cutting speeds of 300 m/min and 400 m/min. It is believed that this behavior is a consequence of the higher levels of tool wear generated under these severe machining conditions. Figure 10 shows the correlation between the Ra roughness amplitudes measured and the MBN rms parameter of the MBN signals. It can be observed that, similar to experiment 1, no correlation was identified between the roughness and the MBN rms parameter. The roughness amplitudes obtained in the samples, which reached values up to 4 Ra, did not have a significant impact on the MBN signal. 3.2.3 Surface hardness and MBN The variation in hardness levels observed in the samples from experiment 2, ± 7.3%, was similar to that in experiment 1. Despite using more severe cutting parameters, exceeding the limits recommended by the tool manufacturer, the changes in hardness were not significant. This suggests that, even under more severe cutting conditions, the material's hardness remained relatively unchanged. This result could be attributed to the inherent high hardness of the material used or the fact that, although the cutting parameters were harsh, they did not induce enough thermal or mechanical stress to cause noticeable changes in hardness. Figure 13 presents the correlation between the cutting parameters and the surface hardness measurements taken from the samples in experiment 2. The results were consistent with those found in experiment 1. Although more severe cutting parameters were applied, they did not exert a significant influence on the hardness of the samples. Figure 14 shows the correlations between the MBN parameters (MBN rms and MBN peak−position ) and the hardness levels obtained from the samples in experiment 2. Once again, it is observed that neither of the two MBN parameters demonstrated any correlation with the surface hardness levels of the samples. It is confirmed that when the variations in hardness levels are relatively small, the analysis of the MBN signal, such as the calculation of MBN rms or MBN peak−position , does not show promising results. Within the experimental conditions employed in this research—even with the use of severe cutting parameters, above the tool manufacturer's specifications—the MBN technique proved ineffective in evaluating the hardness levels generated in the material after the turning processes. In item 3.2.1, it was shown that the severe machining conditions used in experiment 2 resulted in significant wear on the cutting tools. It was also mentioned that the MBN signal exhibited a possible correlation with the tool wear, showing sensitivity to microstructural changes in the material. With the results from this section (Hardness vs. MBN), it can be ruled out that the microstructural changes detected by MBN are related to variations in hardness. As will be presented in the next section, the most relevant microstructural variations generated in experiment 2—and to which MBN showed the greatest sensitivity—were the residual stresses. 3.2.4 Residual stress and MBN Residual stress measurements were performed on a set of 9 samples selected from the 48 generated in experimental stage 2. The selection of these samples was based on the amplitude of the MBNrms parameter, considering its broad application and effectiveness reported in the literature for residual stress measurements [ 11 ][ 12 ][ 13 ][ 14 ]. Thus, samples with distinct MBN rms values were chosen, machined with different cutting parameters, with an emphasis on the variation of cutting speed. In the turning of hardened materials, cutting speed is one of the parameters that most influences the residual stresses generated in the material [ 2 ][ 39 ][ 40 ]. Table 4 presents the results of the residual stresses measured in the selected samples. The pairs of samples (3 and 4, 5 and 6, and 8 and 9) were considered replicas based on the cutting parameters used. However, their surface integrity showed significant differences, mainly due to the progressive wear of the tool. In experimental stage 2, due to the continuous wear of the cutting tool, it can be stated that all the samples were generated under distinct machining conditions. As a result, it was not possible to obtain samples that could be considered true replicas. Table 4 Results of residual stress measurements in the selected samples from experimental stage 2. sample v c (m/min) f n (mm/rpm) MBN rms SD (MBN rms ) (+/-) Residual stress (RS) SD (RS) (+/-) 1 100 0,35 0,029352 0,00146760 -364 14,1 2 200 0,35 0,049569 0,00247845 -249,4 11,9 3 300 0,35 0,072337 0,00361685 93,3 10 4 300 0,35 0,118487 0,00592435 314 14 5 400 0,35 0,099126 0,00495630 266,7 16 6 400 0,35 0,140706 0,00703530 477 15,7 7 400 0,1 0,055989 0,00279945 478,6 9,6 8 400 0,25 0,122129 0,00610645 191,4 10,7 9 400 0,25 0,151336 0,00756680 496,5 15,1 Figure 13 presents the correlation between the MBN rms parameter and the residual stresses generated in the samples. It was observed that the result of sample 7 (highlighted by the red circle in Fig. 13 ) was responsible for the relatively low value of R²=0.55. This sample, despite showing a relatively high tensile residual stress (478.6 MPa), generated a low MBN signal (MBN rms = 0.055). To clarify this behavior, a microstructural analysis was conducted on a cross-sectional sample. Figure 18 shows an image obtained by optical microscopy of the microstructure of sample 7. In the micrograph, the presence of a continuous white layer approximately 3 µm thick was identified on the surface of the part. This type of microstructure explains the low amplitude of the MBNrms parameter = 0.055, even though the sample exhibits a high residual tensile stress. The presence of the white layer drastically reduces the MBN signal due to its specific characteristics: a high percentage of martensite, elevated dislocation density, and high hardness. These properties significantly hinder the movement of magnetic domain walls, resulting in a drastic decrease in the MBN signal. Since sample 7 exhibited a condition significantly different from the others, its result was excluded from the analysis. Figure 15 shows the correlation between the MBNrms parameter and the residual stress values, excluding the measurements of sample 7. With this adjustment, the correlation between these parameters was considerably improved, now presenting an R²=0.928. It can be seen in Fig. 19 that the MBNrms parameter increases with tensile residual stresses and decreases with compressive residual stresses. Tensile stress aligns the 180° domain walls in the direction of the stress, thus generating a greater number of magnetic domain walls in that direction. When a magnetic field is applied in the same direction as the tensile stress, the movement of more 180° domain walls occurs, which results in an increase in the MBN signal. On the other hand, under compressive stress, the 180° domain walls tend to align transversely to the direction of the stress. This transverse alignment reduces the MBN signal, as the magnetic field — applied in the direction of the stress — encounters fewer domain walls ready to respond to the stimulus. The MBN signal is directly influenced by the amount of 180° domain walls aligned with the direction of the magnetic field at a given moment [ 42 ]. 4 Conclusions This study investigated the possibilities of using the Barkhausen Noise (MBN) technique for assessing the surface integrity of hardened SAE-4340 steel after the turning process. Machining experiments were carried out using cutting parameters within the interval recommended by the tool manufacturer, as well as using cutting parameters above these intervals. Based on the results obtained, the following conclusions can be highlighted: In both experiments, the variations in hardness in the samples were relatively small, both under the cutting conditions recommended by the manufacturer (light machining) and under more aggressive conditions (parameters above the recommended values). The variations in hardness amplitudes were below ± 7.4% (between 54 HRC and 46 HRC). Under these conditions, and when analyzing the MBN rms and MBN peak−position signal parameters, the MBN technique did not show sensitivity in detecting the hardness levels. Roughness levels below Ra = 0.4 do not significantly influence the MBN signal response. In the correlation between the MBN technique and the residual stresses generated, the MBN rms parameter demonstrated adequate results, with a linear correlation index R² of 92%. Thus, this technique can be considered a viable alternative for non-destructive testing to monitor residual stresses in the production of serial parts. The presence of a white layer on the surface of the hardened and turned material drastically reduces the amplitude of the MBN signal, regardless of whether the material