algorithm, implemented in a single hidden-layer feedforward neural network, sets random values for weights and biases, keeping them fixed during training. By utilizing two-angle kernel functions without hyperparameters, TAKELM simplifies the model and outperforms existing methods, showcasing remarkable predictive accuracy with minimal error compared to state-of-the-art approaches in tool wear prediction.

## Multi-sensor approach

To overcome the limitations of the use of AE or sound signals, the multi-sensor fusion method has become a hot research direction of TCM [65]. Combining various types of signals can improve the robustness of the method and reduce the sensitivity to noise and transient faults associated with using audio signals alone. A combination of vibration and sound signals (or AE signals) was adopted in more research work [34,35,43,57,61]. Vibrations can be acquired with the use of piezoelectric sensors and occur in the milling process due to the interaction between the material surface and the tool at particular spindle rotation frequencies. They are detrimental to tool life and workpiece surface finish. Zhu et al. [58] combined the use of all three types of signals (vibration, sound, and AE) to predict the tool conditions, fusing all three in a 2-D image with the angular matrix imaging method. The results demonstrated that vibration signals incorporated with sound signals outperform other combinations of signals to solve the tool wear classification problem. Yan et al. [29] combined the three types of signals, constructing a condition-adaptive hidden semi-Markov model for evaluating the wear condition and estimating RUL. Some studies demonstrated, instead, that the use of the only vibration sensor against the sound sensor allows to reached better performance for both regression and classification problems [24,48]. Cutting force signals, highly correlated with tool wear, offer another avenue for signal combination [36]. An increase in tool wear determines an increase in cutting force during milling operations, without changing the machine parameters. Dynamometer can be used to acquire cutting force signals, requiring a relatively simple installation in the machining process. A big disadvantage of using this type of signal is the need to reduce the noise content. Some research work used the current signal in combination with vibration and sound signals [26,41]. Letford et al. [27] combined the use of the four sensors (vibration, AE, cutting force, and spindle motor current) to solve the regression problem of tool wear prediction. Good performance was achieved by applying an artificial neural network model.

Other combinations are carried out in various research work. Sun et al. [49] conducted various experiments to determine optimal sensors and feature combinations for monitoring tool wear. Accelerometers, microphones, current transformers, and AE sensors are considered in the experiments and 29 features are selected from the time and frequency domain of the signals and the machine parameters. Results demonstrated that the use of all sensors, if not appropriate for the examined problem, can achieve poor performance. In addition, the use of only one type of sensor is preferred because it allows a considerable reduction in associated costs. Other studies used the axial load signals, combined with sound and spindle power signals, to solve the three-classes classification problem [50-51]. Chen et al. [53] used an online tool image acquisition method combined with the collection of AE signals to monitor the milling process. The images collected were processed to obtain the tool wear while AE features were used to train four ML models, achieving a recognition accuracy of 96.11%. McLeay et al. [40] developed a fault detection system for predicting tool replacement, utilizing an unsupervised learning model based on multiple sensors (AE, two microphones, two accelerometers, and a spindle power sensor).

Benchmark dataset. Two are the public benchmark datasets for the tool condition problem of a milling process: NASA and PHM 2010. These datasets are intended to check the accuracy of the RUL prediction models. More research work solved the problems using different approaches, where tool wear prediction or tool wear condition classification has been proposed. NASA dataset [66] was provided by the UC Berkeley Emergent Space Tensegrities (BEST) Lab. Experiments
were carried out with the Matsuura Machining Center MC-510, cast iron and steel material as workpiece, and a 60mm face mill with six KC710 inserted tools. Various combinations of machine parameters were tested, including variable depth of cut, variable feed rate, and a constant cutting speed. Data sampled by three different types of sensors (acoustic emission, vibration, and current sensors) were acquired at several positions. PHM 2010 [67] is provided for the PHM Data Challenge, a competition open to all the participants of the 2010 PHM Society Conference. Three types of sensors (dynamometer, accelerometer, and acoustic emission) were used to capture relevant signals from high-speed CNC milling machine operations, in which a workpiece made of Inconel 718 and a 6mm ball nose tungsten carbide cutter were used. Constant input machine parameters are set during the experiments and three training sets (named C1, C4, and C6) are provided to construct the predictive model. Table 1 shows the results obtained by different research work, solving the proposed problems.

<table><caption>Table 1. Literature results for benchmark datasets</caption><thead><tr><th>Benchmark Dataset</th><th>Type problem</th><th>Decision-Making Models</th><th colspan="3">Results</th><th>Study</th></tr></thead><tbody><tr><td rowspan="6">PHM 2010</td><td rowspan="3">Regression</td><td>Temporal Convolutional Network (TCN)</td><td colspan="3">MAE</td><td>[28]</td></tr><tr><td></td><td>C1: 9.5</td><td>C4: 10.9</td><td>C6: 8.5</td><td></td></tr><tr><td>Gated Recurrent Unit Convolutional Neural Network</td><td colspan="3">MAE</td><td>[32]</td></tr><tr><td rowspan="3">Classification</td><td>Condition-adaptive hidden semi-Markov model</td><td>C1: 0.95</td><td>C4: 1.02</td><td>C6: 0.84</td><td></td></tr><tr><td>TAKELM with binary differential evolution</td><td colspan="3">MAE</td><td>[29]</td></tr><tr><td>KELM with bandpass filter</td><td colspan="3">MAE of 3.52 * 10<sup>-6</sup> (for C6)</td><td>[30]</td></tr><tr><td rowspan="4">NASA</td><td rowspan="3">Regression</td><td>CNN- ResNet-50 and EfficientNet-B0</td><td colspan="3">Accuracy of 89% on 20 experiments</td><td>[42]</td></tr><tr><td>Condition-adaptive hidden semi-Markov model</td><td colspan="3">Accuracy of 100%</td><td>[52]</td></tr><tr><td>Neural Networks</td><td colspan="3">Accuracy (%)</td><td>[29]</td></tr><tr><td>Classification</td><td>1D-CNN</td><td>C1: 92.46</td><td>C4: 90.79</td><td>C6: 93.02</td><td></td></tr><tr><td></td><td></td><td>CNN- EfficientNet-B0</td><td colspan="3">Mean RMSE of about 0.10</td><td>[31]</td></tr><tr><td></td><td></td><td></td><td colspan="3">RMSE of 0.09</td><td>[33]</td></tr><tr><td></td><td></td><td></td><td colspan="3">Accuracy of 100%</td><td>[52]</td></tr></tbody></table>

## Conclusion and Future Work

In the last decades, more attention has been paid to the use of AE or sound sensors to monitor the milling process and the application of ML techniques to construct a TCM system. This system enables the prediction of cutting tool wear, allowing for the optimization of process parameters to increase tool life and identify optimal replacement times preventing, moreover, damage to the machined materials. In addition, the acquisition of sound signals has proven to be the cheapest compared to other types of signals. The conclusions that can be drawn from the analysis performed are:

- both approaches, regression and classification, were found to perform well for the TCM problem in the milling process;

- the most widely used and best-performing ML models for the problem under consideration are SVM and CNN.

Despite the presence of numerous works, many challenges remain. The works conducted were all performed in laboratories, including public benchmark datasets, and neglected real-world production challenges like background noise affecting sound signals. Another important limitation is due to the rather small number of observations within the datasets which considered few scenarios in terms of variation in the materials adopted for both tool and workpiece and machine input parameters for which different combinations can be set. Addressing these issues, intelligent datasets, comprising selected instances from larger sets, could offer a more comprehensive representation of wear process behaviors. Future progress should be based on the implementation of ML techniques in combination with reliable and inexpensive sensors, acquisition and
were carried out with the Matsuura Machining Center MC-510, cast iron and steel material as workpiece, and a 60mm face mill with six KC710 inserted tools. Various combinations of machine parameters were tested, including variable depth of cut, variable feed rate, and a constant cutting speed. Data sampled by three different types of sensors (acoustic emission, vibration, and current sensors) were acquired at several positions. PHM 2010 [67] is provided for the PHM Data Challenge, a competition open to all the participants of the 2010 PHM Society Conference. Three types of sensors (dynamometer, accelerometer, and acoustic emission) were used to capture relevant signals from high-speed CNC milling machine operations, in which a workpiece made of Inconel 718 and a 6mm ball nose tungsten carbide cutter were used. Constant input machine parameters are set during the experiments and three training sets (named C1, C4, and C6) are provided to construct the predictive model. Table 1 shows the results obtained by different research work, solving the proposed problems.

<table><caption>Table 1. Literature results for benchmark datasets</caption><thead><tr><th>Benchmark Dataset</th><th>Type problem</th><th>Decision-Making Models</th><th>Results</th><th>Study</th></tr></thead><tbody><tr><td rowspan="6">PHM 2010</td><td rowspan="3">Regression</td><td>Temporal Convolutional Network (TCN)</td><td>MAE<br>C1: 9.5 C4: 10.9 C6: 8.5</td><td>[28]</td></tr><tr><td>Gated Recurrent Unit Convolutional Neural Network</td><td>MAE<br>C1: 0.95 C4: 1.02 C6: 0.84</td><td>[32]</td></tr><tr><td>Condition-adaptive hidden semi-Markov model</td><td>MAE<br>C1: 6.25 C4: 17.73 C6: 18.90</td><td>[29]</td></tr><tr><td rowspan="3">Classification</td><td>TAKELM with binary differential evolution</td><td>MAE of 3.52 * 10<sup>-6</sup> (for C6)</td><td>[30]</td></tr><tr><td>KELM with bandpass filter</td><td>Accuracy of 89% on 20 experiments</td><td>[42]</td></tr><tr><td>CNN- ResNet-50 and EfficientNet-B0</td><td>Accuracy of 100%</td><td>[52]</td></tr><tr><td rowspan="4">NASA</td><td rowspan="2">Regression</td><td>Condition-adaptive hidden semi-Markov model</td><td>Accuracy (%)<br>C1: 92.46 C4: 90.79 C6: 93.02</td><td>[29]</td></tr><tr><td>Neural Networks</td><td>Mean RMSE of about 0.10</td><td>[31]</td></tr><tr><td rowspan="2">Classification</td><td>1D-CNN</td><td>RMSE of 0.09</td><td>[33]</td></tr><tr><td>CNN- EfficientNet-B0</td><td>Accuracy of 100%</td><td>[52]</td></tr></tbody></table>

## Conclusion and Future Work

In the last decades, more attention has been paid to the use of AE or sound sensors to monitor the milling process and the application of ML techniques to construct a TCM system. This system enables the prediction of cutting tool wear, allowing for the optimization of process parameters to increase tool life and identify optimal replacement times preventing, moreover, damage to the machined materials. In addition, the acquisition of sound signals has proven to be the cheapest compared to other types of signals. The conclusions that can be drawn from the analysis performed are:

- both approaches, regression and classification, were found to perform well for the TCM problem in the milling process;

- the most widely used and best-performing ML models for the problem under consideration are SVM and CNN.

Despite the presence of numerous works, many challenges remain. The works conducted were all performed in laboratories, including public benchmark datasets, and neglected real-world production challenges like background noise affecting sound signals. Another important limitation is due to the rather small number of observations within the datasets which considered few scenarios in terms of variation in the materials adopted for both tool and workpiece and machine input parameters for which different combinations can be set. Addressing these issues, intelligent datasets, comprising selected instances from larger sets, could offer a more comprehensive representation of wear process behaviors. Future progress should be based on the implementation of ML techniques in combination with reliable and inexpensive sensors, acquisition and
communication systems that are better suited to real production settings, facilitating more effective monitoring of instrument conditions in real-world environments.

## Acknowledgments

This work contributes to the basic research activities of the WP9.6: “AI for Green” supported by the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU

## References

[1] R. Liu, Z. Zhu, and Y. Zeng, “Multi-sensor Data Fusion and Feature Extraction for Cutting Tool Condition Monitoring: A Review,” in 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN), IEEE, Aug. 2022, pp. 588–593. https://doi.org/10.1109/ICICN56848.2022.10006480

[2] S. Selcuk, “Predictive maintenance, its implementation and latest trends,” Proc Inst Mech Eng B J Eng Manuf, vol. 231, no. 9, pp. 1670–1679, Jul. 2017. https://doi.org/10.1177/0954405415601640

[3] D. Yu. Pimenov, M. Kumar Gupta, L. R. R. da Silva, M. Kiran, N. Khanna, and G. M. Krolczyk, “Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach,” Measurement, vol. 199, p. 111503, Aug. 2022. https://doi.org/10.1016/j.measurement.2022.111503

[4] N. Ambhore, D. Kamble, S. Chinchanikar, and V. Wayal, “Tool Condition Monitoring System: A Review,” Mater Today Proc, vol. 2, no. 4–5, pp. 3419–3428, 2015. https://doi.org/10.1016/j.matpr.2015.07.317

[5] E. N. Diei and D. A. Dornfeld, “Acoustic Emission Sensing of Tool Wear in Face Milling,” Journal of Engineering for Industry, vol. 109, no. 3, pp. 234–240, Aug. 1987. https://doi.org/10.1115/1.3187124

[6] T. A. Carolan et al., “Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 1: Energy analysis,” Proc Inst Mech Eng B J Eng Manuf, vol. 211, no. 4, pp. 299–309, Apr. 1997. https://doi.org/10.1243/0954405971516275

[7] P. S. Pai and P. K. R. Rao, “Acoustic emission analysis for tool wear monitoring in face milling,” Int J Prod Res, vol. 40, no. 5, pp. 1081–1093, Jan. 2002. https://doi.org/10.1080/00207540110107534

[8] T. A. Carolan et al., “Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 2: Frequency analysis,” Proc Inst Mech Eng B J Eng Manuf, vol. 211, no. 4, pp. 311–319, Apr. 1997. https://doi.org/10.1243/0954405971516284

[9] P. Zheng et al., “Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives,” Frontiers of Mechanical Engineering, vol. 13, no. 2, pp. 137–150, Jun. 2018. https://doi.org/10.1007/s11465-018-0499-5

[10] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, “A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques,” Machines, vol. 9, no. 12, p. 351, Dec. 2021. https://doi.org/10.3390/machines9120351

[11] A. Mohamed, M. Hassan, R. M’Saoubi, and H. Attia, “Tool Condition Monitoring for High-Performance Machining Systems—A Review,” Sensors, vol. 22, no. 6, p. 2206, Mar. 2022. https://doi.org/10.3390/s22062206
communication systems that are better suited to real production settings, facilitating more effective monitoring of instrument conditions in real-world environments.

## Acknowledgments

This work contributes to the basic research activities of the WP9.6: "AI for Green" supported by the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU

## References

[1] R. Liu, Z. Zhu, and Y. Zeng, "Multi-sensor Data Fusion and Feature Extraction for Cutting Tool Condition Monitoring: A Review," in 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN), IEEE, Aug. 2022, pp. 588-593.
https://doi.org/10.1109/ICICN56848.2022.10006480

[2] S. Selcuk, "Predictive maintenance, its implementation and latest trends," Proc Inst Mech Eng B J Eng Manuf, vol. 231, no. 9, pp. 1670-1679, Jul. 2017.
https://doi.org/10.1177/0954405415601640

[3] D. Yu. Pimenov, M. Kumar Gupta, L. R. R. da Silva, M. Kiran, N. Khanna, and G. M. Krolczyk, "Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach," Measurement, vol. 199, p. 111503, Aug. 2022.
https://doi.org/10.1016/j.measurement.2022.111503

[4] N. Ambhore, D. Kamble, S. Chinchanikar, and V. Wayal, "Tool Condition Monitoring System: A Review," Mater Today Proc, vol. 2, no. 4-5, pp. 3419-3428, 2015.
https://doi.org/10.1016/j.matpr.2015.07.317

[5] E. N. Diei and D. A. Dornfeld, "Acoustic Emission Sensing of Tool Wear in Face Milling," Journal of Engineering for Industry, vol. 109, no. 3, pp. 234-240, Aug. 1987.
https://doi.org/10.1115/1.3187124

[6] T. A. Carolan et al., "Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 1: Energy analysis," Proc Inst Mech Eng B J Eng Manuf, vol. 211, no. 4, pp. 299-309, Apr. 1997.
https://doi.org/10.1243/0954405971516275

[7] P. S. Pai and P. K. R. Rao, "Acoustic emission analysis for tool wear monitoring in face milling," Int J Prod Res, vol. 40, no. 5, pp. 1081-1093, Jan. 2002.
https://doi.org/10.1080/00207540110107534

[8] T. A. Carolan et al., "Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 2: Frequency analysis," Proc Inst Mech Eng B J Eng Manuf, vol. 211, no. 4, pp. 311-319, Apr. 1997.
https://doi.org/10.1243/0954405971516284

[9] P. Zheng et al., "Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives," Frontiers of Mechanical Engineering, vol. 13, no. 2, pp. 137-150, Jun. 2018.
https://doi.org/10.1007/s11465-018-0499-5

[10] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, "A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques," Machines, vol. 9, no. 12, p. 351, Dec. 2021.
https://doi.org/10.3390/machines9120351

[11] A. Mohamed, M. Hassan, R. M'Saoubi, and H. Attia, "Tool Condition Monitoring for High-Performance Machining Systems—A Review," Sensors, vol. 22, no. 6, p. 2206, Mar. 2022.
https://doi.org/10.3390/s22062206
[12] T. Mohanraj, S. Shankar, R. Rajasekar, N. R. Sakthivel, and A. Pramanik, “Tool condition monitoring techniques in milling process — a review,” Journal of Materials Research and Technology, vol. 9, no. 1, pp. 1032–1042, Jan. 2020. https://doi.org/10.1016/j.jmrt.2019.10.031

[13] M. Iliyas Ahmad, Y. Yusof, M. E. Daud, K. Latiff, A. Z. Abdul Kadir, and Y. Saif, “Machine monitoring system: a decade in review,” The International Journal of Advanced Manufacturing Technology, vol. 108, no. 11–12, pp. 3645–3659, Jun. 2020. https://doi.org/10.1007/s00170-020-05620-3

[14] S. Sayyad, S. Kumar, A. Bongale, P. Kamat, S. Patil, and K. Kotecha, “Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions,” IEEE Access, vol. 9, pp. 110255–110286, 2021. https://doi.org/10.1109/ACCESS.2021.3101284

[15] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, “Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review,” J Intell Manuf, vol. 34, no. 5, pp. 2079–2121, Jun. 2023. https://doi.org/10.1007/s10845-022-01923-2

[16] I. P. Girsang and J. S. Dhupia, “Machine Tools for Machining,” in Handbook of Manufacturing Engineering and Technology, London: Springer London, 2015, pp. 811–865. doi: 10.1007/978-1-4471-4670-4_4

[17] M. Tolouei-Rad and I. M. Bidhendi, “On the optimization of machining parameters for milling operations,” Int J Mach Tools Manuf, vol. 37, no. 1, pp. 1–16, Jan. 1997. https://doi.org/10.1016/S0890-6955(96)00044-2

[18] K. Zhu, Y. S. Wong, and G. S. Hong, “Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results,” Int J Mach Tools Manuf, vol. 49, no. 7–8, pp. 537–553, Jun. 2009. https://doi.org/10.1016/j.ijmachtools.2009.02.003

[19] M. Hu, W. Ming, Q. An, and M. Chen, “Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method,” The International Journal of Advanced Manufacturing Technology, vol. 104, no. 9–12, pp. 4117–4128, Oct. 2019. https://doi.org/10.1007/s00170-019-04125-y

[20] M. Kuntoğlu et al., “A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends,” Sensors, vol. 21, no. 1, p. 108, Dec. 2020. https://doi.org/10.3390/s21010108

[21] P. Huo, M. Zhang, L. Gao, and R. Li, “On-Line Tool Condition Detection Based on Acoustic Signal,” 2014. [Online]. Available: www.ijastnet.com