exhibits residual tensile or compressive stresses. This highly relevant phenomenon must be considered when implementing the technique as a system for monitoring residual stresses. It is believed that the MBN technique can be used for monitoring the wear of cutting tools. However, additional studies are needed to confirm this hypothesis. Declarations Author Contribution Daniel contributed to the conception and design of the study, conducted the experiments, and analyzed the data. He also drafted the original manuscript.Professor Freddy supervised the work, critically reviewed the manuscript, and provided methodological and analytical suggestions.Both authors reviewed and approved the final version of the manuscript. Acknowledgement The authors would like to thank: Favorit Aços Especiais for donating the SAE-4340 steel; Sandvik for donating the cutting tool and tool holder; TM Service Laboratório Metalurgico end EATON for assisting with the residual stress measurements References DeGarmo, E.P., Black, J.T., Kohser, R.A.: DeGarmo’s Materials and Processes in Manufacturing. Wiley (2008) Griffiths, B.: Manufacturing Surface Technology. Elsevier (2001) Barkhausen, H.: Zwei mit Hilfe der neuen Verstärker entdeckte Erscheinungen. Phys. Z., 20 , 401–403 Cardon, M.: The marketing of Barkhausen noise analysis. Shot Peener 18–20. (2007) Tiitto, S.: Acta Polytech. Scand. Appl. Phys. Ser. 119 , 1–80 (1977) Tiitto, M.: OTALA and SAYMJAKANGAS: Nondestr.Test., 9,117–120. 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Magn. 46 , 899–904 (2010). https://doi.org/10.1109/tmag.2009.2032417 Trillon, A., Deneuville, F., Petit, S., Bisiaux, B.: Magnetic Barkhausen Noise for hardness checking on steel pipes. 18th World Conference on Nondestructive Testing, April 2012, Durban, South Africa. e-Journal of Nondestructive Testing Vol. 17(7). https://www.ndt.net/?id=12515 Gauthier, J., Krause, T.W., Atherton, D.L.: Measurement of residual stress in steel using the magnetic Barkhausen noise technique. NDT E Int. 31 , 23–31 (1998). https://doi.org/10.1016/s0963-8695(97)00023-6 Hizli, H.: Applicability of the Magnetic Barkhausen Noise Method for Nondestructive Measurement of Residual Stresses in the Carburized and Tempered 19CrNi5H Steels. Res. Nondestr. Eval. 29 , 221–236 (2017). https://doi.org/10.1080/09349847.2017.1359711 Moorthy, V., Shaw, B.A., Day, S.: Evaluation of applied and residual stresses in case-carburised En36 steel subjected to bending using the magnetic Barkhausen emission technique. 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Journal of Physics Conference Series. 303, 012020–012020 (2011). https://doi.org/10.1088/1742-6596/303/1/012020 Stefanita, C.-G., Clapham, L., Yi, J.-K., ., Atherton, D.L.: Analysis of cold rolled steels of different reduction ratio using the magnetic Barkhausen noise technique. J. Mater. Sci. 36 , 2795–2799 (2001). https://doi.org/10.1023/a:1017981317255 Liu, T., Kikuchi, H., Kamada, Y., Ara, K., Kobayashi, S., Takahashi, S.: Comprehensive analysis of Barkhausen noise properties in the cold rolled mild steel. J. Magn. Magn. Mater. 310 , e989–e991 (2006). https://doi.org/10.1016/j.jmmm.2006.10.1033 Kaplan, M., Gür, C.H., Erdogan, M.: Characterization of Dual-Phase Steels Using Magnetic Barkhausen Noise Technique. J. Nondestr. Eval. 26 , 79–87 (2007). https://doi.org/10.1007/s10921-007-0022-0 Gimenez, R.M., Franco, F.A., Paula: Use of a Micromagnetic Nondestructive Test in the Evaluation of the α’-Martensitic Transformation Generated in the Mechanical Fatigue Process of the AISI 304L Stainless Steel. IEEE Trans. Magn. 56 , 1–8 (2020). https://doi.org/10.1109/tmag.2020.3005390 Avila, J.A., Conde, F.F., Pinto, H.C., Rodriguez, J., Grijalba, F.: Microstructural and Residuals Stress Analysis of Friction Stir Welding of X80 Pipeline Steel Plates Using Magnetic Barkhausen Noise. J. Nondestr. Eval. 38 (2019). https://doi.org/10.1007/s10921-019-0625-2 Armando, F.: Linilson Rodrigues Padovese: Non-destructive Flaw Mapping of Steel Surfaces by the Continuous Magnetic Barkhausen Noise Method: Detection of Plastic Deformation. J. Nondestr. Eval. 37 (2018). https://doi.org/10.1007/s10921-018-0480-6 Epp, J., Hirsch, T.: Residual Stress State Characterization of Machined Components by X-ray Diffraction and Multiparameter Micromagnetic Methods. Exp. Mech. 50 , 195–204 (2009). https://doi.org/10.1007/s11340-009-9231-z Rosipal, M., Neslusan, M., Ochodek, V., Sípek, M.: Application of Barkhausen Noise for analysis of surface quality after machining. Mater. Eng. 17 , (2010) Neslušan, M., Trojan, K., Haušild, P., Minárik, P., Mičietová, A., Čapek, J.: Monitoring of components made of duplex steel after turning as a function of flank wear by the use of Barkhausen noise emission. Mater. Charact. 169 , 110587–110587 (2020). https://doi.org/10.1016/j.matchar.2020.110587 Čížek, J., Neslušan, M., Čilliková, M., Mičietová, A., Melikhova, O.: Modification of steel surfaces induced by turning: non-destructive characterization using Barkhausen noise and positron annihilation. J. Phys. D. 47 , 445301 (2014). https://doi.org/10.1088/0022-3727/47/44/445301 Kubjatko, T., Mičieta, B., Čilliková, M., Neslušan, M., Mičietová, A.: Barkhausen Noise as a Reliable Tool for Sustainable Automotive Production. Sustainability. 14 , 4123 (2022). https://doi.org/10.3390/su14074123 Agrawal, A., Goel, S., Rashid, W.B., Price, M.: Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC). Appl. Soft Comput. 30 , 279–286 (2015). https://doi.org/10.1016/j.asoc.2015.01.059 Das, S.R., Dhupal, D., Kumar, A.: Study of surface roughness and flank wear in hard turning of AISI 4140 steel with coated ceramic inserts. J. Mech. Sci. Technol. 29 , 4329–4340 (2015). https://doi.org/10.1007/s12206-015-0931-2 Aouici, H., Fnides, B., Elbah, M., Benlahmidi, S., Bensouilah, H., Yallese, M.A.: Surface roughness evaluation of various cutting materials in hard turning of AISI H11. Int. J. Ind. Eng. Comput. 339–352 (2016). https://doi.org/10.5267/j.ijiec.2015.9.002 Diniz, A., Marcondes, F., Coppini, N.: Tecnologia da usinagem dos materiais. Artliber, São Paulo (2013) Rocha MachadoA: Teoria Da Usinagem Dos Materiais. BLUCHER, S.l (2015) Srivastava, A., Nahak, B.: Quantifying the impact of different material properties on Barkhausen noise generation from machined steel using machine learning techniques. Int. J. Interact. Des. Manuf. (IJIDeM). 18 , 3029–3041 (2023). https://doi.org/10.1007/s12008-023-01430-5 Tönshoff, H.K., Arendt, C., Amor, R.B.: Cutting of Hardened Steel. CIRP Ann. 49 , 547–566 (2000). https://doi.org/10.1016/s0007-8506(07)63455-6 Özel, T., Karpat, Y., Srivastava, A.: Hard turning with variable micro-geometry PcBN tools. CIRP Ann. 57 , 73–76 (2008). https://doi.org/10.1016/j.cirp.2008.03.063 Dubec, J., Neslusan, M., MULTIPARAMETRIC ANALYSIS OF SURFACE INTEGRITY AFTER TURNING THROUGH BARKHAUSEN NOISE IN RELATION TO TOOL WEAR:. MM Science Journal. 315–318 (2012). (2012). https://doi.org/10.17973/mmsj.2012_07_201205 Jacas-Cabrera, M., Rodríguez-Moliner, T., Silveira, J.L.L.: Influencia de la velocidad de corte y la velocidad de avance en la integridad superficial del acero aisi 1045. (2015). Ingeniería Mecánica. 18 Mohammadpour, M., Razfar, M.R., Jalili Saffar, R.: Numerical investigating the effect of machining parameters on residual stresses in orthogonal cutting. Simul. Model. Pract. Theory. 18 , 378–389 (2010). https://doi.org/10.1016/j.simpat.2009.12.004 Griffiths, B.J.: Mechanisms of White Layer Generation With Reference to Machining and Deformation Processes. J. Tribol. 109 , 525–530 (1987). https://doi.org/10.1115/1.3261495 Guo, Y., Warren, A.W., Krajnik, P.: The basic relationships between residual stress, white layer, and fatigue life of hard turned and ground surfaces in rolling contact. CIRP J. Manufact. Sci. Technol. 2 , 129–134 (2010). https://doi.org/10.1016/j.cirpj.2009.12.002 Krause, T.W., Clapham, L., Atherton, D.L.: Characterization of the magnetic easy axis in pipeline steel using magnetic Barkhausen noise. J. Appl. Phys. 75 , 7983–7988 (1994). https://doi.org/10.1063/1.356561 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Jun, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor assigned by journal 22 Apr, 2025 Submission checks completed at journal 20 Apr, 2025 First submitted to journal 20 Apr, 2025 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. 