[22] P. W. Prickett and C. Johns, “An overview of approaches to end milling tool monitoring,” Int J Mach Tools Manuf, vol. 39, no. 1, pp. 105–122, Jan. 1999. https://doi.org/10.1016/S0890-6955(98)00020-0

[23] A. Kothuru, S. P. Nooka, and R. Liu, “Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling,” The International Journal of Advanced Manufacturing Technology, vol. 95, no. 9–12, pp. 3797–3808, Apr. 2018. https://doi.org/10.1007/s00170-017-1460-1

[24] M. Shah, V. Vakharia, R. Chaudhari, J. Vora, D. Yu. Pimenov, and K. Giasin, “Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models,” The International Journal of Advanced Manufacturing Technology, vol. 121, no. 1–2, pp. 723–736, Jul. 2022. https://doi.org/10.1007/s00170-022-09356-0

[25] Y. Zhou, B. Sun, W. Sun, and Z. Lei, “Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process,” J Intell Manuf, vol. 33, no. 1, pp. 247–258, Jan. 2022. https://doi.org/10.1007/s10845-020-01663-1
[12] T. Mohanraj, S. Shankar, R. Rajasekar, N. R. Sakthivel, and A. Pramanik, “Tool condition monitoring techniques in milling process — a review,” Journal of Materials Research and Technology, vol. 9, no. 1, pp. 1032–1042, Jan. 2020. https://doi.org/10.1016/j.jmrt.2019.10.031

[13] M. Iliyas Ahmad, Y. Yusof, M. E. Daud, K. Latiff, A. Z. Abdul Kadir, and Y. Saif, “Machine monitoring system: a decade in review,” The International Journal of Advanced Manufacturing Technology, vol. 108, no. 11–12, pp. 3645–3659, Jun. 2020. https://doi.org/10.1007/s00170-020-05620-3

[14] S. Sayyad, S. Kumar, A. Bongale, P. Kamat, S. Patil, and K. Kotecha, “Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions,” IEEE Access, vol. 9, pp. 110255–110286, 2021. https://doi.org/10.1109/ACCESS.2021.3101284

[15] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, “Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review,” J Intell Manuf, vol. 34, no. 5, pp. 2079–2121, Jun. 2023. https://doi.org/10.1007/s10845-022-01923-2

[16] I. P. Girsang and J. S. Dhupia, “Machine Tools for Machining,” in Handbook of Manufacturing Engineering and Technology, London: Springer London, 2015, pp. 811–865. doi: 10.1007/978-1-4471-4670-4_4

[17] M. Tolouei-Rad and I. M. Bidhendi, “On the optimization of machining parameters for milling operations,” Int J Mach Tools Manuf, vol. 37, no. 1, pp. 1–16, Jan. 1997. https://doi.org/10.1016/S0890-6955(96)00044-2

[18] K. Zhu, Y. S. Wong, and G. S. Hong, “Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results,” Int J Mach Tools Manuf, vol. 49, no. 7–8, pp. 537–553, Jun. 2009. https://doi.org/10.1016/j.ijmachtools.2009.02.003

[19] M. Hu, W. Ming, Q. An, and M. Chen, “Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method,” The International Journal of Advanced Manufacturing Technology, vol. 104, no. 9–12, pp. 4117–4128, Oct. 2019. https://doi.org/10.1007/s00170-019-04125-y

[20] M. Kuntoğlu et al., “A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends,” Sensors, vol. 21, no. 1, p. 108, Dec. 2020. https://doi.org/10.3390/s21010108

[21] P. Huo, M. Zhang, L. Gao, and R. Li, “On-Line Tool Condition Detection Based on Acoustic Signal,” 2014. [Online]. Available: www.ijastnet.com

[22] P. W. Prickett and C. Johns, “An overview of approaches to end milling tool monitoring,” Int J Mach Tools Manuf, vol. 39, no. 1, pp. 105–122, Jan. 1999. https://doi.org/10.1016/S0890-6955(98)00020-0

[23] A. Kothuru, S. P. Nooka, and R. Liu, “Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling,” The International Journal of Advanced Manufacturing Technology, vol. 95, no. 9–12, pp. 3797–3808, Apr. 2018. https://doi.org/10.1007/s00170-017-1460-1

[24] M. Shah, V. Vakharia, R. Chaudhari, J. Vora, D. Yu. Pimenov, and K. Giasin, “Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models,” The International Journal of Advanced Manufacturing Technology, vol. 121, no. 1–2, pp. 723–736, Jul. 2022. https://doi.org/10.1007/s00170-022-09356-0

[25] Y. Zhou, B. Sun, W. Sun, and Z. Lei, “Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process,” J Intell Manuf, vol. 33, no. 1, pp. 247–258, Jan. 2022. https://doi.org/10.1007/s10845-020-01663-1
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[29] S. Yan, L. Sui, S. Wang, and Y. Sun, “On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM),” Mech Syst Signal Process, vol. 200, p. 110644, Oct. 2023. https://doi.org/10.1016/j.ymssp.2023.110644

[30] Y. Zhou, B. Sun, and W. Sun, “A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling,” Measurement, vol. 166, p. 108186, Dec. 2020. https://doi.org/10.1016/j.measurement.2020.108186

[31] E. Traini, G. Bruno, and F. Lombardi, “Tool condition monitoring framework for predictive maintenance: a case study on milling process,” Int J Prod Res, vol. 59, no. 23, pp. 7179–7193, Dec. 2021. https://doi.org/10.1080/00207543.2020.1836419

[32] Z. Chaowen, J. Jing, and C. chi, “Research On Tool Wear Monitoring Based On GRU-CNN,” in 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), IEEE, Apr. 2021, pp. 729–733. https://doi.org/10.1109/ICSP51882.2021.9408717

[33] P.-H. Kuo, C.-Y. Lin, P.-C. Luan, and H.-T. Yau, “Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear,” IEEE Sens J, vol. 22, no. 21, pp. 20257–20267, Nov. 2022. https://doi.org/10.1109/JSEN.2022.3206308

[34] M. Ferguson, R. Bhinge, J. Park, Y. T. Lee, and K. H. Law, “A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data,” Smart Sustain Manuf Syst, vol. 2, no. 1, p. 20180019, Jan. 2019. https://doi.org/10.1520/SSMS20180019

[35] M. Ferguson, K. H. Law, R. Bhinge, and Y.-T. T. Lee, “A Generalized Method for Featurization of Manufacturing Signals, With Application to Tool Condition Monitoring,” in Volume 1: 37th Computers and Information in Engineering Conference, American Society of Mechanical Engineers, Aug. 2017. https://doi.org/10.1115/DETC2017-67987

[36] S. Shankar, T. Mohanraj, and R. Rajasekar, “Prediction of cutting tool wear during milling process using artificial intelligence techniques,” Int J Comput Integr Manuf, vol. 32, no. 2, pp. 174–182, Feb. 2019. https://doi.org/10.1080/0951192X.2018.1550681

[37] C. Cooper et al., “Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,” Procedia Manuf, vol. 49, pp. 105–111, 2020. https://doi.org/10.1016/j.promfg.2020.07.004

[38] P. Twardowski, M. Tabaszewski, M. Wiciak – Pikuła, and A. Felusiak-Czyryca, “Identification of tool wear using acoustic emission signal and machine learning methods,” Precis Eng, vol. 72, pp. 738–744, Nov. 2021. https://doi.org/10.1016/j.precisioneng.2021.07.019

[39] J. Yuan, L. Li, H. Shao, M. Han, and H. Huang, “Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients,” J Manuf Process, vol. 98, pp. 67–79, Jul. 2023. https://doi.org/10.1016/j.jmapro.2023.05.023

[40] T. McLeay, M. S. Turner, and K. Worden, “A novel approach to machining process fault detection using unsupervised learning,” Proc Inst Mech Eng B J Eng Manuf, vol. 235, no. 10, pp. 1533–1542, Aug. 2021. https://doi.org/10.1177/0954405420937556
[26] P. J. G. Nieto, E. García-Gonzalo, J. A. V. Vilán, and A. S. Robleda, “Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data,” 2015, p. 190021. doi: 10.1063/1.4938988

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[28] M. Van Herreweghe, M. Verbeke, W. Meert, and T. Jacobs, “A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes,” 2020, pp. 414–425. https://doi.org/10.1007/978-3-030-43887-6_34

[29] S. Yan, L. Sui, S. Wang, and Y. Sun, “On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM),” Mech Syst Signal Process, vol. 200, p. 110644, Oct. 2023. https://doi.org/10.1016/j.ymssp.2023.110644

[30] Y. Zhou, B. Sun, and W. Sun, “A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling,” Measurement, vol. 166, p. 108186, Dec. 2020. https://doi.org/10.1016/j.measurement.2020.108186

[31] E. Traini, G. Bruno, and F. Lombardi, “Tool condition monitoring framework for predictive maintenance: a case study on milling process,” Int J Prod Res, vol. 59, no. 23, pp. 7179–7193, Dec. 2021. https://doi.org/10.1080/00207543.2020.1836419

[32] Z. Chaowen, J. Jing, and C. chi, “Research On Tool Wear Monitoring Based On GRU-CNN,” in 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), IEEE, Apr. 2021, pp. 729–733. https://doi.org/10.1109/ICSP51882.2021.9408717

[33] P.-H. Kuo, C.-Y. Lin, P.-C. Luan, and H.-T. Yau, “Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear,” IEEE Sens J, vol. 22, no. 21, pp. 20257–20267, Nov. 2022. https://doi.org/10.1109/JSEN.2022.3206308

[34] M. Ferguson, R. Bhinge, J. Park, Y. T. Lee, and K. H. Law, “A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data,” Smart Sustain Manuf Syst, vol. 2, no. 1, p. 20180019, Jan. 2019. https://doi.org/10.1520/SSMS20180019

[35] M. Ferguson, K. H. Law, R. Bhinge, and Y.-T. T. Lee, “A Generalized Method for Featurization of Manufacturing Signals, With Application to Tool Condition Monitoring,” in Volume 1: 37th Computers and Information in Engineering Conference, American Society of Mechanical Engineers, Aug. 2017. https://doi.org/10.1115/DETC2017-67987

[36] S. Shankar, T. Mohanraj, and R. Rajasekar, “Prediction of cutting tool wear during milling process using artificial intelligence techniques,” Int J Comput Integr Manuf, vol. 32, no. 2, pp. 174–182, Feb. 2019. https://doi.org/10.1080/0951192X.2018.1550681

[37] C. Cooper et al., “Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,” Procedia Manuf, vol. 49, pp. 105–111, 2020. https://doi.org/10.1016/j.promfg.2020.07.004

[38] P. Twardowski, M. Tabaszewski, M. Wiciak – Pikuła, and A. Felusiak-Czyryca, “Identification of tool wear using acoustic emission signal and machine learning methods,” Precis Eng, vol. 72, pp. 738–744, Nov. 2021. https://doi.org/10.1016/j.precisioneng.2021.07.019

[39] J. Yuan, L. Li, H. Shao, M. Han, and H. Huang, “Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients,” J Manuf Process, vol. 98, pp. 67–79, Jul. 2023. https://doi.org/10.1016/j.jmapro.2023.05.023

[40] T. McLeay, M. S. Turner, and K. Worden, “A novel approach to machining process fault detection using unsupervised learning,” Proc Inst Mech Eng B J Eng Manuf, vol. 235, no. 10, pp. 1533–1542, Aug. 2021. https://doi.org/10.1177/0954405420937556
[41] L. Zheng, Y. Jiang, Y. Zhang, and J. Guo, "Tool Wear Prediction with External Signals Based on Lightweight Deep Learning Model," in 2020 Chinese Automation Congress (CAC), IEEE, Nov. 2020, pp. 5311-5315. https://doi.org/10.1109/CAC51589.2020.9327649

[42] Z. Lei, Y. Zhou, and X. Zhang, "A Classification of Milling TCM Based on Bandpass Filter and Kernel Extreme Learning Machine," in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), IEEE, Oct. 2018, pp. 176-179. https://doi.org/10.1109/PHM-Chongqing.2018.00036

[43] M. C. Gomes, L. C. Brito, M. Bacci da Silva, and M. A. Viana Duarte, "Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors," Precis Eng, vol. 67, pp. 137-151, Jan. 2021. https://doi.org/10.1016/j.precisioneng.2020.09.025

[44] A. Kothuru, S. P. Nooka, P. I. Victoria, and R. Liu, "Application of audible sound signals for tool wear monitoring and workpiece hardness identification in gear milling using machine learning techniques," in Proceedings of the ASME Design Engineering Technical Conference, 2017. https://doi.org/10.1115/DETC201768067

[45] S. Ravikumar and K. I. Ramachandran, "Tool Wear Monitoring of Multipoint Cutting Tool using Sound Signal Features Signals with Machine Learning Techniques," Mater Today Proc, vol. 5, no. 11, pp. 25720-25729, 2018. https://doi.org/10.1016/j.matpr.2018.11.014

[46] P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran, "Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach," Int J Comput Intell Appl, vol. 17, no. 03, p. 1850017, Sep. 2018. https://doi.org/10.1142/S1469026818500177

[47] M. Ahmed et al., "Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model," Sensors, vol. 23, no. 6, p. 3084, Mar. 2023. https://doi.org/10.3390/s23063084

[48] P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran, "Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features," Intelligent Decision Technologies, vol. 12, no. 2, pp. 265-282, Mar. 2018. https://doi.org/10.3233/IDT-180332

[49] I.-C. Sun, R.-C. Cheng, and K.-S. Chen, "Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis," The International Journal of Advanced Manufacturing Technology, vol. 119, no. 9-10, pp. 6451-6468, Apr. 2022. https://doi.org/10.1007/s00170-021-08526-w

[50] A. Schueller and C. Saldaña, "Generalizability analysis of tool condition monitoring ensemble machine learning models," J Manuf Process, vol. 84, pp. 1064-1075, Dec. 2022. https://doi.org/10.1016/j.jmapro.2022.10.064

[51] A. Schueller and C. Saldaña, "Indirect Tool Condition Monitoring Using Ensemble Machine Learning Techniques," J Manuf Sci Eng, vol. 145, no. 1, Jan. 2023. https://doi.org/10.1115/1.4055822

[52] Y. E. Karabacak, "Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis," Eksploatacja i Niezawodność - Maintenance and Reliability, vol. 25, no. 3, Jun. 2023. https://doi.org/10.17531/ein/168082

[53] M. Chen, M. Li, L. Zhao, and J. Liu, "Tool wear monitoring based on the combination of machine vision and acoustic emission," The International Journal of Advanced Manufacturing Technology, vol. 125, no. 7-8, pp. 3881-3897, Apr. 2023. https://doi.org/10.1007/s00170-023-11017-9

[54] F. Khan, K. Kamal, T. A. H. Ratlamwala, M. Alkahtani, M. Almatani, and S. Mathavan, "Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term
[41] L. Zheng, Y. Jiang, Y. Zhang, and J. Guo, "Tool Wear Prediction with External Signals Based on Lightweight Deep Learning Model," in 2020 Chinese Automation Congress (CAC), IEEE, Nov. 2020, pp. 5311-5315. https://doi.org/10.1109/CAC51589.2020.9327649

[42] Z. Lei, Y. Zhou, and X. Zhang, "A Classification of Milling TCM Based on Bandpass Filter and Kernel Extreme Learning Machine," in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), IEEE, Oct. 2018, pp. 176-179. https://doi.org/10.1109/PHM-Chongqing.2018.00036

[43] M. C. Gomes, L. C. Brito, M. Bacci da Silva, and M. A. Viana Duarte, "Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors," Precis Eng, vol. 67, pp. 137-151, Jan. 2021. https://doi.org/10.1016/j.precisioneng.2020.09.025

[44] A. Kothuru, S. P. Nooka, P. I. Victoria, and R. Liu, "Application of audible sound signals for tool wear monitoring and workpiece hardness identification in gear milling using machine learning techniques," in Proceedings of the ASME Design Engineering Technical Conference, 2017. https://doi.org/10.1115/DETC201768067

[45] S. Ravikumar and K. I. Ramachandran, "Tool Wear Monitoring of Multipoint Cutting Tool using Sound Signal Features Signals with Machine Learning Techniques," Mater Today Proc, vol. 5, no. 11, pp. 25720-25729, 2018. https://doi.org/10.1016/j.matpr.2018.11.014

[46] P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran, "Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach," Int J Comput Intell Appl, vol. 17, no. 03, p. 1850017, Sep. 2018. https://doi.org/10.1142/S1469026818500177

[47] M. Ahmed et al., "Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model," Sensors, vol. 23, no. 6, p. 3084, Mar. 2023. https://doi.org/10.3390/s23063084

[48] P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran, "Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features," Intelligent Decision Technologies, vol. 12, no. 2, pp. 265-282, Mar. 2018. https://doi.org/10.3233/IDT-180332

[49] I.-C. Sun, R.-C. Cheng, and K.-S. Chen, "Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis," The International Journal of Advanced Manufacturing Technology, vol. 119, no. 9-10, pp. 6451-6468, Apr. 2022. https://doi.org/10.1007/s00170-021-08526-w

[50] A. Schueller and C. Saldaña, "Generalizability analysis of tool condition monitoring ensemble machine learning models," J Manuf Process, vol. 84, pp. 1064-1075, Dec. 2022. https://doi.org/10.1016/j.jmapro.2022.10.064

[51] A. Schueller and C. Saldaña, "Indirect Tool Condition Monitoring Using Ensemble Machine Learning Techniques," J Manuf Sci Eng, vol. 145, no. 1, Jan. 2023. https://doi.org/10.1115/1.4055822

[52] Y. E. Karabacak, "Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis," Eksploatacja i Niezawodność - Maintenance and Reliability, vol. 25, no. 3, Jun. 2023. https://doi.org/10.17531/ein/168082

[53] M. Chen, M. Li, L. Zhao, and J. Liu, "Tool wear monitoring based on the combination of machine vision and acoustic emission," The International Journal of Advanced Manufacturing Technology, vol. 125, no. 7-8, pp. 3881-3897, Apr. 2023. https://doi.org/10.1007/s00170-023-11017-9

[54] F. Khan, K. Kamal, T. A. H. Ratlamwala, M. Alkahtani, M. Almatani, and S. Mathavan, "Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term
Memory Networks: A Deep Learning Approach,” IEEE Access, vol. 11, pp. 126611–126633, 2023. https://doi.org/10.1109/ACCESS.2023.3328582

[55] X. Chuangwen, D. Jianming, C. Yuzhen, L. Huaiyuan, S. Zhicheng, and X. Jing, “The relationships between cutting parameters, tool wear, cutting force and vibration,” Advances in Mechanical Engineering, vol. 10, no. 1, p. 168781401775043, Jan. 2018. https://doi.org/10.1177/1687814017750434

[56] Z. Li, R. Liu, and D. Wu, “Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning,” J Manuf Process, vol. 48, pp. 66–76, Dec. 2019. https://doi.org/10.1016/j.jmapro.2019.10.020

[57] S. Natarajan, M. Thangamuthu, S. Gnanasekaran, and J. Rakkiyannan, “Digital Twin-Driven Tool Condition Monitoring for the Milling Process,” Sensors, vol. 23, no. 12, p. 5431, Jun. 2023. https://doi.org/10.3390/s23125431

[58] Z. Zhu, R. Liu, and Y. Zeng, “Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network,” Engineering Research Express, vol. 5, no. 1, p. 015054, Mar. 2023. https://doi.org/10.1088/2631-8695/acbfa6

[59] A. Kothuru, S. P. Nooka, and R. Liu, “Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling,” in Volume 3: Manufacturing Equipment and Systems, American Society of Mechanical Engineers, Jun. 2017. https://doi.org/10.1115/MSEC2017-3069

[60] A. Kothuru, S. P. Nooka, and R. Liu, “Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks,” in Volume 4: Processes, American Society of Mechanical Engineers, Jun. 2018. https://doi.org/10.1115/MSEC2018-6680