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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-5694201","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447482677,"identity":"3f263d53-ec89-43d4-a496-b2b4fc0ee641","order_by":0,"name":"Daniel Rodrigues Carlos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYHACAxCSATIYDzAw2IDoxgPEaOEBsYAq00BaGojQwgDXcpgBysAN5Gcf3vi5oMCGh7/97IMDH/6ct1vbfhhoS41NNE4rzqUVS88wSOOROJNucHBm2+3kbWcSgVqOpeU24NLCw2MgzWNwmMcA6I3DvA23k80OALUwNhzGqUW+h8f4N4/Bfx4D/mcMh3n+nEs2O/8QvxaGMzxmQFsO8BhIAG3hYTtgZ3aDgC0GZ9jKrHkMknkkbjxjAPolOcHsBtCWBDx+ke9h3nyb54+dHH9/GuODD3/s7M3Opz988KHGBrfD0EEiWGUCscpBwJ4UxaNgFIyCUTAyAABiUV8NjLOeTwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Campinas - UNICAMP","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"Rodrigues","lastName":"Carlos","suffix":""},{"id":447482678,"identity":"1f8e697f-c496-4c85-870d-643c5fe0e192","order_by":1,"name":"Freddy Armando Franco Grijalba","email":"","orcid":"","institution":"University of Campinas - UNICAMP","correspondingAuthor":false,"prefix":"","firstName":"Freddy","middleName":"Armando Franco","lastName":"Grijalba","suffix":""}],"badges":[],"createdAt":"2024-12-22 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5694201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5694201/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01226-5","type":"published","date":"2025-06-26T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81578394,"identity":"0a54cf9b-5bee-4813-a567-a66be5680dea","added_by":"auto","created_at":"2025-04-28 18:22:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26890,"visible":true,"origin":"","legend":"\u003cp\u003eArrangement of samples on each bar.\u003c/p\u003e","description":"","filename":"fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/9acf4772c5bf4633f2a545ea.jpg"},{"id":81579819,"identity":"79de2929-c711-4928-a6bf-2d5253972b6f","added_by":"auto","created_at":"2025-04-28 18:46:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25718,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of MBN measurement system.\u003c/p\u003e","description":"","filename":"fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/01785b67980972993a4e6b94.jpg"},{"id":81579019,"identity":"93b66c0f-b144-4917-878f-371e0f385f85","added_by":"auto","created_at":"2025-04-28 18:30:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69155,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Stage 1. MBN signal envelope obtained from samples with two machining conditions. a) Sample with cutting parameters fn = 0.1 mm/rpm, Vc = 126 m/min, and ap = 0.5 mm, and b) Sample with cutting parameters fn = 0.24 mm/rpm, Vc = 126 m/min, and ap = 0.5 mm.\u003c/p\u003e","description":"","filename":"fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/606003ee6c76ae7994e481a2.jpg"},{"id":81578400,"identity":"48712ab7-89dc-4f77-97c7-8f87aedaea2a","added_by":"auto","created_at":"2025-04-28 18:22:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41117,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of roughness measurements as a function of cutting parameters in experimental stage 1. a) Using a cutting depth of ap 0.25 mm; b) Using a cutting depth of ap 0.50 mm.\u003c/p\u003e","description":"","filename":"fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/9fde58ebc9ad1a41213cea8a.jpg"},{"id":81579632,"identity":"0b7d350e-5b19-44c3-aa33-65ec12aa5df5","added_by":"auto","created_at":"2025-04-28 18:38:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19884,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the MBNrms parameter and the surface roughness levels measured on the samples in experiment 1.\u003c/p\u003e","description":"","filename":"fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/7df2c6006d1f82d54e33611a.jpg"},{"id":81578404,"identity":"12551da7-352d-450e-9200-c23599d7ae82","added_by":"auto","created_at":"2025-04-28 18:22:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41194,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of cutting parameters on surface hardness in Experiment 1. a) Depth of Cut (ap), b) Cutting Speed (Vc), and c) Feed Rate (fn).\u003c/p\u003e","description":"","filename":"fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/a2424a1dd2ca51f71ad3c254.jpg"},{"id":81579637,"identity":"17716c7c-6a10-4d75-b22d-f759c0b92d0a","added_by":"auto","created_at":"2025-04-28 18:38:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41494,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between MBN signal features and surface hardness for Experiment 1. a) MBNrms vs. Hardness, b) MBNpeak-position vs. Hardness.\u003c/p\u003e","description":"","filename":"fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/f9eb40f340de51baa7fb0e77.jpg"},{"id":81579025,"identity":"578059e7-4304-4a78-a55b-a1e664c3dc6e","added_by":"auto","created_at":"2025-04-28 18:30:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":87153,"visible":true,"origin":"","legend":"\u003cp\u003eMBN signal envelopes and tool wear for two machining conditions in experiment 2.\u003c/p\u003e","description":"","filename":"fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/37b4d452ad7ae5952f24a5a0.jpg"},{"id":81579821,"identity":"eb6b6abc-89e7-406a-9ba4-1050ffdd8bdf","added_by":"auto","created_at":"2025-04-28 18:46:26","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33241,"visible":true,"origin":"","legend":"\u003cp\u003eRa roughness measurements as a function of cutting parameters in experiment 2.\u003c/p\u003e","description":"","filename":"fig.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/3b68f1235d04920829975a50.jpg"},{"id":81578409,"identity":"f2846af5-4e59-4cd5-ad69-9896c6f3f362","added_by":"auto","created_at":"2025-04-28 18:22:26","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":18433,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the MBN\u003csub\u003erms\u003c/sub\u003e parameter and the surface roughness levels measured in the samples of experiment 2.\u003c/p\u003e","description":"","filename":"fig.10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/f43bc1ef0376502f51fe16d1.jpg"},{"id":81579021,"identity":"6590b312-89e1-4919-aa59-cdb229a58dcc","added_by":"auto","created_at":"2025-04-28 18:30:26","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":31271,"visible":true,"origin":"","legend":"\u003cp\u003eSurface hardness variation in Experiment 2 as a function of machining parameters. a) Cutting Speed (v\u003csub\u003ec\u003c/sub\u003e), b) Feed Rate (fn).\u003c/p\u003e","description":"","filename":"fig.11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/0844a907bd8470ce715e5219.jpg"},{"id":81580305,"identity":"3d0056f5-0a3c-4c46-a4e9-b166524a56b1","added_by":"auto","created_at":"2025-04-28 18:54:26","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":37744,"visible":true,"origin":"","legend":"\u003cp\u003eMBN signal analysis in relation to surface hardness in Experiment 2. a) MBNrms vs. Hardness, b) MBNpeak-position vs. Hardness.\u003c/p\u003e","description":"","filename":"fig.12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/488dcda1110ae1a2cf33dc53.jpg"},{"id":81579639,"identity":"e5ae6008-5261-4229-96d8-dbbf1a93b1bf","added_by":"auto","created_at":"2025-04-28 18:38:26","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":42750,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the MBN\u003csub\u003erms\u003c/sub\u003e parameter and the residual stress measured in samples from experiment 2.\u003c/p\u003e","description":"","filename":"fig.13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/f1d1f405324a54e20d69a1d1.jpg"},{"id":81578420,"identity":"d163daef-1388-4576-9707-f9efb7e015f8","added_by":"auto","created_at":"2025-04-28 18:22:26","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":46515,"visible":true,"origin":"","legend":"\u003cp\u003eMicrograph of a cross-section of sample 7. Identification of the presence of a white layer. Experiment 2 stage.\u003c/p\u003e","description":"","filename":"fig.14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/7c0f59d99c9dc83360349bf4.jpg"},{"id":81578458,"identity":"9c7fb260-2f47-4477-8a1f-08a07e472b5d","added_by":"auto","created_at":"2025-04-28 18:22:27","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":45963,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the MBN\u003csub\u003erms \u003c/sub\u003eparameter and the residual stress measured in samples from experiment 2, excluding sample 7 which exhibited a white layer on its surface.