[61] P. F. Suawa and M. Hubner, “Health Monitoring of Milling Tools under Distinct Operating Conditions by a Deep Convolutional Neural Network model,” in 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, Mar. 2022, pp. 1107–1110. https://doi.org/10.23919/DATE54114.2022.9774570

[62] C. K. Madhusudana, H. Kumar, and S. Narendranath, “Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal,” Mater Today Proc, vol. 5, no. 5, pp. 12035–12044, 2018. https://doi.org/10.1016/j.matpr.2018.02.178

[63] A. Kothuru, S. P. Nooka, and R. Liu, “Application of deep visualization in CNN-based tool condition monitoring for end milling,” Procedia Manuf, vol. 34, pp. 995–1004, 2019. https://doi.org/10.1016/j.promfg.2019.06.096

[64] Z. Jan et al., “Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities,” Expert Syst Appl, vol. 216, p. 119456, Apr. 2023. https://doi.org/10.1016/j.eswa.2022.119456

[65] Z. He, T. Shi, and J. Xuan, “Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders,” Measurement, vol. 190, p. 110719, Feb. 2022. https://doi.org/10.1016/j.measurement.2022.110719

[66] A. Agogino and K. Goebel, “BEST lab, UC Berkeley. "Milling Data Set ",” NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. [Online]. Available: http://ti.arc.nasa.gov/project/prognostic-data-repository

[67] Information on: https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge/
Memory Networks: A Deep Learning Approach,” IEEE Access, vol. 11, pp. 126611–126633, 2023. https://doi.org/10.1109/ACCESS.2023.3328582

[55] X. Chuangwen, D. Jianming, C. Yuzhen, L. Huaiyuan, S. Zhicheng, and X. Jing, “The relationships between cutting parameters, tool wear, cutting force and vibration,” Advances in Mechanical Engineering, vol. 10, no. 1, p. 168781401775043, Jan. 2018. https://doi.org/10.1177/1687814017750434

[56] Z. Li, R. Liu, and D. Wu, “Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning,” J Manuf Process, vol. 48, pp. 66–76, Dec. 2019. https://doi.org/10.1016/j.jmapro.2019.10.020

[57] S. Natarajan, M. Thangamuthu, S. Gnanasekaran, and J. Rakkiyannan, “Digital Twin-Driven Tool Condition Monitoring for the Milling Process,” Sensors, vol. 23, no. 12, p. 5431, Jun. 2023. https://doi.org/10.3390/s23125431

[58] Z. Zhu, R. Liu, and Y. Zeng, “Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network,” Engineering Research Express, vol. 5, no. 1, p. 015054, Mar. 2023. https://doi.org/10.1088/2631-8695/acbfa6

[59] A. Kothuru, S. P. Nooka, and R. Liu, “Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling,” in Volume 3: Manufacturing Equipment and Systems, American Society of Mechanical Engineers, Jun. 2017. https://doi.org/10.1115/MSEC2017-3069

[60] A. Kothuru, S. P. Nooka, and R. Liu, “Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks,” in Volume 4: Processes, American Society of Mechanical Engineers, Jun. 2018. https://doi.org/10.1115/MSEC2018-6680

[61] P. F. Suawa and M. Hubner, “Health Monitoring of Milling Tools under Distinct Operating Conditions by a Deep Convolutional Neural Network model,” in 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, Mar. 2022, pp. 1107–1110. https://doi.org/10.23919/DATE54114.2022.9774570

[62] C. K. Madhusudana, H. Kumar, and S. Narendranath, “Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal,” Mater Today Proc, vol. 5, no. 5, pp. 12035–12044, 2018. https://doi.org/10.1016/j.matpr.2018.02.178

[63] A. Kothuru, S. P. Nooka, and R. Liu, “Application of deep visualization in CNN-based tool condition monitoring for end milling,” Procedia Manuf, vol. 34, pp. 995–1004, 2019. https://doi.org/10.1016/j.promfg.2019.06.096

[64] Z. Jan et al., “Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities,” Expert Syst Appl, vol. 216, p. 119456, Apr. 2023. https://doi.org/10.1016/j.eswa.2022.119456

[65] Z. He, T. Shi, and J. Xuan, “Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders,” Measurement, vol. 190, p. 110719, Feb. 2022. https://doi.org/10.1016/j.measurement.2022.110719

[66] A. Agogino and K. Goebel, “BEST lab, UC Berkeley. "Milling Data Set ",” NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. [Online]. Available: http://ti.arc.nasa.gov/project/prognostic-data-repository

[67] Information on: https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge/
# Tool Condition Monitoring in machining for the workpiece surface quality evaluation

DEL PRETE Antonio<fnref n="1" />,ª, NYBORG Lars<fnref n="2" />,ᵇ, FRANCHI Rodolfo<fnref n="1" />,ᶜ* and PRIMO Teresa<fnref n="1" />,ᵈ

**Keywords:** Tool Condition Monitoring, Machining, Surface Quality

**Abstract.** Achieving high surface quality is crucial in manufacturing, impacting product functionality and appearance. Poor quality can lead to defects, friction, and safety risks. Cutting tools endure harsh conditions and wear over time, affecting surface quality and increasing costs. Monitoring tool condition is vital for efficiency, reducing cycle times and downtime. Industries like aerospace and automotive require tight quality control for meeting standards. Historically, manual inspections and scheduled changes were used, but advanced technology now allows more efficient tool condition monitoring. The paper outlines a tool condition monitoring approach using sensors and machine learning to predict and classify tool conditions and workpiece surface quality. It integrates acoustic emission, accelerometer, and thermal infrared camera sensors into a lathe machine. Various machine learning algorithms are trained and validated to accurately predict tool and surface conditions. The most effective model is identified and presented.

**Introduction**

The adoption of smart production in Industry 4.0 offers a range of tangible benefits, including cost reduction, improved operational efficiency, and enhanced product quality [1]. This approach enables the creation of flexible and adaptable production systems capable of responding in real-time to changing market demands [2]. Process monitoring through the use of sensors, data analytics, and machine learning algorithms allows for the timely detection and resolution of defects and issues, optimizing production [3].

Surface roughness in machining is influenced by various factors, including cutting speed, feed rate, depth of cut, and tool wear [4]. Sensors such as acoustic emission, accelerometers, and infrared devices are employed to monitor tool conditions and the quality of the machined surface, contributing to process optimization [5] [6].

Analysis of statistical characteristics of sensor signals enables assessment of tool health, with several studies exploring correlations between sensor data and tool wear [7] [8]. Machine learning techniques also represent a step forward in improving tool condition monitoring systems [9].

Timely detection of tool wear through sensor monitoring allows for prompt intervention, minimizing the impact on efficiency and quality of the machining process [10]. However, there remains a knowledge gap regarding the correlation between roughness, temperature, and tool wear based on signal characteristics [11]. Consistently with the context above described, this paper presents the development of a system for TCM implementing different kind of sensors for the quality process control. The set of sensors used for the scope consists of an accelerometer (Acc), an AE sensor and an infrared thermal camera (IRTC). Thus, the results of a preliminary experimental activity having as objective data acquisition for tool wear and surface quality modelling are here presented. Different cutting speed values were considered, under fixed feed

<figure><img src="image_1896.png" /></figure>

<footnote n="1">¹Via per Monteroni, Building “O”, Ecotekne Campus, 73100 Lecce, Italy</footnote>

<footnote n="2">²Chalmers University of Technology, 412 96 Gothenburg, Sweeden</footnote>

<footnote>ªantonio.delprete@unisalento.it, ᵇlars.nyborg@chalmers.se, ᶜrodolfo.franchi@unisalento.it,</footnote>

<footnote>ᵈteresa.primo@unisalento.it</footnote>
# Tool Condition Monitoring in machining for the workpiece surface quality evaluation

DEL PRETE Antonio<fnref n="1" />,ª, NYBORG Lars<fnref n="2" />,ᵇ, FRANCHI Rodolfo<fnref n="1" />,ᶜ* and PRIMO Teresa<fnref n="1" />,ᵈ

**Keywords:** Tool Condition Monitoring, Machining, Surface Quality

**Abstract.** Achieving high surface quality is crucial in manufacturing, impacting product functionality and appearance. Poor quality can lead to defects, friction, and safety risks. Cutting tools endure harsh conditions and wear over time, affecting surface quality and increasing costs. Monitoring tool condition is vital for efficiency, reducing cycle times and downtime. Industries like aerospace and automotive require tight quality control for meeting standards. Historically, manual inspections and scheduled changes were used, but advanced technology now allows more efficient tool condition monitoring. The paper outlines a tool condition monitoring approach using sensors and machine learning to predict and classify tool conditions and workpiece surface quality. It integrates acoustic emission, accelerometer, and thermal infrared camera sensors into a lathe machine. Various machine learning algorithms are trained and validated to accurately predict tool and surface conditions. The most effective model is identified and presented.

**Introduction**

The adoption of smart production in Industry 4.0 offers a range of tangible benefits, including cost reduction, improved operational efficiency, and enhanced product quality [1]. This approach enables the creation of flexible and adaptable production systems capable of responding in real-time to changing market demands [2]. Process monitoring through the use of sensors, data analytics, and machine learning algorithms allows for the timely detection and resolution of defects and issues, optimizing production [3].

Surface roughness in machining is influenced by various factors, including cutting speed, feed rate, depth of cut, and tool wear [4]. Sensors such as acoustic emission, accelerometers, and infrared devices are employed to monitor tool conditions and the quality of the machined surface, contributing to process optimization [5] [6].

Analysis of statistical characteristics of sensor signals enables assessment of tool health, with several studies exploring correlations between sensor data and tool wear [7] [8]. Machine learning techniques also represent a step forward in improving tool condition monitoring systems [9].

Timely detection of tool wear through sensor monitoring allows for prompt intervention, minimizing the impact on efficiency and quality of the machining process [10]. However, there remains a knowledge gap regarding the correlation between roughness, temperature, and tool wear based on signal characteristics [11]. Consistently with the context above described, this paper presents the development of a system for TCM implementing different kind of sensors for the quality process control. The set of sensors used for the scope consists of an accelerometer (Acc), an AE sensor and an infrared thermal camera (IRTC). Thus, the results of a preliminary experimental activity having as objective data acquisition for tool wear and surface quality modelling are here presented. Different cutting speed values were considered, under fixed feed

<figure><img src="image_1891.png" /></figure>

<footnote n="1">¹Via per Monteroni, Building “O”, Ecotekne Campus, 73100 Lecce, Italy</footnote>

<footnote n="2">²Chalmers University of Technology, 412 96 Gothenburg, Sweeden</footnote>

<footnote>ªantonio.delprete@unisalento.it, ᵇlars.nyborg@chalmers.se, ᶜrodolfo.franchi@unisalento.it,</footnote>

<footnote>ᵈteresa.primo@unisalento.it</footnote>
rate and depth of cut. In these conditions, flank wear Vb, surface roughness Ra and temperature T at the tool-chip interface were monitored at different time steps. The signals of the Acc and AE sensors were adequately windowed and filtered for the noise reduction, and a set of features were from them extracted. A Principal Component Analysis (PCA) was performed in order to reduce the problem dimensionality. Then, different regression Machine Learning models for Vb, Ra and T were developed and evaluated, highlighting the strong correlation between Ra, Vb and T. A practical case of the methodology utilization is presented, demonstrating the high potential of the methodology application in the specific turning process. Finally, the best performing model is presented.

## Materials and methods

Fig.1 shows a schematic representation of the developed TCM methodology workflow; the followings phases were adequately considered:

**Data Collection:** Gathering of relevant data from the machining process, including information about tool condition and associated features such as vibration data, acoustic emissions, temperature, cutting forces, and machining parameters.

**Data Preprocessing:** Preparing and cleaning the data, involving handling missing values, noise reduction, and normalization.

**Data Augmentation and Splitting:** Creating new training examples by making various transformations or modifications to existing data; splitting of the dataset into training, validation, and testing sets for machine learning models setup.

**Feature Selection:** Performing feature selection to identify the most important features for the model.

**Model Selection:** Choosing appropriate machine learning algorithms or models for the specific problem.

**Model Training:** Training the selected machine learning models using the training dataset. Fine-tune hyperparameters through cross-validation on the validation set to optimize the model's performance.

**Model Evaluation:** Assessing the models' performance on the test dataset using appropriate evaluation metrics.

<figure><img src="image_1897.png" /><figcaption>Fig.1. Adopted TCM methodology.</figcaption></figure>

To gather relevant data from the machining process, machining turning tests were performed. The material under investigation is the S235jr steel, complying with the EN 10025-2 standard; Table 1 reports its chemical composition.
rate and depth of cut. In these conditions, flank wear Vb, surface roughness Ra and temperature T at the tool-chip interface were monitored at different time steps. The signals of the Acc and AE sensors were adequately windowed and filtered for the noise reduction, and a set of features were from them extracted. A Principal Component Analysis (PCA) was performed in order to reduce the problem dimensionality. Then, different regression Machine Learning models for Vb, Ra and T were developed and evaluated, highlighting the strong correlation between Ra, Vb and T. A practical case of the methodology utilization is presented, demonstrating the high potential of the methodology application in the specific turning process. Finally, the best performing model is presented.

## Materials and methods

Fig.1 shows a schematic representation of the developed TCM methodology workflow; the followings phases were adequately considered:

**Data Collection:** Gathering of relevant data from the machining process, including information about tool condition and associated features such as vibration data, acoustic emissions, temperature, cutting forces, and machining parameters.

**Data Preprocessing:** Preparing and cleaning the data, involving handling missing values, noise reduction, and normalization.

**Data Augmentation and Splitting:** Creating new training examples by making various transformations or modifications to existing data; splitting of the dataset into training, validation, and testing sets for machine learning models setup.

**Feature Selection:** Performing feature selection to identify the most important features for the model.

**Model Selection:** Choosing appropriate machine learning algorithms or models for the specific problem.

**Model Training:** Training the selected machine learning models using the training dataset. Fine-tune hyperparameters through cross-validation on the validation set to optimize the model's performance.

**Model Evaluation:** Assessing the models' performance on the test dataset using appropriate evaluation metrics.

<figure><img src="image_1892.png" /><figcaption>Fig.1. Adopted TCM methodology.</figcaption></figure>

To gather relevant data from the machining process, machining turning tests were performed. The material under investigation is the S235jr steel, complying with the EN 10025-2 standard; Table 1 reports its chemical composition.
<table><caption>Table 1. Chemical composition of the S235JR steel [12].</caption><thead><tr><td>Element</td><td>%weight</td></tr></thead><tbody><tr><td>Carbon, C</td><td>0.17</td></tr><tr><td>Phosphorus, P</td><td>0.035</td></tr><tr><td>Silicon, Si</td><td>0.045</td></tr><tr><td>Manganese, Mn</td><td>1.4</td></tr><tr><td>Nitrogen, N</td><td>0.012</td></tr><tr><td>Copper, Cu</td><td>0.55</td></tr></tbody></table>

Machining tests were performed on bars having a diameter equal to 35 mm and an overhang equal to 100 mm. The adopted lathe machine is a SOGI M2C-450. The TCM methodology was carried out implementing on the lathe machine the following experimental setup:

* An accelerometer for vibration detection (8763b Kistler, Switzerland)

* An acoustic emission sensor (8152C Kistler, Switzerland)

* An infrared thermo-camera (Optris Xi 410).

The software Elsys Tranax 4.0 was used for the Acc and AE signals acquisition; for the thermal imaging, the software Optrix Pix Connect was used.

Fig. 2 reports the experimental setup.

<figure><img src="image_1898.png" /><figcaption>Fig. 2. Experimental setup for turning.</figcaption></figure>

As shown in the figure, the Acc and the AE sensors were fixed near the tool holder. The IRTC was mounted on a rigid bracket and placed, also, near the tool holder.

Thus, it was possible to keep the focus directly on the cutting zone following the movement of the tool during the entire process duration. Then, during the experimental tests, the signal acquired by IRTC indicated the thermal gradient into the cutting zone.

The IRTC emissivity was calibrated in the temperature range 25°C-250°C; method chosen by authors involved heating up by cutting to a known temperature, measured with use of noncontact thermometer, and then measuring the target temperature with the infrared camera. The next step was changing the emissivity until the temperature corresponds to that of the thermometer. Emissivity value determined this way would be use for all future measurements.

Further, for the tool wear measurements (complying with the ISO-3685 standard) and the roughness Ra evaluations (complying with the ISO-1997 standard) the following experimental equipment were adopted:
<table><caption>Table 1. Chemical composition of the S235JR steel [12].</caption><thead><tr><td>Element</td><td>%weight</td></tr></thead><tbody><tr><td>Carbon, C</td><td>0.17</td></tr><tr><td>Phosphorus, P</td><td>0.035</td></tr><tr><td>Silicon, Si</td><td>0.045</td></tr><tr><td>Manganese, Mn</td><td>1.4</td></tr><tr><td>Nitrogen, N</td><td>0.012</td></tr><tr><td>Copper, Cu</td><td>0.55</td></tr></tbody></table>

Machining tests were performed on bars having a diameter equal to 35 mm and an overhang equal to 100 mm. The adopted lathe machine is a SOGI M2C-450. The TCM methodology was carried out implementing on the lathe machine the following experimental setup:

* An accelerometer for vibration detection (8763b Kistler, Switzerland)

* An acoustic emission sensor (8152C Kistler, Switzerland)

* An infrared thermo-camera (Optris Xi 410).

The software Elsys Tranax 4.0 was used for the Acc and AE signals acquisition; for the thermal imaging, the software Optrix Pix Connect was used.

Fig. 2 reports the experimental setup.

<figure><img src="image_1893.png" /><figcaption>Fig. 2. Experimental setup for turning.</figcaption></figure>

As shown in the figure, the Acc and the AE sensors were fixed near the tool holder. The IRTC was mounted on a rigid bracket and placed, also, near the tool holder.

Thus, it was possible to keep the focus directly on the cutting zone following the movement of the tool during the entire process duration. Then, during the experimental tests, the signal acquired by IRTC indicated the thermal gradient into the cutting zone.

The IRTC emissivity was calibrated in the temperature range 25°C-250°C; method chosen by authors involved heating up by cutting to a known temperature, measured with use of noncontact thermometer, and then measuring the target temperature with the infrared camera. The next step was changing the emissivity until the temperature corresponds to that of the thermometer. Emissivity value determined this way would be use for all future measurements.

Further, for the tool wear measurements (complying with the ISO-3685 standard) and the roughness Ra evaluations (complying with the ISO-1997 standard) the following experimental equipment were adopted:
* Dinolite AM7915MZTL Microscope;

* Mahr MarSurf PS10 SET Portable digital roughness tester.

The experimental campaign was carried out under dry conditions; the considered time steps for the responses of interests and the signals acquisitions have been set on 1 minute.

The experiments were performed at the following cutting parameters:

* Cutting speed, $v_c=65m/min - 90m/min - 115m/min$;

* Feed rate, $f=0.075mm/rev$;

* Depth of cut, $p=0.5mm$.

The cutting tool used in the experimental activity is a Mitsubishi CCMT060204 M grade; the cutting parameters were selected based on the manufacturer's recommendations.

As output, the following responses have been considered:

1. Roughness, $Ra$;

2. Tool wear, $V_b$;

3. Temperature, $T$.

The Acc and AE signals were acquired with a 2.5 MHz sample rate; the signals processing and analysis was performed in Matlab 2021 environment. To remove the noise due to the different occurrences not directly related with the process, the Acc and AE signals were adequately filtered, and a Hamming window was then applied to the signals to mitigate the adverse effects of impulse response truncation. In particular, the Acc signals (in x, y and z directions) were filtered with a low pass FIR filter, with a 250 kHz cut frequency while the AE signals were filtered with a high pass FIR filter, with a 10 kHz threshold frequency. Finally, the Power Spectrum Density was calculated to analyse the signals in the frequency domain. To verify the repeatability of the process, three replicas were carried out. Fig. 3 shows, for the test with $v_c=90m/min$, an example of AE signal Power Spectrum related to all the replicas; it is possible to observe that the process is reasonably repeatable.