\u003c/p\u003e","description":"","filename":"fig.15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/f80cb0f6e4541fce4775e3fa.jpg"},{"id":85686283,"identity":"2db0d81a-46fe-4c45-9b91-2c8576e9bbd2","added_by":"auto","created_at":"2025-06-30 16:05:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5694201/v1/590df5e4-09e2-4523-9663-78db2a1f540e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensitivity of Barkhausen Noise in Monitoring the Surface Integrity of SAE 4340 Hardened Steel After Turning Process","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe surface integrity of mechanical components plays a crucial role in performance and durability in industrial applications, especially in cases where components are subjected to high loads or harsh environments. Rigorous control of this integrity, through optimized manufacturing processes, is essential to meet the required quality standards, ensuring the preservation of mechanical properties. This attention is even more relevant in designs that demand a high degree of surface integrity, as changes can occur both in the surface layers and the subsurface layers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-destructive testing (NDT) plays an essential role in the manufacturing of mechanical components, ensuring the quality and reliability of products without compromising their functionality. In the context of surface integrity of parts, the application of NDT is indispensable to guarantee superior performance of components. Although conventional techniques, such as X-ray diffraction for residual stress evaluation and hardness testing, are widely used for this purpose, in many cases, these methodologies are classified as destructive.\u003c/p\u003e \u003cp\u003eNew non-destructive testing (NDT) techniques have been widely studied over the years, resulting in procedures that offer higher quality and safety in the evaluation of mechanical components and industrial equipment. Among these techniques, Magnetic Barkhausen Noise (MBN), discovered in 1919 by Professor Heinrich Barkhausen [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], stands out. MBN gained practical relevance in the 1970s with the contributions of researcher Tiitto Seppo, who began exploring its applications in the field of NDT [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Since then, MBN has proven to be effective in various applications, such as hardness level evaluation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], measurement of residual stresses on machined surfaces [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], plastic deformation analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], study of anisotropy in rolled sheets [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and detection of phase transformations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMBN is generated by the movement of magnetic domain walls in ferromagnetic materials under the influence of an alternating magnetic field. This movement occurs irregularly, involving irreversible jumps and, to a lesser extent, reversible jumps, as well as rotations of the magnetic domains. As a result, MBN presents a stochastic and complex signal, covering a wide range of frequencies. The analysis of this signal requires the use of advanced signal processing techniques and statistical methods, as it reflects the interaction between microscopic events and the magnetic properties of the material [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of the MBN technique to evaluate the surface integrity of machined parts has proven to be very promising, especially for the analysis of residual stresses and the detection of grinding burns. In the context of surface integrity generated by turning processes, some studies have been published. Epp and Hirsch (2009) compared the results obtained using X-ray diffraction and MBN techniques, using the FWHM parameter (Full Width at Half Maximum of the MBN envelope) to measure the residual stress generated after turning processes on samples with different geometries. The authors found that MBN showed good correlation with the X-ray technique in AISI 52100 and AISI 5210 materials that were heat-treated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Rosipal et al. (2010) used the MBN technique to evaluate 100Cr6 steel with an average hardness of 62 HRC after turning and grinding processes. The analysis of the MBN signal envelope allowed for the identification of increased residual stress levels [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Neslušan (2020) investigated the use of MBN in monitoring microstructural changes and residual stresses induced by severe plastic deformations in duplex stainless steel subjected to turning. The results revealed that wear significantly influenced both the microstructure and the residual stress state of the parts. The MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e parameter (position of the maximum amplitude of the MBN envelope) showed a progressive increase with wear and observed microstructural changes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Cizek (2014) studied the behavior of 100Cr6 steel during turning, using carbide inserts with different flank wear levels (VB). The results showed that the MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e and MBN\u003csub\u003erms\u003c/sub\u003e parameters were affected by the dislocation density generated by tool wear [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Kubjatko et al. (2022) employed the MBN technique to monitor the integrity of automotive components after turning, considering different tool wear (VB) conditions and subsequent plasma nitriding. The results indicated that the MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e parameter was effective in identifying parts with compromised quality due to low nitrides density, particularly in situations of high tool wear [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to contribute to the advancement of the content available in the scientific literature, this work presents results on the use of the Magnetic Barkhausen Noise technique as a tool for monitoring the surface integrity of SAE 4340 steel hardened after turning processes. For this, two distinct experimental plans were conducted. In the first experiment, the cutting parameters were defined within the ranges recommended by the tool manufacturer. In the second experiment, more aggressive machining conditions were applied, with cutting parameters above those specified by the manufacturer. The results obtained highlight which surface integrity characteristics can be efficiently monitored using the MBN technique.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Material\u003c/h2\u003e \u003cp\u003eIn this study, SAE 4340 steel was used, provided in the form of shafts with 1 meter in length and a diameter of 50.8 mm (2\u0026rdquo;). The chemical composition of the material is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The samples were submitted to quenching heat treatment at 850\u0026deg;C for 60 minutes, and cooled in oil at 25\u0026deg;C. Then, the samples were tempered at 250\u0026deg;C (for 120 minutes) and cooled in a calm air environment. The resulting hardness value was 54 HRC.\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 SAE 4340 material. Weight %.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,25\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 Samples\u003c/h2\u003e \u003cp\u003eThe 1-meter shafts were cut into smaller sections of 250 mm, resulting in 6 bars. After the cutting process, in order to correct imperfections from the rolling process, the material was machined, removing 1 mm from the diameter. The machining was performed on a conventional Nardini lathe, model MS205, using the following cutting parameters: cutting speed (Vc) of 90 m/min, feed rate (fn) of 0.1 mm/rev, and cutting depth (ap) of 0.25 mm. Subsequently, rounded channels with a width of 3 mm were created to form tracks of 15 mm width. In each bar, 11 tracks were allocated, 9 of which were used as samples in the experiments. The tracks at the ends (first and last) were not considered for testing. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the arrangement of the samples in each bar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental design\u003c/h2\u003e \u003cp\u003eFor the machining of the test specimens with the cutting parameters to be evaluated, a NARDINI CNC lathe, model LOGIC 195, equipped with Fanuc Oi Mate-TC controls was used. The tool employed was a ceramic insert, model CNGA 120408T01020, mounted on a DCLNR 2525 M12 tool holder. This setup allowed the following geometry to be achieved: main position χr\u0026thinsp;=\u0026thinsp;95\u0026deg;, inclination angle λs\u0026thinsp;=\u0026thinsp;5\u0026ordm; and exit angle γn =-6\u0026ordm;.\u003c/p\u003e \u003cp\u003eThe experimental design of the cutting parameter combinations was divided into two stages. In experimental stage 1, light machining parameters, within the tool manufacturer's recommendations, were applied. In experimental stage 2, more severe machining parameters were used, exceeding the levels recommended by the manufacturer.\u003c/p\u003e \u003cp\u003eIn experimental stage 1, the selection of cutting parameters was based on the range of values recommended by the tool manufacturer, distributed between the minimum, average, and maximum values for cutting speed, feed rate, and depth of cut. The experimental design for this stage is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor each machining condition, three replicas (samples) were produced. Thus, a total of 54 samples were generated (3 cutting speeds \u0026times; 3 feed rates \u0026times; 2 depths of cut \u0026times; 3 replicas). After machining each bar (corresponding to 9 samples), the cutting edge of the tool was replaced with a new one.\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\u003eCutting parameters used in experimental stage 1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVc (m/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efn (mm/rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eap (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the machining of the samples from experimental step 2, more aggressive cutting parameters were used as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Four cutting speed levels and four feed rate levels were defined, keeping the depth of cut constant. Thus, a total of 48 samples were produced (4 cutting speeds \u0026times; 4 feed rates \u0026times; 3 replicas).\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\u003eCutting parameters used in experimental stage 2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVc (m/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efn (mm/rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eap (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5\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\u003eIn the machining procedures of experimental step 2, no pre-machining was performed, as no significant geometric errors were identified in the previous step (experiment 1) that could compromise the homogeneous material removal. Additionally, considering the cutting depth (ap) of 0.5 mm used in experimental step 2, there was no overlap of material effects generated by the two experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Hardness measurements.\u003c/h2\u003e \u003cp\u003eThe surface hardness test was conducted using a Mitutoyo hardness tester, model Wizhard, on the HR 15N scale. To verify hardness homogeneity, four measurements were taken per sample, distributed along its perimeter. However, in the presentation of the results, the values were converted and expressed on the HRC scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Roughness measurement\u003c/h2\u003e \u003cp\u003eThe roughness measurements were performed with a portable roughness tester from Mitutoyo, model SJ-201. For each sample, six equidistant measurements were taken along the perimeter, using a cut-off of 0,8 mm and oriented transversely to the cutting direction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Residual stress measurement by X-ray diffraction\u003c/h2\u003e \u003cp\u003eResidual stresses were evaluated using the X-ray diffraction (XRD) method on the surface of the workpiece, in the longitudinal direction relative to the cutting direction of the tool. The residual stress profiles were obtained using a PROTO X-ray diffractometer, model LXRD, equipped with Cr K-Alpha radiation (wavelength of 2,291 \u0026Aring;), a 1 mm beam, and a diffraction angle of 156.41\u0026deg;, in the crystallographic plane {211}.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Barkhausen Magnetic Noise Measurement.\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the schematic diagram of the MBN measurement system used in the tests. The measurement system was developed in our laboratory. The MBN probe consists of a U-shaped iron-silicon core, which is why it is called a YOKE. Around the core, approximately 400 turns of AWG24 copper wire were wound. Due to the geometric condition of the samples, the poles of the YOKE were machined using wire electrical discharge machining to enhance its coupling with the cylindrical surface of the sample.\u003c/p\u003e \u003cp\u003eThe YOKE receives an alternating current with a sine wave pattern, with a current amplitude of 1.0 A and an excitation frequency of 20 Hz, magnetizing the sample to be analyzed. The magnetization causes the movement of the domain walls in the material, generating magnetic pulses that are converted into voltage pulses by the pick-up coil. The spool of the pick-up coil, with a diameter of 10 mm, was made from polyvinyl chloride (PVC) to accommodate about 4,000 turns of copper wire with a diameter of 0.05 mm.\u003c/p\u003e \u003cp\u003eThe generated MBN signal passes through a signal conditioner, which amplifies it by 30 dB and filters it in a frequency band of 1-150 kHz. This frequency range is chosen to ensure the capture of the most representative signal measured by the pick-up coil. After conditioning, the signal was converted from analog to digital using the NATIONAL INSTRUMENTS NI USB-6361 data acquisition board. To acquire the data, it is necessary to use a sampling frequency that is at least twice the analysis frequency (in this case, 150 kHz). Therefore, a data acquisition frequency of 600 kHz was used to improve the resolution of the recorded signal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn experiment 1, measurements were taken at 7 different positions along the perimeter of each sample's surface, spaced 52\u0026deg; apart. At each position, 10 MBN signals were acquired, each signal consisting of 4 MBN bursts. In experiment 2, measurements were taken at 4 positions along the perimeter of each sample's surface, spaced 45\u0026deg; apart. Similar to experiment 1, 10 MBN signals were acquired at each measurement position. All MBN measurements were performed by orienting the magnetization field in the cutting direction of the samples, i.e., in the tangential direction.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experimental stage 1\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 MBN Signal Behavior.\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the result of the MBN signal envelope obtained for two groups of samples (replicate sets). In both groups, the same cutting speed, vc\u0026thinsp;=\u0026thinsp;126 mm/min, and cutting depth, ap\u0026thinsp;=\u0026thinsp;0.5 mm, were used, while the feed rate, fn, was different. In the first group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), fn\u0026thinsp;=\u0026thinsp;0.1 mm/rpm, and in the second group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), fn\u0026thinsp;=\u0026thinsp;0.24 mm/rpm. The envelopes, indicated by the same color (7 curves), represent the average of the 10 MBN signals measured at each position (7 positions along the perimeter of each sample).\u003c/p\u003e \u003cp\u003eIn the case of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, using a lower feed rate of 0.1 mm/rpm, minimal variations are observed in the MBN envelope, both between the different positions of a single sample and between the replicas. Hypothetically, this result suggests the formation of a surface with homogeneous characteristics along the entire perimeter of each sample, with minimal variations between the replicas. On the other hand, in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, with a higher feed rate of fn\u0026thinsp;=\u0026thinsp;0.24 mm/rpm, a difference is observed between the signals of the replicas, which may indicate the influence of possible changes in the tool during the machining process of the replicas. In the results of experiment 2, this effect was more pronounced and is discussed in more depth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Roughness and MBN.\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the correlation between the cutting parameters and the roughness amplitudes obtained from the samples after the turning processes. The measured roughness values are in accordance with what is expected in the literature and in line with the cutting parameters used [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It can be observed that feed rate was the cutting parameter with the greatest influence on roughness. The feed rate is directly related to the formation of the geometric profile of the machined surface. As the feed increases, the spacing between the grooves generated by the cutting edge of the tool also increases, resulting in a more irregular surface and, consequently, higher Ra values. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the correlation between the measured roughness amplitudes Ra and the MBN\u003csub\u003erms\u003c/sub\u003e parameter of the MBN signals. It can be observed that there was no significant correlation between roughness and the MBN\u003csub\u003erms\u003c/sub\u003e parameter. The roughness amplitudes found in the samples, below 2 Ra, did not generate a noticeable effect on the MBN signal\u003c/p\u003e \u003cp\u003eSrivastava and Nahak (2023) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that the Barkhausen signal was sensitive to roughness, especially in the analysis of the MBN\u003csub\u003erms\u003c/sub\u003e parameters and the peak amplitude of the MBN envelope. This result was justified by the decrease in magnetic coupling, which directly influenced the MBN signals due to the reduction in magnetic flux. In the tests conducted in this work, the samples showed roughness values ranging from Ra\u0026thinsp;=\u0026thinsp;6 \u0026micro;m to Ra\u0026thinsp;=\u0026thinsp;9 \u0026micro;m, whereas in the present study, these roughness variations were significantly lower, ranging from Ra\u0026thinsp;=\u0026thinsp;0.9 \u0026micro;m Ra\u0026thinsp;=\u0026thinsp;2 \u0026micro;m. Therefore, it is believed that the low roughness levels obtained in this study did not significantly affect the probe's coupling with the surface of the sample or the magnetic flux.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Surface hardness and MBN\u003c/h2\u003e \u003cp\u003eThe variation in the hardness levels measured in the samples from experiment 1 was low, with a fluctuation of \u0026plusmn;\u0026thinsp;7.4%. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the correlation between the cutting parameters and the surface hardness measurements made on the samples. The relatively high deviations shown in the figures are a consequence of the low variation obtained in the hardness levels. It can be seen that the feed rate (fn) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) and cutting speed (Vc) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) did not exert a significant influence on the hardness, as indicated by the overlap of deviations in the graphs. On the other hand, the depth of cut (ap) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) showed a slight influence, although considered negligible. When the output variable, in this case the measured hardness levels, shows low variation, it is concluded that the input parameters do not significantly influence the response. Thus, the cutting parameters did not significantly influence the hardness of the samples. This result can be explained by the characteristics of the test: materials with relatively high hardness, such as those that are quenched, do not show significant changes in hardness levels due to the machining process, especially when it is performed within the parameters specified by the tool manufacturer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e][\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is worth noting that the cutting parameters used in experiment 1 were within the range recommended by the manufacturer, and these machining conditions are considered ideal, without significantly affecting the material's hardness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the correlations between MBN parameters (MBN\u003csub\u003erms\u003c/sub\u003e and MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e) and the hardness levels obtained in the samples from experiment 1. It can be observed that neither of the MBN parameters showed correlation with the surface hardness levels of the samples.\u003c/p\u003e \u003cp\u003eIn the vast majority of studies found in the scientific literature, where the Barkhausen method has been effective in monitoring hardness, the variation in hardness was significant. Trillon et al. (2012) measured hardness levels with a variation of \u0026plusmn;\u0026thinsp;26% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], Davut et al. (2007) with a variation of \u0026plusmn;\u0026thinsp;28% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Kaplan (2007) with a variation of \u0026plusmn;\u0026thinsp;23% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Grijalba et al. with a variation of \u0026plusmn;\u0026thinsp;19% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, the relatively high hardness variations have a more pronounced effect on the MBN signal compared to other potential changes generated in the material, such as residual stresses, plastic deformations, or phase transformations. Under these conditions, the analysis of a simple parameter like MBN\u003csub\u003erms\u003c/sub\u003e or MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e shows good sensitivity. In the results of this work, in experiment 1, the hardness variations were relatively low, around \u0026plusmn;\u0026thinsp;7.4%. Since the machining conditions were within the range recommended by the tool manufacturer, it is believed that other changes in the material, such as plastic deformations and residual stresses, were also relatively low and influenced the MBN signal in an equivalent manner. Thus, the MBN\u003csub\u003erms\u003c/sub\u003e or MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e parameters did not show sensitivity in measuring low hardness levels. The application of more sophisticated methods for analyzing MBN signals could help improve the measurement of small hardness variations. As of the submission date of this article, no published works with these characteristics have been found.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Experimental stage 2\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Behavior of MBN signals.\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the MBN signal envelopes for two sets (replicates) of samples where only the cutting speed was changed. The wear levels generated on the corresponding tools after machining the third replicate are also displayed. In Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, the results obtained with a cutting speed of vc\u0026thinsp;=\u0026thinsp;300 m/min are shown, while in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, the results for vc\u0026thinsp;=\u0026thinsp;400 m/min are presented.\u003c/p\u003e \u003cp\u003eIt is important to highlight that the cutting parameters used in experiment 2 were adjusted above the manufacturer's recommended range, characterizing severe machining conditions. Additionally, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, significant tool wear was observed after machining the third replicate. In the MBN envelopes, the black lines represent the signal measured in the first replicate machined with the tool in a 'new' condition (without wear), while the red lines correspond to the signal of the third replicate machined. The tool wear levels, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, were measured after machining the third replicate.\u003c/p\u003e \u003cp\u003eThis evolution in tool wear inevitably resulted in distinct changes in the material's microstructure between the replicates, changes that are evident in the MBN envelopes. It is observed that the envelopes of the replicates showed more noticeable differences compared to experiment 1, which was conducted under lighter machining conditions.\u003c/p\u003e \u003cp\u003eWhen comparing the MBN signal envelopes in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea (vc\u0026thinsp;=\u0026thinsp;300 m/min) with those in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb (vc\u0026thinsp;=\u0026thinsp;400 m/min), it is observed that, with a higher cutting speed, the difference between the replicates becomes even more evident. This behavior highlights that cutting speed has a significant influence on the quality of the replicates. Increasing the cutting speed raises the temperature during the machining process, accelerating tool wear.\u003c/p\u003e \u003cp\u003eAccording to Dubec [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], severe tool wear can induce structural transformations in the layers near the surface, compromising both the surface integrity and the magnetic behavior of the material. The MBN envelopes obtained at vc\u0026thinsp;=\u0026thinsp;400 m/min, which showed more pronounced differences between replicas compared to those at vc\u0026thinsp;=\u0026thinsp;300 m/min, indicate more intense microstructural changes due to the higher tool wear.\u003c/p\u003e \u003cp\u003eThe sensitivity of the MBN technique makes it a promising tool for quality control in continuous monitoring of tool wear. This application is particularly relevant in high-productivity scenarios, where mass production of low-complexity parts requires quick and effective control. The use of MBN can allow for early identification of problems in the machining process, reducing cutting downtime and ensuring the dimensional consistency of the manufactured parts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Roughness and MBN.\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the correlation between the cutting parameters and the roughness amplitudes obtained in the samples after the turning processes carried out in experiment 2. In this experiment, the samples reached roughness levels of up to 4 Ra, which represents twice the highest level observed in experiment 1.