<figure><img src="image_1899.png" /><figcaption>Fig. 3. AE signal Power Spectrum.</figcaption></figure>

In the Feature extraction phase, a set of 40 features in the Time and Frequency domains were extracted from the Acc and AE sensors signals. The considered features in the Time Domain are listed as follows: Mean, Standard Deviation, Root Mean Square (RMS), Peak to Peak, Peak to Valley, Crest Factor and Kurtosis. The considered features in the Frequency Domain are listed as follows: Mean, Root Mean Square (RMS) and Crest Factor.

To reduce the problem complexity and to manage with less dimensions, a Principal Components Analysis was performed (Dimensionality reduction phase); the 40 features were reduced to 12 considering the scores S1-S12, without a loss of information content (there was still a 96% explained variance with only 12 features).

$T$, $V_b$ and $Ra$ were acquired during the experimental tests; Fig. 4 shows how the data were operationally collected.
* Dinolite AM7915MZTL Microscope;

* Mahr MarSurf PS10 SET Portable digital roughness tester.

The experimental campaign was carried out under dry conditions; the considered time steps for the responses of interests and the signals acquisitions have been set on 1 minute.

The experiments were performed at the following cutting parameters:

* Cutting speed, $v_c=65m/min - 90m/min - 115m/min$;

* Feed rate, $f=0.075mm/rev$;

* Depth of cut, $p=0.5mm$.

The cutting tool used in the experimental activity is a Mitsubishi CCMT060204 M grade; the cutting parameters were selected based on the manufacturer's recommendations.

As output, the following responses have been considered:

1. Roughness, $Ra$;

2. Tool wear, $V_b$;

3. Temperature, $T$.

The Acc and AE signals were acquired with a 2.5 MHz sample rate; the signals processing and analysis was performed in Matlab 2021 environment. To remove the noise due to the different occurrences not directly related with the process, the Acc and AE signals were adequately filtered, and a Hamming window was then applied to the signals to mitigate the adverse effects of impulse response truncation. In particular, the Acc signals (in x, y and z directions) were filtered with a low pass FIR filter, with a 250 kHz cut frequency while the AE signals were filtered with a high pass FIR filter, with a 10 kHz threshold frequency. Finally, the Power Spectrum Density was calculated to analyse the signals in the frequency domain. To verify the repeatability of the process, three replicas were carried out. Fig. 3 shows, for the test with $v_c=90m/min$, an example of AE signal Power Spectrum related to all the replicas; it is possible to observe that the process is reasonably repeatable.

<figure><img src="image_1894.png" /><figcaption>Fig. 3. AE signal Power Spectrum.</figcaption></figure>

In the Feature extraction phase, a set of 40 features in the Time and Frequency domains were extracted from the Acc and AE sensors signals. The considered features in the Time Domain are listed as follows: Mean, Standard Deviation, Root Mean Square (RMS), Peak to Peak, Peak to Valley, Crest Factor and Kurtosis. The considered features in the Frequency Domain are listed as follows: Mean, Root Mean Square (RMS) and Crest Factor.

To reduce the problem complexity and to manage with less dimensions, a Principal Components Analysis was performed (Dimensionality reduction phase); the 40 features were reduced to 12 considering the scores S1-S12, without a loss of information content (there was still a 96% explained variance with only 12 features).

$T$, $V_b$ and $Ra$ were acquired during the experimental tests; Fig. 4 shows how the data were operationally collected.
<figure><img src="image_1900.png" /><figcaption>Fig. 4. Data acquisition operative modes.</figcaption></figure>

Finally, in the tool wear, roughness, and temperature conditions prediction phase, the
followings different algorithms were trained and tested:

1. Decision Tree, with two different values of Minimum Life Size: DT-4, DT-12;

2. Artificial Neural Network, with two different values of the First Layer Size: ANN-10, ANN-25;

3. Support Vector Machine, with two different kernel functions: SVM-Q, SVM-C;

4. Gaussian Process Regression, with two different kernel functions: GPR-RQ, GPR-M5/2;

5. Gaussian Process Regression, with bayesian optimization: GPR-BO.

The models learning phase (training and validation) was performed using the 60% of the overall
data for the training and the 20% for the validation. The resting 20% of the data was used for the
testing phase. For the development of the models, the Matlab 2021 environment was used.

Results

In Fig.5 are represented the trends of Vb and roughness, acquired during the experimental activity
(the error bars are reported). As can be seen from the graphs, the trends of these responses are
consistent with the behaviors widely described in the literature [13].

<figure><img src="image_1901.png" /></figure>
<figure><img src="image_1895.png" /><figcaption>Fig. 4. Data acquisition operative modes.</figcaption></figure>

Finally, in the tool wear, roughness, and temperature conditions prediction phase, the followings different algorithms were trained and tested:

1. Decision Tree, with two different values of Minimum Life Size: DT-4, DT-12;

2. Artificial Neural Network, with two different values of the First Layer Size: ANN-10, ANN-25;

3. Support Vector Machine, with two different kernel functions: SVM-Q, SVM-C;

4. Gaussian Process Regression, with two different kernel functions: GPR-RQ, GPR-M5/2;

5. Gaussian Process Regression, with bayesian optimization: GPR-BO.

The models learning phase (training and validation) was performed using the 60% of the overall data for the training and the 20% for the validation. The resting 20% of the data was used for the testing phase. For the development of the models, the Matlab 2021 environment was used.

## Results

In Fig. 5 are represented the trends of Vb and roughness, acquired during the experimental activity (the error bars are reported). As can be seen from the graphs, the trends of these responses are consistent with the behaviors widely described in the literature [13].

<figure><img src="image_1896.png" /></figure>
<figure><img src="image_1902.png" /><figcaption>Fig.5. Vb and roughness experimental trends.</figcaption></figure>

Because the original dataset was sparse, the Makima interpolation technique was utilized to create new data points between existing ones, to generate a more extensive dataset. Therefore, the time steps were augmented obtaining data every 30 seconds, and 11 levels were added to the cutting speed values range (65m/min to 115m/min). This increased from 56 to 390 observations.

All data, including temperature, were used to train and validate the responses regression models as functions of the time t and the S1-S12 scores, obtained from the application of the PCA to the 40 features extracted from the Acc and AE signals. Then, the following models were developed and tested:

* VB=VB(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12);

* Ra=Ra(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12);

* T=T(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12).

Fig.6 shows the results obtained about the performances of Vb, Ra and T models. For all of them, RMSE and R-Squared values obtained both during the models' validation and testing phases, are presented. The RMSE index is related with the prediction error: the lower this index, the higher the model accuracy. On the other hand, the R-Squared index is function of the portion of the total variability explained by the model: the closer this index is to 1, the more accurate the model is.

Regarding the Vb regression, GPR models exhibits the lowest RMSE values and the highest R-Squared values; GPR with Bayesian optimization (GPR-BO) can be considered the most performing algorithm.

About the Ra regression, GPR models, particularly the GPR-BO, still excel in terms of RMSE and R-Squared.

Finally, about the T regression, there is a clear distinction between the performance offered by the GPR algorithms and others such as DT and ANN.
<figure><img src="image_1897.png" /><figcaption>Fig.5. Vb and roughness experimental trends.</figcaption></figure>

Because the original dataset was sparse, the Makima interpolation technique was utilized to create new data points between existing ones, to generate a more extensive dataset. Therefore, the time steps were augmented obtaining data every 30 seconds, and 11 levels were added to the cutting speed values range (65m/min to 115m/min). This increased from 56 to 390 observations.

All data, including temperature, were used to train and validate the responses regression models as functions of the time t and the S1-S12 scores, obtained from the application of the PCA to the 40 features extracted from the Acc and AE signals. Then, the following models were developed and tested:

* VB=VB(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12);

* Ra=Ra(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12);

* T=T(t,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12).

Fig.6 shows the results obtained about the performances of Vb, Ra and T models. For all of them, RMSE and R-Squared values obtained both during the models' validation and testing phases, are presented. The RMSE index is related with the prediction error: the lower this index, the higher the model accuracy. On the other hand, the R-Squared index is function of the portion of the total variability explained by the model: the closer this index is to 1, the more accurate the model is.

Regarding the Vb regression, GPR models exhibits the lowest RMSE values and the highest R-Squared values; GPR with Bayesian optimization (GPR-BO) can be considered the most performing algorithm.

About the Ra regression, GPR models, particularly the GPR-BO, still excel in terms of RMSE and R-Squared.

Finally, about the T regression, there is a clear distinction between the performance offered by the GPR algorithms and others such as DT and ANN.
<figure><img src="image_1903.png" /><figcaption>Fig.6. Vb, Ra and T models performances: RMSE and R-Squared values.</figcaption></figure>

Fig.7 shows the responses vs record number and the predicted vs true plots related to the Vb, Ra and T GPR-BO models.

The "responses vs record number" plot shows how the responses in the dataset change concerning the record number or index of the data: the plot shows how the response variable changes over time.

The "true vs predicted" plot is a visualization used to evaluate the performance of a predictive model, especially in regression or estimation tasks. It compares the actual (true) values of the target variable against the predicted values generated by the model.

As for the trends of Vb over time, ordered by increasing cutting speeds, it can be observed that at high speeds the curves are steeper and contain fewer observations. The difference between actual observations, in blue, and predicted observations, in orange, is minimal. About the Vb true vs predicted response plot, it shows how the observations are densely distributed around the line of perfect prediction. For Vb values below 0.1mm, the prediction is extremely accurate; with the evolution of the process, the contribution of uncontrollable phenomena is more marked, then the model predictions are less accurate. This occurrence is more evident in the Fig.8 where the experimental Standard Deviation versus Vb are linked with the evidence in the True vs Predicted plot; as can be seen, the Standard Deviation increasing with the Vb (and then, with the time), probably also generating the spread of the True vs Predicted plot after a Vb of 0.1 mm.
<figure><img src="image_1898.png" /><figcaption>Fig.6. Vb, Ra and T models performances: RMSE and R-Squared values.</figcaption></figure>

Fig.7 shows the responses vs record number and the predicted vs true plots related to the Vb, Ra and T GPR-BO models.

The "responses vs record number" plot shows how the responses in the dataset change concerning the record number or index of the data: the plot shows how the response variable changes over time.

The "true vs predicted" plot is a visualization used to evaluate the performance of a predictive model, especially in regression or estimation tasks. It compares the actual (true) values of the target variable against the predicted values generated by the model.

As for the trends of Vb over time, ordered by increasing cutting speeds, it can be observed that at high speeds the curves are steeper and contain fewer observations. The difference between actual observations, in blue, and predicted observations, in orange, is minimal. About the Vb true vs predicted response plot, it shows how the observations are densely distributed around the line of perfect prediction. For Vb values below 0.1mm, the prediction is extremely accurate; with the evolution of the process, the contribution of uncontrollable phenomena is more marked, then the model predictions are less accurate. This occurrence is more evident in the Fig.8 where the experimental Standard Deviation versus Vb are linked with the evidence in the True vs Predicted plot; as can be seen, the Standard Deviation increasing with the Vb (and then, with the time), probably also generating the spread of the True vs Predicted plot after a Vb of 0.1 mm.
The Ra plot over time shows the tendency of the response to decrease as the cutting speed increases. From each the individual trends, after a running-in phase, Ra stabilizes and then increases as a announce of process drift.

From the true vs predicted plot it is immediate to observe the high predictive capacity of the Ra GPR-BO model, in fact almost all the 390 observations of the dataset lie on the perfect prediction line.

Finally, from the observation of the response vs record number and true vs predicted plots, even for T the GPR_BO model demonstrates to provide a high degree of accuracy.

Additionally, when examining plot response and record numbers, a discernible almost quadratic trend emerges in the relationship between T and cutting speed. The temperature rises with the increase in cutting speed until reaching a specific threshold, beyond which it begins to decrease.

This pattern is explicable by the heightened heat dissipation resulting from the higher Material Removal Rate (MRR) at elevated cutting speeds.

<figure><img src="image_1904.png" /></figure>

<figure><img src="image_1905.png" /></figure>

<figure><img src="image_1906.png" /></figure>

<figure><img src="image_1907.png" /></figure>
The Ra plot over time shows the tendency of the response to decrease as the cutting speed increases. From each the individual trends, after a running-in phase, Ra stabilizes and then increases as a announce of process drift.

From the true vs predicted plot it is immediate to observe the high predictive capacity of the Ra GPR-BO model, in fact almost all the 390 observations of the dataset lie on the perfect prediction line.

Finally, from the observation of the response vs record number and true vs predicted plots, even for T the GPR_BO model demonstrates to provide a high degree of accuracy.

Additionally, when examining plot response and record numbers, a discernible almost quadratic trend emerges in the relationship between T and cutting speed. The temperature rises with the increase in cutting speed until reaching a specific threshold, beyond which it begins to decrease.

This pattern is explicable by the heightened heat dissipation resulting from the higher Material Removal Rate (MRR) at elevated cutting speeds.

<figure><img src="image_1899.png" /></figure>

<figure><img src="image_1900.png" /></figure>

<figure><img src="image_1901.png" /></figure>

<figure><img src="image_1902.png" /></figure>
<figure><img src="image_1908.png" /><figcaption>Fig. 7. Vb, Ra and T models performances: responses vs record number plots (actual observations, in blue, predicted observations, in orange), predicted vs true response plots.</figcaption></figure>

<figure><img src="image_1909.png" /><figcaption>Fig. 8. Standard Deviation vs Vb.</figcaption></figure>

**Conclusions and future developments.**

This study evaluates the performance of artificial intelligence-based approaches for estimating roughness, tool wear and temperature at the tool/chip interface during turning of S235jr alloy steel. Different algorithms were trained to estimate the roughness, tool wear and temperature values in the process at several cutting speeds. The proposed models are developed on a dataset of 390 data points (60% of which are used in the training phase, 20% in the validation phase and the remaining 20% in the testing phase). The reported results show the feasibility of the GPR-BO models for estimating roughness, tool wear and temperature with high accuracy (RMSE and R-Squared were evaluated). The estimated values are in good agreement with the performed experiments and with the results reported in literature. The obtained results demonstrate the effectiveness of the Tool and Surface Conditions Monitoring based on previous training dataset in the reduction of the experimental work and resources. As future development, the authors will extend the experimental activity considering tests at different feed rate and depth of cut. Moreover, the developed models will be integrated in a process automation able of near real time anomaly detection.

In addition, the authors will develop a process automation integrating sensors, acquisition devices, analysis software, developed models and that will be able to perform anomaly detection in near real time.
<figure><img src="image_1903.png" /><figcaption>Fig. 7. Vb, Ra and T models performances: responses vs record number plots (actual observations, in blue, predicted observations, in orange), predicted vs true response plots.</figcaption></figure>

<figure><img src="image_1904.png" /><figcaption>Fig. 8. Standard Deviation vs Vb.</figcaption></figure>

**Conclusions and future developments.**

This study evaluates the performance of artificial intelligence-based approaches for estimating roughness, tool wear and temperature at the tool/chip interface during turning of S235jr alloy steel. Different algorithms were trained to estimate the roughness, tool wear and temperature values in the process at several cutting speeds. The proposed models are developed on a dataset of 390 data points (60% of which are used in the training phase, 20% in the validation phase and the remaining 20% in the testing phase). The reported results show the feasibility of the GPR-BO models for estimating roughness, tool wear and temperature with high accuracy (RMSE and R-Squared were evaluated). The estimated values are in good agreement with the performed experiments and with the results reported in literature. The obtained results demonstrate the effectiveness of the Tool and Surface Conditions Monitoring based on previous training dataset in the reduction of the experimental work and resources. As future development, the authors will extend the experimental activity considering tests at different feed rate and depth of cut. Moreover, the developed models will be integrated in a process automation able of near real time anomaly detection.

In addition, the authors will develop a process automation integrating sensors, acquisition devices, analysis software, developed models and that will be able to perform anomaly detection in near real time.
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[6] B. S. Prasad, K. A. Prabha, P.V.S. Ganesh Kumar, Condition monitoring of turning process using infrared thermography technique – An experimental approach, Infrared Physics & Technology, Volume 81, 2017, Pages 137-147, ISSN 1350-4495. https://doi.org/10.1016/j.infrared.2016.12.023

[7] K. Jemielniak, P.J. Arrazola, Application of AE and cutting force signals in tool condition monitoring in micro-milling, CIRP Journal of Manufacturing Science and Technology, Volume 1, Issue 2, 2008, Pages 97-102, ISSN 1755-5817, https://doi.org/10.1016/j.cirpj.2008.09.007

[8] G. Serin & B. Sener & A. M. Ozbayoglu & H. O. Unver, Review of tool condition monitoring in machining and opportunities for deep learning, The International Journal of Advanced Manufacturing Technology (2020) 109:953-974. https://doi.org/10.1007/s00170-020-05449-w

[9] Krzysztof Jemielniak & Tomasz Urbański & Joanna Kossakowska & Sebastian Bombiński, Tool condition monitoring based on numerous signal features, Int J Adv Manuf Technol (2012) 59:73-81. https://doi.org/10.1007/s00170-011-3504-2

[10] Mustafa Kuntoglu, Hacı Saglam, Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning, Measurement Volume 173, March 2021, 108582. https://doi.org/10.1016/j.measurement.2020.108582

[11] Mustafa Kuntoglu, Abdullah Aslan, Hacı Saglam, Danil Yurievich Pimenov, Khaled Giasin and Tadeusz Mikolajczyk, Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140, 2020 Sensors. https://doi.org/10.3390/s20164377

[12] Pauline Ong, Woon Kiow Lee & Raymond Jit Hoo Lau, Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision, The International Journal of Advanced Manufacturing Technology (2019) 104:1369-1379. https://doi.org/10.1007/s00170-019-04020-6

[13] P.G. Benardos, G.-C. Vosniakos, Predicting surface roughness in machining: a review, International Journal of Machine Tools and Manufacture, Volume 43, Issue 8, 2003, Pages 833-844, ISSN 0890-6955. https://doi.org/10.1016/S0890-6955(03)00059-2
## References

[1] Çelik, Y.H., Kilickap, E. & Güney, M. Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti-6Al-4V using CVD and PVD coated tools. J Braz. Soc. Mech. Sci. Eng. 39, 2085-2093 (2017). https://doi.org/10.1007/s40430-016-0607-6

[2] Saini, S., Ahuja, I.S. & Sharma, V.S. Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools. Int. J. Precis. Eng. Manuf. 13, 1295-1302 (2012). https://doi.org/10.1007/s12541-012-0172-6

[3] H.K. Tönshoff, I. Inasaki, Sensors in Manufacturing, Volume 1, 2001, Wiley-VCH Verlag GmbH. https://doi.org/10.1002/3527600027

[4] M. S. H. Bhuiyan, I. A. Choudhury, Y. Nukman, Tool Condition Monitoring using Acoustic Emission and Vibration Signature in Turning, Proceedings of the World Congress on Engineering 2012 Vol III WCE 2012, July 4 - 6, 2012, London, U.K.

[5] J. Ratava, M. Lohtander, J. Varis, Tool condition monitoring in interrupted cutting with acceleration sensors, Robotics and Computer-Integrated Manufacturing, Volume 47, 2017, Pages 70-75, ISSN 0736-5845, https://doi.org/10.1016/j.rcim.2016.11.008

[6] B. S. Prasad, K. A. Prabha, P.V.S. Ganesh Kumar, Condition monitoring of turning process using infrared thermography technique – An experimental approach, Infrared Physics & Technology, Volume 81, 2017, Pages 137-147, ISSN 1350-4495. https://doi.org/10.1016/j.infrared.2016.12.023

[7] K. Jemielniak, P.J. Arrazola, Application of AE and cutting force signals in tool condition monitoring in micro-milling, CIRP Journal of Manufacturing Science and Technology, Volume 1, Issue 2, 2008, Pages 97-102, ISSN 1755-5817, https://doi.org/10.1016/j.cirpj.2008.09.007

[8] G. Serin & B. Sener & A. M. Ozbayoglu & H. O. Unver, Review of tool condition monitoring in machining and opportunities for deep learning, The International Journal of Advanced Manufacturing Technology (2020) 109:953-974. https://doi.org/10.1007/s00170-020-05449-w

[9] Krzysztof Jemielniak & Tomasz Urbański & Joanna Kossakowska & Sebastian Bombiński, Tool condition monitoring based on numerous signal features, Int J Adv Manuf Technol (2012) 59:73-81. https://doi.org/10.1007/s00170-011-3504-2

[10] Mustafa Kuntoglu, Hacı Saglam, Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning, Measurement Volume 173, March 2021, 108582. https://doi.org/10.1016/j.measurement.2020.108582

[11] Mustafa Kuntoglu, Abdullah Aslan, Hacı Saglam, Danil Yurievich Pimenov, Khaled Giasin and Tadeusz Mikolajczyk, Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140, 2020 Sensors. https://doi.org/10.3390/s20164377

[12] Pauline Ong, Woon Kiow Lee & Raymond Jit Hoo Lau, Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision, The International Journal of Advanced Manufacturing Technology (2019) 104:1369-1379. https://doi.org/10.1007/s00170-019-04020-6

[13] P.G. Benardos, G.-C. Vosniakos, Predicting surface roughness in machining: a review, International Journal of Machine Tools and Manufacture, Volume 43, Issue 8, 2003, Pages 833-844, ISSN 0890-6955. https://doi.org/10.1016/S0890-6955(03)00059-2
# Estimation of the residual stress field of laminated aeronautical parts to prevent distortion after machining

CHAABANI Khayel¹,²,³,a*, BEN SAADA Mariem¹,b, LAVISSE Bruno¹,c,
RITOU Mathieu²,d and GERMAIN Guénaël¹,e

¹LAMPA, Arts et Métiers, Angers, France

²LS2N, Nantes Université, Nantes, France

³IRT Jules Verne, Nantes, France

a khayel.chaabani@ensam.eu, b mariem.bensaada@ensam.eu, c bruno.lavisse@ensam.eu,

d mathieu.ritou@univ-nantes.fr, e guenael.germain@ensam.eu

**Keywords:** Distortion, Residual Stresses (RS), Distortion Control, Simulation, Machining

**Abstract.** The estimation of post-machining distortion of monolithic aeronautical parts induced by the redistribution of the bulk residual stresses (RS) during machining is one of the major challenges of aeronautical parts manufacturing. Since it is the main cause of thick parts post-machining distortion, it is essential to know the state of the initial RS so that the machining strategy can be modified to minimize distortion of each part. The problem is even more complex because the RS field is not identical from one part to another. Considering an average stress field provides satisfactory results only for parts with simple geometries and a highly repeatable manufacturing process, which is rarely the case in an industrial setting. By simulating the steps of the production of the laminated blank, the variability of RS field will be established. This variability can be used to determine the distribution of the RS field of each part during machining.

**Introduction**

Several industrial sectors, including the automotive, medical, and aerospace industries, heavily rely on aluminum alloy material due to its high strength and corrosion resistance [1, 2]. Aeronautical industries extensively use aluminum laminated blanks which are usually machined in thick plates. To reach high mechanical properties, these blanks are subject to heat treatments (i.e. quenching, tempering, etc.). Thus, they undergo plastic deformations and thermal gradients, during the forming process, due to thermo-mechanical loads which generate in the blanks high intensity residual stresses called also initial Residual Stresses (RS) [3, 4].

For manufacturers, reducing part distortions remains a major challenge even with the widespread use of this material. During forming steps, blank rolled plates undergo heat treatments and plastic deformations which generate RS in the blank.

The blanks are then subject to a machining process aiming to obtain the desired final part shape. The loading applied during this process can also induce RS on the part surface called near-surface RS [4, 5]. Compared to the initial RS, the near-surface RS can be neglected when working with large thick aluminum alloy parts since the affected depth does not exceed a few hundred microns from the top surface [6]. In industry, the main issue is not directly related to the near-surface RS but rather to the distortion of the finished part [1, 7]. The redistribution of the initial RS owing to material removal during machining is the main cause of post-machining distortions (after unclamping). These distortions may cause the parts to be rejected or require additional conforming operations, which could increase the cost of production [1, 2]. Since it is the main cause of thick parts post-machining distortion, it is essential to know the state of the initial RS so that the machining strategy can be modified to minimize distortion of each part.

<figure><img src="image_1910.png" /></figure>
# Estimation of the residual stress field of laminated aeronautical parts to prevent distortion after machining

CHAABANI Khayel¹,²,³,a*, BEN SAADA Mariem¹,ᵇ, LAVISSE Bruno¹,ᶜ, RITOU Mathieu²,ᵈ and GERMAIN Guénaël¹,ᵉ

¹LAMPA, Arts et Métiers, Angers, France

²LS2N, Nantes Université, Nantes, France

³IRT Jules Verne, Nantes, France

ªkhayel.chaabani@ensam.eu, ᵇmariem.bensaada@ensam.eu, ᶜbruno.lavisse@ensam.eu,

ᵈmathieu.ritou@univ-nantes.fr, ᶜguenael.germain@ensam.eu

**Keywords:** Distortion, Residual Stresses (RS), Distortion Control, Simulation, Machining

**Abstract.** The estimation of post-machining distortion of monolithic aeronautical parts induced by the redistribution of the bulk residual stresses (RS) during machining is one of the major challenges of aeronautical parts manufacturing. Since it is the main cause of thick parts post-machining distortion, it is essential to know the state of the initial RS so that the machining strategy can be modified to minimize distortion of each part. The problem is even more complex because the RS field is not identical from one part to another. Considering an average stress field provides satisfactory results only for parts with simple geometries and a highly repeatable manufacturing process, which is rarely the case in an industrial setting. By simulating the steps of the production of the laminated blank, the variability of RS field will be established. This variability can be used to determine the distribution of the RS field of each part during machining.

**Introduction**

Several industrial sectors, including the automotive, medical, and aerospace industries, heavily rely on aluminum alloy material due to its high strength and corrosion resistance [1, 2]. Aeronautical industries extensively use aluminum laminated blanks which are usually machined in thick plates. To reach high mechanical properties, these blanks are subject to heat treatments (i.e. quenching, tempering, etc.). Thus, they undergo plastic deformations and thermal gradients, during the forming process, due to thermo-mechanical loads which generate in the blanks high intensity residual stresses called also initial Residual Stresses (RS) [3, 4].

For manufacturers, reducing part distortions remains a major challenge even with the widespread use of this material. During forming steps, blank rolled plates undergo heat treatments and plastic deformations which generate RS in the blank.

The blanks are then subject to a machining process aiming to obtain the desired final part shape. The loading applied during this process can also induce RS on the part surface called near-surface RS [4, 5]. Compared to the initial RS, the near-surface RS can be neglected when working with large thick aluminum alloy parts since the affected depth does not exceed a few hundred microns from the top surface [6]. In industry, the main issue is not directly related to the near-surface RS but rather to the distortion of the finished part [1, 7]. The redistribution of the initial RS owing to material removal during machining is the main cause of post-machining distortions (after unclamping). These distortions may cause the parts to be rejected or require additional conforming operations, which could increase the cost of production [1, 2]. Since it is the main cause of thick parts post-machining distortion, it is essential to know the state of the initial RS so that the machining strategy can be modified to minimize distortion of each part.

<figure><img src="image_1905.png" /></figure>
The RS profiles can be experimentally determined using destructive and non-destructive tests [4, 8, 9]. Among these techniques, the most generally used are the X-ray diffraction, which is more suitable for the surface; and the layer removal or by incremental drilling. The experimental RS profiles are then used to develop Finite Element Models (FEM) which allows simulating machining process and then to predict the machining distortion considering the redistribution of the initial RS [10]. Numerical simulations have been widely used to simulate the RS field in aeronautical parts, as experimental studies are often costly and time-consuming, and may require sacrificing the part to carry out the measurements [11, 12].

The approach of the present work aims to establish a hybrid methodology to predict the variability of the RS field induced in aluminum bulk blanks, due to the manufacturing history of the blank. This enables controlling the machining strategy at each stage to reduce the final part distortions. This approach is detailed in the section “Proposed methodology” and then the results of the variability of the initial RS field are presented. This present paper focuses only on the first part of the proposed approach which is the numerical simulations of the variability of the initial RS field.

## Proposed methodology

Part distortion is caused by the release and redistribution of the RS generated by forming and heat treatments applied before machining. Improving initial RS field comprehension allows the definition of a more effective machining strategy to reduce post-machining distortions. For this purpose, we have implemented this methodology, wherein the preparation stage of this approach consists of simulating the manufacturing process of the bulk of laminated blanks using Finite Element Method (FEM). Numerical simulations under the same experimental conditions were performed using Abaqus software. These simulations enable therefore to determine the variability of the initial RS field induced by process variability. This part of the work will be carried out only once since the applied manufacturing process that is being used is unchanged. Once quantified, the initial RS will be used later in an Artificial Intelligence (AI) model. This AI model will be applied to predict the RS field for each bulk blank. The estimated RS field will allow us to control the machining strategy at all its stages in order to reduce the part distortions. After each machining step, in-process distortion measurements will be taken and integrated in the developed machining model to adjust the next machining step.

Concerning the blank manufacturing process, before quenching, the plates undergo a solution heat treatment (a homogenization thermal cycle), as shown in Fig.1, in order to relieve the RS induced by the plastic deformations during the rolling steps. It is assumed that the RS field is free after the solution heat treatment.

During quenching process, thick aluminum blanks of 7010 alloys were submerged in a uniform heat treatment solution which is supposed to remove the RS induced by lamination. The parts are maintained at 470 °C for many hours and then, they are subject to a sudden immersion in the water at 20 °C which is a common quenching media for Al alloys. In this model, the material's behavior is governed by a modified power law. The mechanical properties of the Al7010-T7451 are thermo-dependent and all parameters are confidential properties.

After quenching, the temperature distribution is inserted in a static analysis to release the RS. A cold stretch or a detensioning by traction is then carried out in the range of 1,5% to 3% before releasing the stresses at the last step.

We did not simulate tempering step due to huge simulation time of the coupled thermomechanical simulation. The influence of the tempering stage on the RS field is very slight, as the temperature rise is slow and fairly low, resulting no significant thermal gradient. There is therefore no stress gradient due to expansion in this manufacturing stage. Thus, this stage has not been simulated.
The RS profiles can be experimentally determined using destructive and non-destructive tests [4, 8, 9]. Among these techniques, the most generally used are the X-ray diffraction, which is more suitable for the surface; and the layer removal or by incremental drilling. The experimental RS profiles are then used to develop Finite Element Models (FEM) which allows simulating machining process and then to predict the machining distortion considering the redistribution of the initial RS [10]. Numerical simulations have been widely used to simulate the RS field in aeronautical parts, as experimental studies are often costly and time-consuming, and may require sacrificing the part to carry out the measurements [11, 12].

The approach of the present work aims to establish a hybrid methodology to predict the variability of the RS field induced in aluminum bulk blanks, due to the manufacturing history of the blank. This enables controlling the machining strategy at each stage to reduce the final part distortions. This approach is detailed in the section “Proposed methodology” and then the results of the variability of the initial RS field are presented. This present paper focuses only on the first part of the proposed approach which is the numerical simulations of the variability of the initial RS field.

## Proposed methodology

Part distortion is caused by the release and redistribution of the RS generated by forming and heat treatments applied before machining. Improving initial RS field comprehension allows the definition of a more effective machining strategy to reduce post-machining distortions. For this purpose, we have implemented this methodology, wherein the preparation stage of this approach consists of simulating the manufacturing process of the bulk of laminated blanks using Finite Element Method (FEM). Numerical simulations under the same experimental conditions were performed using Abaqus software. These simulations enable therefore to determine the variability of the initial RS field induced by process variability. This part of the work will be carried out only once since the applied manufacturing process that is being used is unchanged. Once quantified, the initial RS will be used later in an Artificial Intelligence (AI) model. This AI model will be applied to predict the RS field for each bulk blank. The estimated RS field will allow us to control the machining strategy at all its stages in order to reduce the part distortions. After each machining step, in-process distortion measurements will be taken and integrated in the developed machining model to adjust the next machining step.

Concerning the blank manufacturing process, before quenching, the plates undergo a solution heat treatment (a homogenization thermal cycle), as shown in Fig.1, in order to relieve the RS induced by the plastic deformations during the rolling steps. It is assumed that the RS field is free after the solution heat treatment.

During quenching process, thick aluminum blanks of 7010 alloys were submerged in a uniform heat treatment solution which is supposed to remove the RS induced by lamination. The parts are maintained at 470 °C for many hours and then, they are subject to a sudden immersion in the water at 20 °C which is a common quenching media for Al alloys. In this model, the material's behavior is governed by a modified power law. The mechanical properties of the Al7010-T7451 are thermo-dependent and all parameters are confidential properties.

After quenching, the temperature distribution is inserted in a static analysis to release the RS. A cold stretch or a detensioning by traction is then carried out in the range of 1,5% to 3% before releasing the stresses at the last step.

We did not simulate tempering step due to huge simulation time of the coupled thermomechanical simulation. The influence of the tempering stage on the RS field is very slight, as the temperature rise is slow and fairly low, resulting no significant thermal gradient. There is therefore no stress gradient due to expansion in this manufacturing stage. Thus, this stage has not been simulated.
<figure><img src="image_1911.png" /><figcaption>Fig. 1 Forming and heat treatment process</figcaption></figure>

Simulation setting

The final use case concerns large parts of aeronautic structure that are machined in long and thick laminated blank of Al7010 or Al7050 Aluminum. The alloy Al7010 which is of European origin has properties similar to the American Grade 7050. For example, the blank can measure approximately 3 m long, 1 m width, and 120 mm thickness. To carry out a simulation of a three meters length part, the calculation time is oversized. Assuming an isotropic behavior of the material, we have opted for a more effective model. The simulated part measures 800 mm long, 800mm width and 120mm thickness and we have considered the symmetric property of the part. These dimensions are more than sufficient to avoid edge effect. The results shown in Fig. 2 (a) clearly demonstrate the edge effect and that the stress field becomes stable at the center of the part. This observation shows that the dimensions chosen for the model mean that it is not affected by edge effects. A total of 76800 5mm-sized hexahedral components were used to mesh the plate. The mesh type is “DC3D8” during heat transfer step and “C3D8R” during static ones.

<figure><img src="image_1912.png" /><figcaption>Fig. 2 (a) RS field (S11) after quenching and cold stretching stages with the nominal parameters – (b) Boundary conditions</figcaption></figure>

For the quenching step, heat transfer is modeled by assigning an initial temperature of 470°C to the part. During cooling, the external surfaces of the part are subject to a convection-type boundary condition, with an exchange coefficient h depending on the part temperature and a constant bath temperature (see Table 1). The other surfaces have symmetrical boundary conditions, implying no heat exchange on these surfaces. For the cold stretching step, the symmetry conditions are maintained (Fig. 2b). On the end surface, a homogeneous displacement was imposed to obtain
<figure><img src="image_1906.png" /><figcaption>Fig. 1 Forming and heat treatment process</figcaption></figure>

Simulation setting

The final use case concerns large parts of aeronautic structure that are machined in long and thick laminated blank of Al7010 or Al7050 Aluminum. The alloy Al7010 which is of European origin has properties similar to the American Grade 7050. For example, the blank can measure approximately 3 m long, 1 m width, and 120 mm thickness. To carry out a simulation of a three meters length part, the calculation time is oversized. Assuming an isotropic behavior of the material, we have opted for a more effective model. The simulated part measures 800 mm long, 800mm width and 120mm thickness and we have considered the symmetric property of the part. These dimensions are more than sufficient to avoid edge effect. The results shown in Fig. 2 (a) clearly demonstrate the edge effect and that the stress field becomes stable at the center of the part. This observation shows that the dimensions chosen for the model mean that it is not affected by edge effects. A total of 76800 5mm-sized hexahedral components were used to mesh the plate. The mesh type is “DC3D8” during heat transfer step and “C3D8R” during static ones.

<figure><img src="image_1907.png" /><figcaption>Fig. 2 (a) RS field (S11) after quenching and cold stretching stages with the nominal parameters – (b) Boundary conditions</figcaption></figure>

For the quenching step, heat transfer is modeled by assigning an initial temperature of 470°C to the part. During cooling, the external surfaces of the part are subject to a convection-type boundary condition, with an exchange coefficient h depending on the part temperature and a constant bath temperature (see Table 1). The other surfaces have symmetrical boundary conditions, implying no heat exchange on these surfaces. For the cold stretching step, the symmetry conditions are maintained (Fig. 2b). On the end surface, a homogeneous displacement was imposed to obtain
the desired elongation (see Table 1). Figure 2a shows an example of the RS field obtained after stretching.

To simulate the effect of the heat treatments, it is conventional to separate the thermal simulation from the mechanical simulation, in order to limit the resources required and save calculation time [4, 13].

In this approach, the thermomechanical problem is assumed to be uncoupled: the thermal and mechanical analysis are solved sequentially and consecutively. This assumption allows controlling complex phenomena released during the heat treatment process, that leads to a better representation of the RS distribution. The simulation steps can be summarized in four principal tasks as shown in Fig. 3: initially, a non-linear heat transfer analysis is carried out, considering the thermo-dependent mechanical parameters. Subsequently, a thermo-elastoplastic analysis using the predicted temperature distributions, assuming an isotropic behavior of the material and considering the temperature dependence of its mechanical properties.

<figure><img src="image_1913.png" /><figcaption>Fig. 3 Steps followed in the numerical simulation model</figcaption></figure>

The variability of the manufacturing process parameters is the main cause of the initial RS field's variability. Three parameters are studied during these simulations as shown in the Table 1:

- The temperature of the quench bath: 20°C is the nominal temperature of the quench.

- The thermal convection coefficient with water h: several parameters affect the thermal convection coefficient like the temperature of the water, its flow, etc. The thermal convection coefficient used during these simulations is temperature dependent and taken from the literature [14]. "h" referred in Table 1 is the nominal one of the simulations.

- The cold stretch or elongation range: in general, the plate is stretched by classical traction between 1.5 and 3%.

In order to represent this variability, we carried out several numerical simulations combining the different parameters. The results of these simulations will be discussed in the next section.

<table><caption>Table 1. Simulation plan of the process parameters variability</caption><thead><tr><th rowspan="2">Process parameters</th><th colspan="5">Variability</th></tr><tr><th>18°C</th><th>20°C</th><th>25°C</th><th>30°C</th><th>---</th></tr></thead><tbody><tr><th>Bath temperature</th><td>h</td><td>1.5 * h</td><td>2 * h</td><td>2.5 * h</td><td>3 * h</td></tr><tr><th>Thermal convection coefficient</th><td>h</td><td>1.5 * h</td><td>2 * h</td><td>2.5 * h</td><td>3 * h</td></tr><tr><th>Cold stretch rate</th><td>1.5%</td><td>2%</td><td>2.5%</td><td>3%</td><td>3.5%</td></tr></tbody></table>

## Results and discussion

During quenching, the cooling rate is not uniform from the surface to the core of the part, creating a thermal gradient. The contraction of the part's core during cooling will therefore compress the part's surface, as shown in Fig. 4. The RS fields depicted in Figures 4 to 8 correspond to the values along the path illustrated in Fig. 2 (a).
the desired elongation (see Table 1). Figure 2a shows an example of the RS field obtained after stretching.

To simulate the effect of the heat treatments, it is conventional to separate the thermal simulation from the mechanical simulation, in order to limit the resources required and save calculation time [4, 13].