\u003c/p\u003e \u003cp\u003eIt is important to highlight that, even when using cutting parameters above the values recommended by the tool manufacturer, the feed rate continues to be the parameter with the greatest influence on roughness. It can be observed, in some conditions, that the deviations between the replicas show relatively high values, especially from a feed rate of 0.25 mm/rev, at cutting speeds of 300 m/min and 400 m/min. It is believed that this behavior is a consequence of the higher levels of tool wear generated under these severe machining conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the correlation between the Ra roughness amplitudes measured and the MBN\u003csub\u003erms\u003c/sub\u003e parameter of the MBN signals. It can be observed that, similar to experiment 1, no correlation was identified between the roughness and the MBN\u003csub\u003erms\u003c/sub\u003e parameter. The roughness amplitudes obtained in the samples, which reached values up to 4 Ra, did not have a significant impact on the MBN signal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Surface hardness and MBN\u003c/h2\u003e \u003cp\u003eThe variation in hardness levels observed in the samples from experiment 2, \u0026plusmn;\u0026thinsp;7.3%, was similar to that in experiment 1. Despite using more severe cutting parameters, exceeding the limits recommended by the tool manufacturer, the changes in hardness were not significant. This suggests that, even under more severe cutting conditions, the material's hardness remained relatively unchanged. This result could be attributed to the inherent high hardness of the material used or the fact that, although the cutting parameters were harsh, they did not induce enough thermal or mechanical stress to cause noticeable changes in hardness.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e presents the correlation between the cutting parameters and the surface hardness measurements taken from the samples in experiment 2. The results were consistent with those found in experiment 1. Although more severe cutting parameters were applied, they did not exert a significant influence on the hardness of the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the correlations between the MBN parameters (MBN\u003csub\u003erms\u003c/sub\u003e and MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e) and the hardness levels obtained from the samples in experiment 2. Once again, it is observed that neither of the two MBN parameters demonstrated any correlation with the surface hardness levels of the samples. It is confirmed that when the variations in hardness levels are relatively small, the analysis of the MBN signal, such as the calculation of MBN\u003csub\u003erms\u003c/sub\u003e or MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e, does not show promising results. Within the experimental conditions employed in this research\u0026mdash;even with the use of severe cutting parameters, above the tool manufacturer's specifications\u0026mdash;the MBN technique proved ineffective in evaluating the hardness levels generated in the material after the turning processes.\u003c/p\u003e \u003cp\u003eIn item 3.2.1, it was shown that the severe machining conditions used in experiment 2 resulted in significant wear on the cutting tools. It was also mentioned that the MBN signal exhibited a possible correlation with the tool wear, showing sensitivity to microstructural changes in the material. With the results from this section (Hardness vs. MBN), it can be ruled out that the microstructural changes detected by MBN are related to variations in hardness. As will be presented in the next section, the most relevant microstructural variations generated in experiment 2\u0026mdash;and to which MBN showed the greatest sensitivity\u0026mdash;were the residual stresses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Residual stress and MBN\u003c/h2\u003e \u003cp\u003eResidual stress measurements were performed on a set of 9 samples selected from the 48 generated in experimental stage 2. The selection of these samples was based on the amplitude of the MBNrms parameter, considering its broad application and effectiveness reported in the literature for residual stress measurements [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, samples with distinct MBN\u003csub\u003erms\u003c/sub\u003e values were chosen, machined with different cutting parameters, with an emphasis on the variation of cutting speed.\u003c/p\u003e \u003cp\u003eIn the turning of hardened materials, cutting speed is one of the parameters that most influences the residual stresses generated in the material [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the residual stresses measured in the selected samples. The pairs of samples (3 and 4, 5 and 6, and 8 and 9) were considered replicas based on the cutting parameters used. However, their surface integrity showed significant differences, mainly due to the progressive wear of the tool.\u003c/p\u003e \u003cp\u003eIn experimental stage 2, due to the continuous wear of the cutting tool, it can be stated that all the samples were generated under distinct machining conditions. As a result, it was not possible to obtain samples that could be considered true replicas.\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\u003eResults of residual stress measurements in the selected samples from experimental stage 2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev\u003csub\u003ec\u003c/sub\u003e (m/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ef\u003csub\u003en\u003c/sub\u003e (mm/rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBN\u003csub\u003erms\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD (MBN\u003csub\u003erms\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003e(+/-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResidual stress (RS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD (RS)\u003c/p\u003e \u003cp\u003e(+/-)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,029352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00146760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,049569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00247845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-249,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,072337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00361685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,118487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00592435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,099126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00495630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e266,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,140706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00703530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,055989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00279945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e478,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,122129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00610645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e191,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,151336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,00756680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e496,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,1\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\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e presents the correlation between the MBN\u003csub\u003erms\u003c/sub\u003e parameter and the residual stresses generated in the samples. It was observed that the result of sample 7 (highlighted by the red circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e) was responsible for the relatively low value of R\u0026sup2;=0.55. This sample, despite showing a relatively high tensile residual stress (478.6 MPa), generated a low MBN signal (MBN\u003csub\u003erms\u003c/sub\u003e = 0.055).\u003c/p\u003e \u003cp\u003eTo clarify this behavior, a microstructural analysis was conducted on a cross-sectional sample. Figure\u0026nbsp;18 shows an image obtained by optical microscopy of the microstructure of sample 7. In the micrograph, the presence of a continuous white layer approximately 3 \u0026micro;m thick was identified on the surface of the part.