In this approach, the thermomechanical problem is assumed to be uncoupled: the thermal and mechanical analysis are solved sequentially and consecutively. This assumption allows controlling complex phenomena released during the heat treatment process, that leads to a better representation of the RS distribution. The simulation steps can be summarized in four principal tasks as shown in Fig. 3: initially, a non-linear heat transfer analysis is carried out, considering the thermo-dependent mechanical parameters. Subsequently, a thermo-elastoplastic analysis using the predicted temperature distributions, assuming an isotropic behavior of the material and considering the temperature dependence of its mechanical properties.

<figure><img src="image_1908.png" /><figcaption>Fig. 3 Steps followed in the numerical simulation model</figcaption></figure>

The variability of the manufacturing process parameters is the main cause of the initial RS field's variability. Three parameters are studied during these simulations as shown in the Table 1:

- The temperature of the quench bath: 20°C is the nominal temperature of the quench.

- The thermal convection coefficient with water h: several parameters affect the thermal convection coefficient like the temperature of the water, its flow, etc. The thermal convection coefficient used during these simulations is temperature dependent and taken from the literature [14]. "h" referred in Table 1 is the nominal one of the simulations.

- The cold stretch or elongation range: in general, the plate is stretched by classical traction between 1.5 and 3%.

In order to represent this variability, we carried out several numerical simulations combining the different parameters. The results of these simulations will be discussed in the next section.

<table><caption>Table 1. Simulation plan of the process parameters variability</caption><thead><tr><th rowspan="2">Process parameters</th><th colspan="5">Variability</th></tr><tr><th>18°C</th><th>20°C</th><th>25°C</th><th>30°C</th><th>---</th></tr></thead><tbody><tr><th>Bath temperature</th><td>h</td><td>1.5 * h</td><td>2 * h</td><td>2.5 * h</td><td>3 * h</td></tr><tr><th>Thermal convection coefficient</th><td>h</td><td>1.5 * h</td><td>2 * h</td><td>2.5 * h</td><td>3 * h</td></tr><tr><th>Cold stretch rate</th><td>1.5%</td><td>2%</td><td>2.5%</td><td>3%</td><td>3.5%</td></tr></tbody></table>

## Results and discussion

During quenching, the cooling rate is not uniform from the surface to the core of the part, creating a thermal gradient. The contraction of the part's core during cooling will therefore compress the part's surface, as shown in Fig. 4. The RS fields depicted in Figures 4 to 8 correspond to the values along the path illustrated in Fig. 2 (a).
<figure><img src="image_1914.png" /><figcaption>Fig. 4 Shape of half of the RS field along the half of the thickness of the rolled plate</figcaption></figure>

<figure><img src="image_1915.png" /><figcaption>Fig. 5 Thermal convection coefficient's influence on the RS field</figcaption></figure>

The variability of the thermal convection coefficient has a significant impact on RS, as it depends on various factors such as the quenching bath temperature, water flow rate, whether convection is forced or natural, etc. This variability affects both the surface and the core of the part, as shown in Fig. 5.

The quench bath temperature has little impact on the core of the part, resulting in an increase in compression of a few MPa as the bath temperature decreases, from 192 MPa at 18 degrees to 184 MPa at 30 degrees. This influence remains limited to the core of the part and does not significantly affect the stresses at the surface as shown in Fig. 6.
<figure><img src="image_1909.png" /><figcaption>Fig. 4 Shape of half of the RS field along the depth of the rolled plate</figcaption></figure>

<figure><img src="image_1910.png" /><figcaption>Fig. 5 Thermal convection coefficient's influence on the RS field</figcaption></figure>

The variability of the thermal convection coefficient has a significant impact on RS, as it depends on various factors such as the quenching bath temperature, water flow rate, whether convection is forced or natural, etc. This variability affects both the surface and the core of the part, as shown in Fig. 5.

The quench bath temperature has little impact on the core of the part, resulting in an increase in compression of a few MPa as the bath temperature decreases, from 192 MPa at 18 degrees to 184 MPa at 30 degrees. This influence remains limited to the core of the part and does not significantly affect the stresses at the surface as shown in Fig. 6.
During cold stretch, the elongation value has a significant influence on the distribution of the stress field as shown in Fig. 8. Indeed, stretching results in a significant reduction of core stresses, decreasing from 191 MPa before stretching to 20 MPa after a 3% stretch, as illustrated in Fig. 8. Similarly, at the surface, compression stresses increase from -265 MPa before stretching to -31 MPa after a 3% stretch. Stress values and gradients after stretching are therefore lower, which will greatly limit part deformation after machining. Thus, a well-controlled elongation value ensures a predictable and consistent stress distribution within the material. This underscores the need for meticulous control to minimize undesired stress field variations. A variation in elongation from 1.5% to 3.5%, as shown in Fig. 7, causes the surface stress to vary from approximately -120 MPa to -20 MPa, an impressive reduction of 100 MPa.

<figure><img src="image_1916.png" /><figcaption>Fig. 6 Quenching bath temperature's influence on the RS field</figcaption></figure>
During cold stretch, the elongation value has a significant influence on the distribution of the stress field as shown in Fig. 8. Indeed, stretching results in a significant reduction of core stresses, decreasing from 191 MPa before stretching to 20 MPa after a 3% stretch, as illustrated in Fig. 8. Similarly, at the surface, compression stresses increase from -265 MPa before stretching to -31 MPa after a 3% stretch. Stress values and gradients after stretching are therefore lower, which will greatly limit part deformation after machining. Thus, a well-controlled elongation value ensures a predictable and consistent stress distribution within the material. This underscores the need for meticulous control to minimize undesired stress field variations. A variation in elongation from 1.5% to 3.5%, as shown in Fig. 7, causes the surface stress to vary from approximately -120 MPa to -20 MPa, an impressive reduction of 100 MPa.

<figure><img src="image_1911.png" /><figcaption>Fig. 6 Quenching bath temperature's influence on the RS field</figcaption></figure>
<figure><img src="image_1917.png" /><figcaption>Fig. 7 Effect of the elongation value on the RS field</figcaption></figure>

<figure><img src="image_1918.png" /><figcaption>Fig. 8 RS field before and after elongation</figcaption></figure>
<figure><img src="image_1912.png" /><figcaption>Fig. 7 Effect of the elongation value on the RS field</figcaption></figure>

<figure><img src="image_1913.png" /><figcaption>Fig. 8 RS field before and after elongation</figcaption></figure>
## Sensitivity study

<figure><img src="image_1919.png" /><figcaption>Fig. 9 Parameters sensitivity analysis results</figcaption></figure>

Based on this finite element model, we assessed the sensitivity of RS to process parameters during the forming and heat treatment of the blank parts. The parameters sensitivity analysis is illustrated in Fig. 9.
## Sensitivity study

<figure><img src="image_1914.png" /><figcaption>Fig. 9 Parameters sensitivity analysis results</figcaption></figure>

Based on this finite element model, we assessed the sensitivity of RS to process parameters during the forming and heat treatment of the blank parts. The parameters sensitivity analysis is illustrated in Fig. 9.
This analysis reveals that the initial stress field of the part, encompassing both surface and core, is exclusively influenced by the applied elongation value during the stretching step, and the thermal convection coefficient of the quenching medium.

Depending on the deformation of the part obtained at the start of machining, it will be possible to estimate the elongation value and the thermal convection value applied to the part and thus know the stress field of the part during manufacture (with the future IA model). In this way, it will be possible to modify the strategy at the end of the machining stage to minimize the final deformation of the part.

## Conclusions

In this work, a thermomechanical model was developed to simulate the manufacturing process of aluminum alloy laminated blanks on Abaqus software. Several numerical simulations, combining the different material parameters, have been carried out to determine the initial RS field's variability.

The results show that the variability of quenching temperature does not significantly influence the final stress field. On the other hand, the stretching step plays a crucial role in stress distribution. The thermal convection coefficient also influences the stress field, but to a much lesser extent. This highlights the need for meticulous control for these parameters to reduce undesirable stress variations.

Based on these results, variations in the RS field are now known as a function of operating parameters. This will make it possible to predict the distribution of the RS field for each part during machining, offering a proactive approach to prevent distortions.

## Acknowledgements

The authors acknowledge the funding of PERFORM Program of IRT Jules Verne.

## References

[1] Sim W-M (2010) Challenges of residual stress and part distortion in the civil airframe industry. International Journal of Microstructure and Materials Properties 5:446-455. https://doi.org/10.1504/IJMMP.2010.037621

[2] Li J, Wang S (2017) Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances. Int J Adv Manuf Technol 89:997-1012. https://doi.org/10.1007/s00170-016-9066-6

[3] Tang ZT, Yu T, Xu LQ, Liu ZQ (2013) Machining deformation prediction for frame components considering multifactor coupling effects. Int J Adv Manuf Technol 68:187-196. https://doi.org/10.1007/s00170-012-4718-7

[4] Jeanmart P, Bouvaist J (1985) Finite element calculation and measurement of thermal stresses in quenched plates of high-strength 7075 aluminium alloy. Materials Science and Technology 1:765-769. https://doi.org/10.1179/mst.1985.1.10.765

[5] Denkena B, De Leon L (2008) Milling induced residual stresses in structural parts out of forged aluminium alloys. International Journal of Machining and Machinability of Materials 4:335-344. https://doi.org/10.1504/IJMMM.2008.023717

[6] Chatelain J-F, Lalonde J-F, Tahan AS (2012) Effect of Residual Stresses Embedded within Workpieces on the Distortion of Parts after Machining. 6:

[7] Lequeu P, Lassince P, Warner T, Raynaud GM (2001) Engineering for the future: weight saving and cost reduction initiatives. Aircraft Engineering and Aerospace Technology 73:147-159. https://doi.org/10.1108/00022660110386663
This analysis reveals that the initial stress field of the part, encompassing both surface and core, is exclusively influenced by the applied elongation value during the stretching step, and the thermal convection coefficient of the quenching medium.

Depending on the deformation of the part obtained at the start of machining, it will be possible to estimate the elongation value and the thermal convection value applied to the part and thus know the stress field of the part during manufacture (with the future IA model). In this way, it will be possible to modify the strategy at the end of the machining stage to minimize the final deformation of the part.

## Conclusions

In this work, a thermomechanical model was developed to simulate the manufacturing process of aluminum alloy laminated blanks on Abaqus software. Several numerical simulations, combining the different material parameters, have been carried out to determine the initial RS field's variability.

The results show that the variability of quenching temperature does not significantly influence the final stress field. On the other hand, the stretching step plays a crucial role in stress distribution. The thermal convection coefficient also influences the stress field, but to a much lesser extent. This highlights the need for meticulous control for these parameters to reduce undesirable stress variations.

Based on these results, variations in the RS field are now known as a function of operating parameters. This will make it possible to predict the distribution of the RS field for each part during machining, offering a proactive approach to prevent distortions.

## Acknowledgements

The authors acknowledge the funding of PERFORM Program of IRT Jules Verne.

## References

[1] Sim W-M (2010) Challenges of residual stress and part distortion in the civil airframe industry. International Journal of Microstructure and Materials Properties 5:446-455. https://doi.org/10.1504/IJMMP.2010.037621

[2] Li J, Wang S (2017) Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances. Int J Adv Manuf Technol 89:997-1012. https://doi.org/10.1007/s00170-016-9066-6

[3] Tang ZT, Yu T, Xu LQ, Liu ZQ (2013) Machining deformation prediction for frame components considering multifactor coupling effects. Int J Adv Manuf Technol 68:187-196. https://doi.org/10.1007/s00170-012-4718-7

[4] Jeanmart P, Bouvaist J (1985) Finite element calculation and measurement of thermal stresses in quenched plates of high-strength 7075 aluminium alloy. Materials Science and Technology 1:765-769. https://doi.org/10.1179/mst.1985.1.10.765

[5] Denkena B, De Leon L (2008) Milling induced residual stresses in structural parts out of forged aluminium alloys. International Journal of Machining and Machinability of Materials 4:335-344. https://doi.org/10.1504/IJMMM.2008.023717

[6] Chatelain J-F, Lalonde J-F, Tahan AS (2012) Effect of Residual Stresses Embedded within Workpieces on the Distortion of Parts after Machining. 6:

[7] Lequeu P, Lassince P, Warner T, Raynaud GM (2001) Engineering for the future: weight saving and cost reduction initiatives. Aircraft Engineering and Aerospace Technology 73:147-159. https://doi.org/10.1108/00022660110386663
[8] Virkar AV (1990) Determination of Residual Stress Profile Using a Strain Gage Technique. Journal of the American Ceramic Society 73:2100-2102. https://doi.org/10.1111/j.1151-2916.1990.tb05276.x

[9] Tandon R, Green DJ (1990) Residual Stress Determination Using Strain Gage Measurements - Tandon - 1990 - Journal of the American Ceramic Society - Wiley Online Library. https://ceramics.onlinelibrary.wiley.com/doi/abs/10.1111/j.1151-2916.1990.tb06738.x. Accessed 5 Feb 2024

[10] Wei Y, Wang XW (2007) Computer simulation and experimental study of machining deflection due to original residual stress of aerospace thin-walled parts. Int J Adv Manuf Technol 33:260-265. https://doi.org/10.1007/s00170-006-0470-1

[11] Cerutti X, Arsene S, Mocellin K (2015) Prediction of Machining Quality due to the Initial Residual Stress Redistribution of Aerospace Structural Parts Made of Low-Density Aluminium Alloy Rolled Plates. International Journal of Material Forming 9: https://doi.org/10.1007/s12289-015-1254-7

[12] Chabeauti H, Ritou M, Lavisse B, et al (2023) Digital twin of forged part to reduce distortion in machining. CIRP Annals 72:77-80. https://doi.org/10.1016/j.cirp.2023.04.021

[13] Lamba NK, Khobragade NW (2012) Uncoupled thermoelastic analysis for a thick cylinder with radiation. Theoretical and Applied Mechanics Letters 2:021005. https://doi.org/10.1063/2.1202105

[14] Louhichi MA, Poulachon G, Lorong P, et al (2022) Experimental and simulative determination of residual stress during heat treatment of 7075-T6 aluminum. Procedia CIRP 108:82-87. https://doi.org/10.1016/j.procir.2022.03.018
[8] Virkar AV (1990) Determination of Residual Stress Profile Using a Strain Gage Technique. Journal of the American Ceramic Society 73:2100-2102. https://doi.org/10.1111/j.1151-2916.1990.tb05276.x

[9] Tandon R, Green DJ (1990) Residual Stress Determination Using Strain Gage Measurements - Tandon - 1990 - Journal of the American Ceramic Society - Wiley Online Library. https://ceramics.onlinelibrary.wiley.com/doi/abs/10.1111/j.1151-2916.1990.tb06738.x. Accessed 5 Feb 2024

[10] Wei Y, Wang XW (2007) Computer simulation and experimental study of machining deflection due to original residual stress of aerospace thin-walled parts. Int J Adv Manuf Technol 33:260-265. https://doi.org/10.1007/s00170-006-0470-1

[11] Cerutti X, Arsene S, Mocellin K (2015) Prediction of Machining Quality due to the Initial Residual Stress Redistribution of Aerospace Structural Parts Made of Low-Density Aluminium Alloy Rolled Plates. International Journal of Material Forming 9: https://doi.org/10.1007/s12289-015-1254-7

[12] Chabeauti H, Ritou M, Lavisse B, et al (2023) Digital twin of forged part to reduce distortion in machining. CIRP Annals 72:77-80. https://doi.org/10.1016/j.cirp.2023.04.021

[13] Lamba NK, Khobragade NW (2012) Uncoupled thermoelastic analysis for a thick cylinder with radiation. Theoretical and Applied Mechanics Letters 2:021005. https://doi.org/10.1063/2.1202105

[14] Louhichi MA, Poulachon G, Lorong P, et al (2022) Experimental and simulative determination of residual stress during heat treatment of 7075-T6 aluminum. Procedia CIRP 108:82-87. https://doi.org/10.1016/j.procir.2022.03.018
# Microstructure refinement by a novel friction-based processing on Mg-Zn-Ca alloy

CHEN Ting¹,ᵃ*, FU Banglong¹,ᵇ, SHEN Junjun¹,ᶜ, SUHUDDIN Uceu F.H.R.¹,ᵈ, WIESE Björn²,ᵉ, DOS SANTOS Jorge F.³,ᵉ, BERGMANN Jean Pierre⁴,⁵, and KLUSEMANN Benjamin¹,⁵,ʰ

¹Solid State Materials Processing, Institute of Material Mechanics, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany

²Functional Magnesium Materials, Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany

³Applied Materials and Manufacturing, Energy and Environment Division, Pacific Northwest National Laboratory, PO Box 999, Richland, 99352 WA, USA

⁴Production Technology Group, Technische Universität Ilmenau, Gustav-Kirchhoff-Platz 2, 98693 Ilmenau, Germany

⁵Institute for Production Technology and Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany

**Keywords:** Constrained Friction Processing, Magnesium Alloys, Microstructure

**Abstract.** Insufficient mechanical properties and uncontrollable degradation rates limit the wide application of Mg alloys in bioimplant materials. Microstructure refinement is a common method to improve both the mechanical properties and the corrosion resistance of Mg alloys. In order to efficiently obtain Mg alloys with fine microstructures for potential applications in bioimplant materials, a novel constrained friction processing (CFP) was proposed. In this work, the resulting compression properties of ZX10 alloy obtained by CFP with optimized processing parameter are reported. Additionally, the microstructure evolution during CFP was studied. The results show that during CFP, materials are subjected to high shear strain at the transition zone between the stir zone and thermo-mechanical affected zone, leading to recrystallization with strong local basal fiber shear texture. As the shoulder plunges down, the fraction of recrystallized grain and grain size increase. ZX10 alloy obtained by CFP exhibited higher compressive yield strength by more than 300% and ultimate compressive strength improves by 60%, which indicates the bright prospect of CFP for Mg processing.

**Introduction**

Magnesium (Mg) alloys represent a prominent choice for biodegradable implants owing to their notable biocompatibility and biodegradability characteristics. The density and elastic modulus of Mg alloys closely resemble those of natural bones, thereby mitigating mechanical mismatches between the implant and bone in comparison to alternative alloys. Moreover, magnesium constitutes an essential element in the human body, and its alloys possess inherent biodegradability, obviating the necessity for subsequent surgical interventions. Within medical contexts, Mg alloys predominantly find utility as scaffolds for tissue engineering, implants for bone repair, and stents for cardiovascular applications.

However, Mg alloys with innocuous chemical compositions typically exhibit inadequate strength and corrosion resistance, which limits their use as biodegradable implants [1]. One

<figure><img src="image_1920.png" /></figure>

<footnote>ᵃting.chen@hereon.de, ᵇbanglong.fu@hereon.de, ᶜjunjun.shen@hereon.de,</footnote>

<footnote>ᵈuceu.suhuddin@hereon.de, ᶜbjoern.wiese@hereon.de, ᵈjorge.dos.santos@hereon.de,</footnote>

<footnote n="9">⁹jeanpierre.bergmann@tu-ilmenau.de, ᵇbenjamin.klusemann@hereon.de</footnote>
# Microstructure refinement by a novel friction-based processing on Mg-Zn-Ca alloy

CHEN Ting<fnref n="1" />,ᵃ*, FU Banglong<fnref n="1" />,ᵇ, SHEN Junjun<fnref n="1" />,ᶜ, SUHUDDIN Uceu F.H.R.<fnref n="1" />,ᵈ, WIESE Björn<fnref n="2" />,ᵉ, DOS SANTOS Jorge F.<fnref n="3" />,ᵉ, BERGMANN Jean Pierre<fnref n="4" />,<fnref n="5" />, and KLUSEMANN Benjamin<fnref n="1" />,<fnref n="5" />,ʰ

**Keywords:** Constrained Friction Processing, Magnesium Alloys, Microstructure

**Abstract.** Insufficient mechanical properties and uncontrollable degradation rates limit the wide application of Mg alloys in bioimplant materials. Microstructure refinement is a common method to improve both the mechanical properties and the corrosion resistance of Mg alloys. In order to efficiently obtain Mg alloys with fine microstructures for potential applications in bioimplant materials, a novel constrained friction processing (CFP) was proposed. In this work, the resulting compression properties of ZX10 alloy obtained by CFP with optimized processing parameter are reported. Additionally, the microstructure evolution during CFP was studied. The results show that during CFP, materials are subjected to high shear strain at the transition zone between the stir zone and thermo-mechanical affected zone, leading to recrystallization with strong local basal fiber shear texture. As the shoulder plunges down, the fraction of recrystallized grain and grain size increase. ZX10 alloy obtained by CFP exhibited higher compressive yield strength by more than 300% and ultimate compressive strength improves by 60%, which indicates the bright prospect of CFP for Mg processing.