\u003c/p\u003e \u003cp\u003eThis type of microstructure explains the low amplitude of the MBNrms parameter\u0026thinsp;=\u0026thinsp;0.055, even though the sample exhibits a high residual tensile stress. The presence of the white layer drastically reduces the MBN signal due to its specific characteristics: a high percentage of martensite, elevated dislocation density, and high hardness. These properties significantly hinder the movement of magnetic domain walls, resulting in a drastic decrease in the MBN signal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince sample 7 exhibited a condition significantly different from the others, its result was excluded from the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e shows the correlation between the MBNrms parameter and the residual stress values, excluding the measurements of sample 7. With this adjustment, the correlation between these parameters was considerably improved, now presenting an R\u0026sup2;=0.928.\u003c/p\u003e \u003cp\u003eIt can be seen in Fig.\u0026nbsp;19 that the MBNrms parameter increases with tensile residual stresses and decreases with compressive residual stresses. Tensile stress aligns the 180\u0026deg; domain walls in the direction of the stress, thus generating a greater number of magnetic domain walls in that direction. When a magnetic field is applied in the same direction as the tensile stress, the movement of more 180\u0026deg; domain walls occurs, which results in an increase in the MBN signal.\u003c/p\u003e \u003cp\u003e On the other hand, under compressive stress, the 180\u0026deg; domain walls tend to align transversely to the direction of the stress. This transverse alignment reduces the MBN signal, as the magnetic field \u0026mdash; applied in the direction of the stress \u0026mdash; encounters fewer domain walls ready to respond to the stimulus. The MBN signal is directly influenced by the amount of 180\u0026deg; domain walls aligned with the direction of the magnetic field at a given moment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study investigated the possibilities of using the Barkhausen Noise (MBN) technique for assessing the surface integrity of hardened SAE-4340 steel after the turning process. Machining experiments were carried out using cutting parameters within the interval recommended by the tool manufacturer, as well as using cutting parameters above these intervals. Based on the results obtained, the following conclusions can be highlighted:\u003c/p\u003e \u003cp\u003eIn both experiments, the variations in hardness in the samples were relatively small, both under the cutting conditions recommended by the manufacturer (light machining) and under more aggressive conditions (parameters above the recommended values). The variations in hardness amplitudes were below \u0026plusmn;\u0026thinsp;7.4% (between 54 HRC and 46 HRC). Under these conditions, and when analyzing the MBN\u003csub\u003erms\u003c/sub\u003e and MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e signal parameters, the MBN technique did not show sensitivity in detecting the hardness levels.\u003c/p\u003e \u003cp\u003eRoughness levels below Ra\u0026thinsp;=\u0026thinsp;0.4 do not significantly influence the MBN signal response.\u003c/p\u003e \u003cp\u003eIn the correlation between the MBN technique and the residual stresses generated, the MBN\u003csub\u003erms\u003c/sub\u003e parameter demonstrated adequate results, with a linear correlation index R\u0026sup2; of 92%. Thus, this technique can be considered a viable alternative for non-destructive testing to monitor residual stresses in the production of serial parts.\u003c/p\u003e \u003cp\u003eThe presence of a white layer on the surface of the hardened and turned material drastically reduces the amplitude of the MBN signal, regardless of whether the material exhibits residual tensile or compressive stresses. This highly relevant phenomenon must be considered when implementing the technique as a system for monitoring residual stresses.\u003c/p\u003e \u003cp\u003eIt is believed that the MBN technique can be used for monitoring the wear of cutting tools. However, additional studies are needed to confirm this hypothesis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDaniel contributed to the conception and design of the study, conducted the experiments, and analyzed the data. He also drafted the original manuscript.Professor Freddy supervised the work, critically reviewed the manuscript, and provided methodological and analytical suggestions.Both authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank: Favorit A\u0026ccedil;os Especiais for donating the SAE-4340 steel; Sandvik for donating the cutting tool and tool holder; TM Service Laborat\u0026oacute;rio Metalurgico end EATON for assisting with the residual stress measurements\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeGarmo, E.P., Black, J.T., Kohser, R.A.: DeGarmo\u0026rsquo;s Materials and Processes in Manufacturing. Wiley (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths, B.: Manufacturing Surface Technology. 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Phys. \u003cb\u003e75\u003c/b\u003e, 7983\u0026ndash;7988 (1994). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1063/1.356561\u003c/span\u003e\u003cspan address=\"10.1063/1.356561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Magnetic Barkhausen Noise, Turning, Surface Characterization, Surface integrity.","lastPublishedDoi":"10.21203/rs.3.rs-5694201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5694201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work investigates the feasibility of using the non-destructive testing technique based on the measurement of Magnetic Barkhausen Noise (MBN) to assess the surface integrity of SAE 4340 hardened steel after the turning process. The experiments were conducted using ceramic inserts and divided into two distinct experimental setups. In Experiment 1, the cutting parameters were kept within the levels recommended by the tool manufacturer. In Experiment 2, cutting parameters above these levels were applied. For the resulting samples, measurements of roughness, hardness, and residual stress were performed. MBN measurements were taken in the longitudinal direction to the cutting direction, and from the obtained signals, the MBN\u003csub\u003erms\u003c/sub\u003e e MBN\u003csub\u003epeak\u0026minus;position\u003c/sub\u003e parameters were calculated and analyzed. The results showed that the MBN technique was unable to differentiate the hardness levels generated in the samples from both experiments. This behavior is attributed to the low variation in hardness observed between the samples (\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;7,4%). Regarding the roughness levels, it was found that, for the levels generated below Ra\u0026thinsp;=\u0026thinsp;4, the MBN parameters were not influenced. However, when comparing the MBN results with the residual stress levels obtained by X-ray technique, the method demonstrated high efficiency for the analyzed conditions, showing a strong linear correlation (R\u0026sup2; \u0026gt; 0,93).\u003c/p\u003e","manuscriptTitle":"Sensitivity of Barkhausen Noise in Monitoring the Surface Integrity of SAE 4340 Hardened Steel After Turning Process","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 18:22:21","doi":"10.21203/rs.3.rs-5694201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T13:41:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T13:38:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269607519323626060141253651870009368071","date":"2025-05-02T12:10:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T10:33:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T12:42:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291192605572214228369699495808754173031","date":"2025-04-23T19:46:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131007718588493847565027246000134210383","date":"2025-04-23T19:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T18:35:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-22T14:33:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T03:06:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Nondestructive Evaluation","date":"2025-04-20T15:50:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db0a178b-5b80-4690-bb1c-2bd1cdc814f2","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T16:04:48+00:00","versionOfRecord":{"articleIdentity":"rs-5694201","link":"https://doi.org/10.1007/s10921-025-01226-5","journal":{"identity":"journal-of-nondestructive-evaluation","isVorOnly":false,"title":"Journal of Nondestructive Evaluation"},"publishedOn":"2025-06-26 15:57:14","publishedOnDateReadable":"June 26th, 2025"},"versionCreatedAt":"2025-04-28 18:22:21","video":"","vorDoi":"10.1007/s10921-025-01226-5","vorDoiUrl":"https://doi.org/10.1007/s10921-025-01226-5","workflowStages":[]},"version":"v1","identity":"rs-5694201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5694201","identity":"rs-5694201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}