**Introduction**

Magnesium (Mg) alloys represent a prominent choice for biodegradable implants owing to their notable biocompatibility and biodegradability characteristics. The density and elastic modulus of Mg alloys closely resemble those of natural bones, thereby mitigating mechanical mismatches between the implant and bone in comparison to alternative alloys. Moreover, magnesium constitutes an essential element in the human body, and its alloys possess inherent biodegradability, obviating the necessity for subsequent surgical interventions. Within medical contexts, Mg alloys predominantly find utility as scaffolds for tissue engineering, implants for bone repair, and stents for cardiovascular applications.

However, Mg alloys with innocuous chemical compositions typically exhibit inadequate strength and corrosion resistance, which limits their use as biodegradable implants [1]. One

<figure><img src="image_1915.png" /></figure>

<footnote n="1">¹Solid State Materials Processing, Institute of Material Mechanics, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany</footnote>

<footnote n="2">²Functional Magnesium Materials, Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany</footnote>

<footnote n="3">³Applied Materials and Manufacturing, Energy and Environment Division, Pacific Northwest National Laboratory, PO Box 999, Richland, 99352 WA, USA</footnote>

<footnote n="4">⁴Production Technology Group, Technische Universität Ilmenau, Gustav-Kirchhoff-Platz 2, 98693 Ilmenau, Germany</footnote>

<footnote n="5">⁵Institute for Production Technology and Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany</footnote>

<footnote>ᵃting.chen@hereon.de, ᵇbanglong.fu@hereon.de, ᶜjunjun.shen@hereon.de,</footnote>

<footnote>ᵈuceu.suhuddin@hereon.de, ᶜbjoern.wiese@hereon.de, ᵈjorge.dos.santos@hereon.de,</footnote>

<footnote n="9">⁹jeanpierre.bergmann@tu-ilmenau.de, ᵇbenjamin.klusemann@hereon.de</footnote>
solution is to modify the microstructure of the alloys. Modifying the microstructure to achieve a finer grain size and secondary phases is beneficial for improving both mechanical properties and biocorrosion resistance [2]. Thermo-mechanical processing (TMP) methods, such as hot rolling [3] and hot extrusion [4], have been adapted to refine grains and secondary phases of as-cast Mg alloys. Additionally, severe plastic deformation (SPD) processing, such as equal-channel angular pressing (ECAP) [5] and high-pressure torsion (HPT) [6], have been developed to achieve a much finer microstructure.

While TMP and SPD have been shown to refine the microstructure and have potential for use in processing biodegradable Mg alloys with sufficient mechanical properties and corrosion resistance, there are still limitations. The microstructure resulting from a single TMP or SPD method is typically inhomogeneous, exhibiting shear zones [7], large deformed grains [8], and concentration of secondary phases [9]. This is due to insufficient or inhomogeneous deformation, which can result in weak mechanical strength and inhomogeneous corrosion properties. Therefore, these methods usually require multiple passes [10] or cooperation with long-time pre-heat treatment to achieve a homogeneous microstructure [11]. Additionally, samples obtained with certain SPD methods, such as HPT, may have limited dimensions.

The present work employes a new friction-based processing method, called constrained friction processing (CFP) [12,13]. This method has several advantages over other methods mentioned above. Firstly, it does not require external heating as the heat is generated by the friction between the tool and materials as well as plastic deformation. Secondly, it subjects materials to high strain and strain rates, and the optimized processing time is much shorter (approximately 3 seconds). Materials are stirred thoroughly and homogeneously, so additional pre-heating is unnecessary to homogenize the microstructure beforehand. The primary objective of this study is to fabricate defect-free, fine-grained Mg alloys with superior mechanical properties from a large-grained state of as-cast ZX10 using CFP for potential applications as bioimplant materials. The overall microstructure evolution was analyzed using the “stop-action” technique.

## Materials and Method

The materials used in this work were conventional chill-casting Mg-1Zn-0.3Ca (wt.%) alloy (ZX10), with higher Zn content than ZX00, which has been reported in [13]. The average grain size of as-cast ZX10 alloys is more than 1 mm, see Fig. 1(a). The ZX10 ingot was cut into specimens with a dimension of 100 × 25 × 6 mm³, and then cleaned by alcohol prior to processing.

The novel CFP is shown in Fig. 1. A tool system consists of a clamping ring, a shoulder and a probe. First of all, the specimen is compressed by the clamping ring to the backing. Then the shoulder rotates and plunges down into the material, and at the same time, the probe rotates and retracts. The friction between the rotating tool and material generates heat to plasticize the material. Finally, when the shoulder reaches the set depth, the tool system stops rotating and is removed from the material. After processing, a rod with the same diameter as the probe is obtained.
solution is to modify the microstructure of the alloys. Modifying the microstructure to achieve a finer grain size and secondary phases is beneficial for improving both mechanical properties and biocorrosion resistance [2]. Thermo-mechanical processing (TMP) methods, such as hot rolling [3] and hot extrusion [4], have been adapted to refine grains and secondary phases of as-cast Mg alloys. Additionally, severe plastic deformation (SPD) processing, such as equal-channel angular pressing (ECAP) [5] and high-pressure torsion (HPT) [6], have been developed to achieve a much finer microstructure.

While TMP and SPD have been shown to refine the microstructure and have potential for use in processing biodegradable Mg alloys with sufficient mechanical properties and corrosion resistance, there are still limitations. The microstructure resulting from a single TMP or SPD method is typically inhomogeneous, exhibiting shear zones [7], large deformed grains [8], and concentration of secondary phases [9]. This is due to insufficient or inhomogeneous deformation, which can result in weak mechanical strength and inhomogeneous corrosion properties. Therefore, these methods usually require multiple passes [10] or cooperation with long-time pre-heat treatment to achieve a homogeneous microstructure [11]. Additionally, samples obtained with certain SPD methods, such as HPT, may have limited dimensions.

The present work employes a new friction-based processing method, called constrained friction processing (CFP) [12,13]. This method has several advantages over other methods mentioned above. Firstly, it does not require external heating as the heat is generated by the friction between the tool and materials as well as plastic deformation. Secondly, it subjects materials to high strain and strain rates, and the optimized processing time is much shorter (approximately 3 seconds). Materials are stirred thoroughly and homogeneously, so additional pre-heating is unnecessary to homogenize the microstructure beforehand. The primary objective of this study is to fabricate defect-free, fine-grained Mg alloys with superior mechanical properties from a large-grained state of as-cast ZX10 using CFP for potential applications as bioimplant materials. The overall microstructure evolution was analyzed using the “stop-action” technique.

## Materials and Method

The materials used in this work were conventional chill-casting Mg-1Zn-0.3Ca (wt.%) alloy (ZX10), with higher Zn content than ZX00, which has been reported in [13]. The average grain size of as-cast ZX10 alloys is more than 1 mm, see Fig. 1(a). The ZX10 ingot was cut into specimens with a dimension of 100 × 25 × 6 mm³, and then cleaned by alcohol prior to processing.

The novel CFP is shown in Fig. 1. A tool system consists of a clamping ring, a shoulder and a probe. First of all, the specimen is compressed by the clamping ring to the backing. Then the shoulder rotates and plunges down into the material, and at the same time, the probe rotates and retracts. The friction between the rotating tool and material generates heat to plasticize the material. Finally, when the shoulder reaches the set depth, the tool system stops rotating and is removed from the material. After processing, a rod with the same diameter as the probe is obtained.
<figure><img src="image_1921.png" /><figcaption>Fig. 1 (a)Microstructure and (b-d) Schematic drawing of procedure of CFP</figcaption></figure>

In this work, the processing was performed on the refill FSSW machine RPS200 produced by Harms & Wende. The diameter of the clamping ring, shoulder and probe were 17 mm, 9 mm and 6 mm, respectively. The processing force was set as 12 kN. The maximum plunge depth of the shoulder was set as 3 mm, and the total plunge time was 3 s, resulting in an average pungle speed of 1 mm/s. To keep the volume of materials balanced, the retraction speed of the probe was set as 1.25 times plunge speed of the shoulder. The rotation speed of the shoulder and probe was the same, keeping 1800 rpm during processing.

Specimens were machined to a cylindrical sample with a diameter of 6 mm and a height of 6 mm for compression tests. These were carried out at room temperature using a Zwick/Roell 1478 universal testing machine at a strain rate of 10⁻³ s⁻¹. Compressive yield strength, ultimate compressive strength and elongation were evaluated by the average of three specimens per processing parameter.

In order to characterize the microstructure of the rod obtained by CFP, the longitudinal-section of the rod was ground and polished according to standard metallographic sample preparation. Then, the sample was electricity polished using AC2 solution using voltage 25 V at -20 °C for 35 s. An FEI Quanta 650 field emission gun (FEG) SEM equipped with electron backscattered diffraction (EBSD) detectors was used to further analyze the microstructure. The data from EBSD were analyzed by TSL OIM 7.3, respectively. The volume fraction of recrystallized grain was calculated according to Grain Orientation Spread (GOS) obtained by EBSD. The grains with GOS lower than 2° are defined as recrystallized grains, while those with GOS higher than 2° are deformed grains.

# Results

## Microstructure

Fig. 2 displays the microstructure of various subregions in the ZX10 rod obtained through CFP with moderate heat input at a rotation speed of 1800 rpm and a plunge speed of 1 mm/s. The average grain size after CFP decreased significantly compared to the as-casted ZX10 alloy, being the primary reason for the improvement in compression strength. The rod's average grain size is
<figure><img src="image_1916.png" /><figcaption>Fig. 1 (a)Microstructure and (b-d) Schematic drawing of procedure of CFP</figcaption></figure>

In this work, the processing was performed on the refill FSSW machine RPS200 produced by Harms & Wende. The diameter of the clamping ring, shoulder and probe were 17 mm, 9 mm and 6 mm, respectively. The processing force was set as 12 kN. The maximum plunge depth of the shoulder was set as 3 mm, and the total plunge time was 3 s, resulting in an average pungle speed of 1 mm/s. To keep the volume of materials balanced, the retraction speed of the probe was set as 1.25 times plunge speed of the shoulder. The rotation speed of the shoulder and probe was the same, keeping 1800 rpm during processing.

Specimens were machined to a cylindrical sample with a diameter of 6 mm and a height of 6 mm for compression tests. These were carried out at room temperature using a Zwick/Roell 1478 universal testing machine at a strain rate of 10⁻³ s⁻¹. Compressive yield strength, ultimate compressive strength and elongation were evaluated by the average of three specimens per processing parameter.

In order to characterize the microstructure of the rod obtained by CFP, the longitudinal-section of the rod was ground and polished according to standard metallographic sample preparation. Then, the sample was electricity polished using AC2 solution using voltage 25 V at -20 °C for 35 s. An FEI Quanta 650 field emission gun (FEG) SEM equipped with electron backscattered diffraction (EBSD) detectors was used to further analyze the microstructure. The data from EBSD were analyzed by TSL OIM 7.3, respectively. The volume fraction of recrystallized grain was calculated according to Grain Orientation Spread (GOS) obtained by EBSD. The grains with GOS lower than 2° are defined as recrystallized grains, while those with GOS higher than 2° are deformed grains.

# Results

## Microstructure

Fig. 2 displays the microstructure of various subregions in the ZX10 rod obtained through CFP with moderate heat input at a rotation speed of 1800 rpm and a plunge speed of 1 mm/s. The average grain size after CFP decreased significantly compared to the as-casted ZX10 alloy, being the primary reason for the improvement in compression strength. The rod's average grain size is
approximately 5-6 µm, as shown in the IPF images in Fig. 2. The grain size in the horizontal direction is similar from the axis center to the edge.

<figure><img src="image_1922.png" /><figcaption>Fig. 2 Inverse pole figures (IPF) of ZX10 obtained by CFP with rotation speed of 1800 rpm and plunge speed of 1 mm/s corresponding to the position marked by blue rectangle. The average grain size is labeled at the lower right corner.</figcaption></figure>

The [0002] pole figures (PFs) in Fig. 3 show the analyzed textures of the observed subregions. The basal pole maximum intensity is high, ranging from 25 to 97 multiples of a random distribution (mrd), indicating a strong basal texture. The basal pole peak near the axis center of the CFP rod is oriented parallel to the ED direction. As the distance from the axis center increases, the angle between the basal pole peak and ED also increases, resulting in a dome-shaped basal plane with a gradient [0002] direction in the global rod. The gradient basal texture of the CFP Mg rod is unique
approximately 5-6 µm, as shown in the IPF images in Fig. 2. The grain size in the horizontal direction is similar from the axis center to the edge.

<figure><img src="image_1917.png" /><figcaption>Fig. 2 Inverse pole figures (IPF) of ZX10 obtained by CFP with rotation speed of 1800 rpm and plunge speed of 1 mm/s corresponding to the position marked by blue rectangle. The average grain size is labeled at the lower right corner.</figcaption></figure>

The [0002] pole figures (PFs) in Fig. 3 show the analyzed textures of the observed subregions. The basal pole maximum intensity is high, ranging from 25 to 97 multiples of a random distribution (mrd), indicating a strong basal texture. The basal pole peak near the axis center of the CFP rod is oriented parallel to the ED direction. As the distance from the axis center increases, the angle between the basal pole peak and ED also increases, resulting in a dome-shaped basal plane with a gradient [0002] direction in the global rod. The gradient basal texture of the CFP Mg rod is unique
compared to other processing methods such as hot extrusion and ECAP, and requires further
investigation.

<figure><img src="image_1923.png" /><figcaption>Fig. 3 [0002] pole figures (PF) with texture intensity of ZX10 obtained by CFP with rotation speed of 1800 rpm and plunge speed of 1 mm/s corresponded to the position marked by blue rectangle.</figcaption></figure>

Mechanical Properties

Fig. 4 compares the compression properties of as-cast ZX10 and CFP ZX10 alloys obtained with rotation speeds of 1800 rpm and a plunge speed of 1 mm/s. The compressive yield strength of the
compared to other processing methods such as hot extrusion and ECAP, and requires further investigation.

<figure><img src="image_1918.png" /><figcaption>Fig. 3 [0002] pole figures (PF) with texture intensity of ZX10 obtained by CFP with rotation speed of 1800 rpm and plunge speed of 1 mm/s corresponded to the position marked by blue rectangle.</figcaption></figure>

## Mechanical Properties

Fig. 4 compares the compression properties of as-cast ZX10 and CFP ZX10 alloys obtained with rotation speeds of 1800 rpm and a plunge speed of 1 mm/s. The compressive yield strength of the
as-cast ZX10 alloy is significantly improved by CFP, increasing from approximately 50 MPa to about 200 MPa, an increase of over 300%. Similarly, the ultimate compressive strength is improved from approximately 208 MPa to about 334 MPa, an increase of about 60%. However, the elongation decreases. The improved compressive properties after CFP are related to the effectively refined grains. The results indicate that CFP is highly competitive in achieving excellent mechanical properties, meeting the design criteria for biodegradable bone-fixation devices proposed by Erinc et al [14]. This reflects the high potential of CFP in processing biomedical Mg alloys. Compared to the CFP ZX00 alloy with lower Zn content as reported in [13], the CFP ZX10 alloy demonstrated higher compression yield strength, increasing from approximately 180 MPa to 200 MPa, as well as ultimate compression strength, rising from around 290 MPa to 334 MPa. This enhancement can likely be attributed to solid solution strengthening resulting from the higher Zn content.

<figure><img src="image_1924.png" /><figcaption>Fig. 4 (a) Compressive properties and (b) stress-strain curves of as-cast ZX10 alloy and CFP ZX10 alloy, produced at plunge speed of 1 mm/s and rotation speeds of 1800 rpm.</figcaption></figure>

## Discussion

### Microstructure evolution ahead of stir zone

To analyze the evolution of microstructure during CFP, the “stop-action” technique was employed [15]. The CFP machine was stopped at a plunge depth of 2 mm via pressing the emergency button during processing, and the material was cooled using an ice-water mixture to “freeze” the microstructure and prevent subsequent static recrystallization and grain growth. The “frozen” transition zone from the thermo-mechanically affected zone (TMAZ) to the stir zone (SZ) was detected using EBSD, as shown in Fig. 5.

Firstly, Mg alloys were subjected to high compression stress from the shoulder, resulting in a large number of twins forming in the original large as-cast grains, as shown in Fig. 5. In this region, extensive twinning (T1 and T2) with the same [1120] direction as the parent grain (P) was observed, and the high-angle twinning boundaries provided more nucleation sites for subsequent recrystallization [16]. For the parent grain in Fig. 5, the original basal plane was nearly perpendicular to the shear plane, i.e. the interface between SZ and TMAZ. As it approaches the SZ, which was subjected to more severe shear stress and higher temperature, pyramidal and prismatic slip become much easier to activate, and Mg exhibited higher plasticity [17]. Under the influence of shear stress, the grain lattice and basal plane rotated, leading to the formation of high grain boundary and continuous dynamic recrystallized (CDRXed) grains in the SZ with similar c-axis and strong basal fiber texture, whose basal plane is parallel to the shear plane.
as-cast ZX10 alloy is significantly improved by CFP, increasing from approximately 50 MPa to about 200 MPa, an increase of over 300%. Similarly, the ultimate compressive strength is improved from approximately 208 MPa to about 334 MPa, an increase of about 60%. However, the elongation decreases. The improved compressive properties after CFP are related to the effectively refined grains. The results indicate that CFP is highly competitive in achieving excellent mechanical properties, meeting the design criteria for biodegradable bone-fixation devices proposed by Erinc et al [14]. This reflects the high potential of CFP in processing biomedical Mg alloys. Compared to the CFP ZX00 alloy with lower Zn content as reported in [13], the CFP ZX10 alloy demonstrated higher compression yield strength, increasing from approximately 180 MPa to 200 MPa, as well as ultimate compression strength, rising from around 290 MPa to 334 MPa. This enhancement can likely be attributed to solid solution strengthening resulting from the higher Zn content.

<figure><img src="image_1919.png" /><figcaption>Fig. 4 (a) Compressive properties and (b) stress-strain curves of as-cast ZX10 alloy and CFP ZX10 alloy, produced at plunge speed of 1 mm/s and rotation speeds of 1800 rpm.</figcaption></figure>

## Discussion

### Microstructure evolution ahead of stir zone

To analyze the evolution of microstructure during CFP, the “stop-action” technique was employed [15]. The CFP machine was stopped at a plunge depth of 2 mm via pressing the emergency button during processing, and the material was cooled using an ice-water mixture to “freeze” the microstructure and prevent subsequent static recrystallization and grain growth. The “frozen” transition zone from the thermo-mechanically affected zone (TMAZ) to the stir zone (SZ) was detected using EBSD, as shown in Fig. 5.

Firstly, Mg alloys were subjected to high compression stress from the shoulder, resulting in a large number of twins forming in the original large as-cast grains, as shown in Fig. 5. In this region, extensive twinning (T1 and T2) with the same [1120] direction as the parent grain (P) was observed, and the high-angle twinning boundaries provided more nucleation sites for subsequent recrystallization [16]. For the parent grain in Fig. 5, the original basal plane was nearly perpendicular to the shear plane, i.e. the interface between SZ and TMAZ. As it approaches the SZ, which was subjected to more severe shear stress and higher temperature, pyramidal and prismatic slip become much easier to activate, and Mg exhibited higher plasticity [17]. Under the influence of shear stress, the grain lattice and basal plane rotated, leading to the formation of high grain boundary and continuous dynamic recrystallized (CDRXed) grains in the SZ with similar c-axis and strong basal fiber texture, whose basal plane is parallel to the shear plane.
<figure><img src="image_1925.png" /><figcaption>Fig. 5 Microstructure of transition zone between SZ and TMAZ obtained with "stop-action" technique.</figcaption></figure>

Microstructure evolution in stir zone

Fig. 6 displays the GOS maps of different subregions in a CFP rod and the volume fraction of grains with different GOS ranges. From the bottom to the top of the CFP rod, the proportion of recrystallized grains with GOS lower than 2° increased. The bottom of SZ was subjected to severe shear strain, so the proportion of recrystallized grains is low. As the shoulder plunged down, the shear strain rate decreased, allowing more time for grains at upper part to recover and grow at high temperature. This results in an increase in the fraction of recrystallized grains as well as grain size from the bottom, see Fig. 2 and Fig. 5. From axis center to the edge of the CFP rod, the proportion of recrystallized grains decreases due to larger rotation radius and higher shear strain rate.
<figure><img src="image_1920.png" /><figcaption>Fig. 5 Microstructure of transition zone between SZ and TMAZ obtained with "stop-action" technique.</figcaption></figure>

Microstructure evolution in stir zone

Fig. 6 displays the GOS maps of different subregions in a CFP rod and the volume fraction of grains with different GOS ranges. From the bottom to the top of the CFP rod, the proportion of recrystallized grains with GOS lower than 2° increased. The bottom of SZ was subjected to severe shear strain, so the proportion of recrystallized grains is low. As the shoulder plunged down, the shear strain rate decreased, allowing more time for grains at upper part to recover and grow at high temperature. This results in an increase in the fraction of recrystallized grains as well as grain size from the bottom, see Fig. 2 and Fig. 5. From axis center to the edge of the CFP rod, the proportion of recrystallized grains decreases due to larger rotation radius and higher shear strain rate.
<figure><img src="image_1926.png" /><figcaption>Fig. 6 GOS maps corresponding to different subregion in Fig. 2. Volume fraction of recrystallized grains (GOS < 2°) is labeled at the lower right corner.</figcaption></figure>

**Summary**

In this study, the novel constrained friction processing was utilized to refine the microstructure of the ZX10 alloy and improve its mechanical properties. The microstructure evolution was discussed. The following conclusions were obtained:

(1) Defect-free ZX10 rods with grain size below 6 µm can be obtained using CFP with moderate heat input in a short time (3 s) from as-cast state with large grain size greater than 1 mm. The compression yield strength of ZX10 increased by more than 300% and the ultimate compression by 60% after CFP, which also higher than those of CFP ZX00.
<figure><img src="image_1921.png" /><figcaption>Fig. 6 GOS maps corresponding to different subregion in Fig. 2. Volume fraction of recrystallized grains (GOS < 2°) is labeled at the lower right corner.</figcaption></figure>

**Summary**

In this study, the novel constrained friction processing was utilized to refine the microstructure of the ZX10 alloy and improve its mechanical properties. The microstructure evolution was discussed. The following conclusions were obtained:

(1) Defect-free ZX10 rods with grain size below 6 µm can be obtained using CFP with moderate heat input in a short time (3 s) from as-cast state with large grain size greater than 1 mm. The compression yield strength of ZX10 increased by more than 300% and the ultimate compression by 60% after CFP, which also higher than those of CFP ZX00.
(2) From the base materials to the TMAZ until SZ, microstructure experiences twinning and CDRX, resulting in strong basal fiber texture. In the processed rods, microstructure exhibits continuous development of DRX combined with grain recovery and growth.

## Acknowledgements

Mr. Ting Chen thanks the China Scholarship Council for the award of fellowship and funding (No. 202006230137). The authors are grateful to Mr. Günter Meister and Mr. Gert Wiese from Helmholtz-Zentrum Hereon, Institute of Metallic Biomaterials, for the provision of the base materials used in this study and the guidance of the metallographic preparation. The technical support of Mr. Menno Peters and Ms. Camila Caroline de Castro, from Helmholtz-Zentrum Hereon, Institute of Materials Mechanics during this work is gratefully acknowledged.

## References

[1] A. Bahmani, M. Lotfpour, M. Taghizadeh, W.J. Kim, Corrosion behavior of severely plastically deformed Mg and Mg alloys, J. Magnes. Alloy 10(10) (2022) 2607-2648.
https://doi.org/10.1016/j.jma.2022.09.007

[2] Z. Savaedi, H. Mirzadeh, R.M. Aghdam, R. Mahmudi, Effect of grain size on the mechanical properties and bio-corrosion resistance of pure magnesium, J. Mater. Res. Technol. 19 (2022) 3100-3109.
https://doi.org/10.1016/j.jmrt.2022.06.048

[3] F.Y. Cao, Z.M. Shi, G.L. Song, M. Liu, M.S. Dargusch, A. Atrens, Influence of hot rolling on the corrosion behavior of several Mg-X alloys, Corros. Sci. 90 (2015) 176-191.
https://doi.org/10.1016/j.corsci.2014.10.012

[4] V. Bazhenov, A. Li, A. Komissarov, A. Koltygin, S. Tavolzhanskii, V. Bautin, O. Voropaeva, A. Mukhametshina, A. Tokar, Microstructure and mechanical and corrosion properties of hot-extruded Mg-Zn-Ca-(Mn) biodegradable alloys, J. Magnes. Alloy 9(4) (2021) 1428-1442.
https://doi.org/10.1016/j.jma.2020.11.008

[5] E. Mostaed, M. Vedani, M. Hashempour, M. Bestetti, Influence of ECAP process on mechanical and corrosion properties of pure Mg and ZK60 magnesium alloy for biodegradable stent applications, Biomatter 4(1) (2014) e28283.
https://doi.org/10.4161/biom.28283

[6] C. Zhang, S. Zhu, L. Wang, R. Guo, G. Yue, S. Guan, Microstructures and degradation mechanism in simulated body fluid of biomedical Mg-Zn-Ca alloy processed by high pressure torsion, Mater. Des. 96 (2016) 54-62.
https://doi.org/10.1016/j.matdes.2016.01.072

[7] M. Vaughan, A. Karayan, A. Srivastava, B. Mansoor, J. Seitz, R. Eifler, I. Karaman, H. Castaneda, H. Maier, The effects of severe plastic deformation on the mechanical and corrosion characteristics of a bioresorbable Mg-ZKQX6000 alloy, Mater. Sci. Eng. C 115 (2020) 111130.
https://doi.org/10.1016/j.msec.2020.111130

[8] X. Zhang, Z. Wang, G. Yuan, Y. Xue, Improvement of mechanical properties and corrosion resistance of biodegradable Mg-Nd-Zn-Zr alloys by double extrusion, Mater. Sci. Eng. B 177(13) (2012) 1113-1119.
https://doi.org/10.1016/j.mseb.2012.05.020

[9] Z.J. Yu, C. Xu, J. Meng, X.H. Zhang, S. Kamado, Effects of pre-annealing on microstructure and mechanical properties of as-extruded Mg-Gd-Y-Zn-Zr alloy, J. Alloys Compd. 729 (2017) 627-637.
https://doi.org/10.1016/j.jallcom.2017.09.214

[10] S. Prithivirajan, S. Narendranath, V. Desai, Analysing the combined effect of crystallographic orientation and grain refinement on mechanical properties and corrosion behaviour of ECAPed ZE41 Mg alloy, J. Magnes. Alloy 8(4) (2020) 1128-1143.
https://doi.org/10.1016/j.jma.2020.08.015
(2) From the base materials to the TMAZ until SZ, microstructure experiences twinning and CDRX, resulting in strong basal fiber texture. In the processed rods, microstructure exhibits continuous development of DRX combined with grain recovery and growth.

### Acknowledgements

Mr. Ting Chen thanks the China Scholarship Council for the award of fellowship and funding (No. 202006230137). The authors are grateful to Mr. Günter Meister and Mr. Gert Wiese from Helmholtz-Zentrum Hereon, Institute of Metallic Biomaterials, for the provision of the base materials used in this study and the guidance of the metallographic preparation. The technical support of Mr. Menno Peters and Ms. Camila Caroline de Castro, from Helmholtz-Zentrum Hereon, Institute of Materials Mechanics during this work is gratefully acknowledged.

### References

[1] A. Bahmani, M. Lotfpour, M. Taghizadeh, W.J. Kim, Corrosion behavior of severely plastically deformed Mg and Mg alloys, J. Magnes. Alloy 10(10) (2022) 2607-2648.
https://doi.org/10.1016/j.jma.2022.09.007

[2] Z. Savaedi, H. Mirzadeh, R.M. Aghdam, R. Mahmudi, Effect of grain size on the mechanical properties and bio-corrosion resistance of pure magnesium, J. Mater. Res. Technol. 19 (2022) 3100-3109.
https://doi.org/10.1016/j.jmrt.2022.06.048

[3] F.Y. Cao, Z.M. Shi, G.L. Song, M. Liu, M.S. Dargusch, A. Atrens, Influence of hot rolling on the corrosion behavior of several Mg-X alloys, Corros. Sci. 90 (2015) 176-191.
https://doi.org/10.1016/j.corsci.2014.10.012

[4] V. Bazhenov, A. Li, A. Komissarov, A. Koltygin, S. Tavolzhanskii, V. Bautin, O. Voropaeva, A. Mukhametshina, A. Tokar, Microstructure and mechanical and corrosion properties of hot-extruded Mg-Zn-Ca-(Mn) biodegradable alloys, J. Magnes. Alloy 9(4) (2021) 1428-1442.
https://doi.org/10.1016/j.jma.2020.11.008

[5] E. Mostaed, M. Vedani, M. Hashempour, M. Bestetti, Influence of ECAP process on mechanical and corrosion properties of pure Mg and ZK60 magnesium alloy for biodegradable stent applications, Biomatter 4(1) (2014) e28283.
https://doi.org/10.4161/biom.28283

[6] C. Zhang, S. Zhu, L. Wang, R. Guo, G. Yue, S. Guan, Microstructures and degradation mechanism in simulated body fluid of biomedical Mg-Zn-Ca alloy processed by high pressure torsion, Mater. Des. 96 (2016) 54-62.
https://doi.org/10.1016/j.matdes.2016.01.072

[7] M. Vaughan, A. Karayan, A. Srivastava, B. Mansoor, J. Seitz, R. Eifler, I. Karaman, H. Castaneda, H. Maier, The effects of severe plastic deformation on the mechanical and corrosion characteristics of a bioresorbable Mg-ZKQX6000 alloy, Mater. Sci. Eng. C 115 (2020) 111130.
https://doi.org/10.1016/j.msec.2020.111130

[8] X. Zhang, Z. Wang, G. Yuan, Y. Xue, Improvement of mechanical properties and corrosion resistance of biodegradable Mg-Nd-Zn-Zr alloys by double extrusion, Mater. Sci. Eng. B 177(13) (2012) 1113-1119.
https://doi.org/10.1016/j.mseb.2012.05.020

[9] Z.J. Yu, C. Xu, J. Meng, X.H. Zhang, S. Kamado, Effects of pre-annealing on microstructure and mechanical properties of as-extruded Mg-Gd-Y-Zn-Zr alloy, J. Alloys Compd. 729 (2017) 627-637.
https://doi.org/10.1016/j.jallcom.2017.09.214

[10] S. Prithivirajan, S. Narendranath, V. Desai, Analysing the combined effect of crystallographic orientation and grain refinement on mechanical properties and corrosion behaviour of ECAPed ZE41 Mg alloy, J. Magnes. Alloy 8(4) (2020) 1128-1143.
https://doi.org/10.1016/j.jma.2020.08.015
[11] C. Wang, A. Ma, J. Sun, H. Liu, H. Huang, Z. Yang, J. Jiang, Effect of ECAP process on as-cast and as-homogenized Mg-Al-Ca-Mn alloys with different Mg2Ca morphologies, J. Alloys Compd. 793 (2019) 259-270. https://doi.org/10.1016/j.jallcom.2019.04.202

[12] C.C. de Castro, J. Shen, J.F. dos Santos, B. Klusemann, Microstructural development of as-cast AM50 during Constrained Friction Processing: Grain refinement and influence of process parameters, J. Mater. Process. Technol. 318 (2023) 118018. https://doi.org/10.1016/j.jmatprotec.2023.118018

[13] T. Chen, B. Fu, U.F. Suhuddin, B. Wiese, Y. Huang, M. Wang, J.F. dos Santos, J.P. Bergmann, B. Klusemann. Application of novel constrained friction processing method to produce fine grained biomedical Mg-Zn-Ca alloy, J. Magnes. Alloy. https://doi.org/10.1016/j.jma.2023.10.007

[14] M. Erinc, W. Sillekens, R. Mannens, R. Werkhoven, Applicability of existing magnesium alloys as biomedical implant materials, in: E.A. Nyberg, S.R. Agnew, N.R. Neelameggham, M.Q. Pekguleryuz (Eds.), Magnesium Technology. San Francisco, Minerals, Metals and Materials Society, Warrendale, PA, 2009, pp. 209-214.

[15] P. Prangnell, C. Heason, Grain structure formation during friction stir welding observed by the ‘stop action technique’, Acta Mater. 53(11) (2005) 3179-3192. https://doi.org/10.1016/j.actamat.2005.03.044

[16] S. Lu, D. Wu, R. Chen, The effect of twinning on dynamic recrystallization behavior of Mg-Gd-Y alloy during hot compression, J. Alloys Compd. 803 (2019) 277-290. https://doi.org/10.1016/j.jallcom.2019.06.279

[17] S. Sandlöbes, M. Friák, J. Neugebauer, D. Raabe, Basal and non-basal dislocation slip in Mg-Y, Mater. Sci. Eng. A 576 (2013) 61-68. https://doi.org/10.1016/j.msea.2013.03.006
[11] C. Wang, A. Ma, J. Sun, H. Liu, H. Huang, Z. Yang, J. Jiang, Effect of ECAP process on as-cast and as-homogenized Mg-Al-Ca-Mn alloys with different Mg2Ca morphologies, J. Alloys Compd. 793 (2019) 259-270. https://doi.org/10.1016/j.jallcom.2019.04.202

[12] C.C. de Castro, J. Shen, J.F. dos Santos, B. Klusemann, Microstructural development of as-cast AM50 during Constrained Friction Processing: Grain refinement and influence of process parameters, J. Mater. Process. Technol. 318 (2023) 118018. https://doi.org/10.1016/j.jmatprotec.2023.118018

[13] T. Chen, B. Fu, U.F. Suhuddin, B. Wiese, Y. Huang, M. Wang, J.F. dos Santos, J.P. Bergmann, B. Klusemann. Application of novel constrained friction processing method to produce fine grained biomedical Mg-Zn-Ca alloy, J. Magnes. Alloy. https://doi.org/10.1016/j.jma.2023.10.007

[14] M. Erinc, W. Sillekens, R. Mannens, R. Werkhoven, Applicability of existing magnesium alloys as biomedical implant materials, in: E.A. Nyberg, S.R. Agnew, N.R. Neelameggham, M.Q. Pekguleryuz (Eds.), Magnesium Technology. San Francisco, Minerals, Metals and Materials Society, Warrendale, PA, 2009, pp. 209-214.

[15] P. Prangnell, C. Heason, Grain structure formation during friction stir welding observed by the ‘stop action technique’, Acta Mater. 53(11) (2005) 3179-3192. https://doi.org/10.1016/j.actamat.2005.03.044

[16] S. Lu, D. Wu, R. Chen, The effect of twinning on dynamic recrystallization behavior of Mg-Gd-Y alloy during hot compression, J. Alloys Compd. 803 (2019) 277-290. https://doi.org/10.1016/j.jallcom.2019.06.279

[17] S. Sandlöbes, M. Friák, J. Neugebauer, D. Raabe, Basal and non-basal dislocation slip in Mg-Y, Mater. Sci. Eng. A 576 (2013) 61-68. https://doi.org/10.1016/j.msea.2013.03.006
# Numerical simulation of milling operations on flexible composite parts

NUTTE Matthias¹,ª*, RIVIÈRE-LORPHÈVRE Edouard¹, DAMBLY Valentin¹, ARRAZOLA Pedro-José², LAZOGLU Ismail³ and DUCOBU François¹

¹Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, UMONS, Belgium

²Mechanical and Manufacturing Department, Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragón, Spain

³Koc University Manufacturing and Automation Research Center, Istanbul 34450, Turkey

ªMatthias.NUTTE@umons.ac.be

**Keywords:** Machining, Milling, CFRP, Flexible Parts, Form Error

**Abstract.** Fiber-reinforced polymers (FRPs) are a widely used and growing material in industry, thanks to their excellent mechanical properties. Manufactured FRPs parts usually have thin walls. These parts also require finishing operations such as edge trimming. Problems like those encountered when machining thin metal parts are also encountered with FRPs: form error, chatter vibrations and poor surface finish. However, the study and numerical modelling of thin FRP parts are not well developed up to now. The aim of this paper is to demonstrate the feasibility of adapting a numerical model for metals to FRPs. The modelling of the shape error during the thinning of a CFRP (Carbon Fiber Reinforced Polymers) part is studied in this paper using a quasi-static analysis. Compared to metals, two adaptations are introduced here for the FRPs. First, the material properties are adapted from isotropic to orthotropic. Secondly, a mechanical model was applied to calculate cutting forces for FRPs. The results of the study show the feasibility of this adaptation and examination of form error in the case of FRPs.

**Introduction**

Fiber-reinforced polymers (FRPs) are excellent material owning a high strength to weight ratio and the performance of high toughness, good fatigue resistance and shock absorption, and strong designability [1]. However, FRPs forming techniques often result in the production of thin-walled parts. It is therefore important to consider the parts' flexibility when machining them, particularly when milling. On the one hand, the numerical simulation of flexible metal parts has been explored in numerous studies [2–4]. On the other hand, other studies have explored the numerical simulation of the machining of FRPs parts ([1] published a review on this subject in 2021). In addition, some articles, for example [5], have studied subjects already well known for metals, such as chattering, in the case of composites. However, to the authors' knowledge, no article has studied the numerical simulation of milling operations on flexible composite parts. Ciecielag et al. [6] studied the milling of flexible composite parts, but only experimentally.

To simulate the behaviour of a thin plate during milling, it is necessary to compute the cutting forces. There are 4 categories of models for calculating cutting forces in milling for unidirectional FRPs (UD-FRPs): mechanistic models, macro-mechanical models, micro-mechanical models, and numerical models. Mechanistic models are semi-empirical models. They are the most intuitive method to optimize cutting parameters and tool geometries, but they require a lot of time-consuming experiments to ensure the accuracy and applicability [7]. However, the calculation time after obtaining the cutting coefficients is short. Macro-mechanical models consider the material as an equivalent homogeneous material. They can help to optimize cutting parameters and to reduce

<figure><img src="image_1927.png" /></figure>
# Numerical simulation of milling operations on flexible composite parts

NUTTE Matthias¹,ᵃ*, RIVIÈRE-LORPHÈVRE Edouard¹, DAMBLY Valentin¹, ARRAZOLA Pedro-José², LAZOGLU Ismail³ and DUCOBU François¹

¹Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, UMONS, Belgium

²Mechanical and Manufacturing Department, Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragón, Spain

³Koc University Manufacturing and Automation Research Center, Istanbul 34450, Turkey

ªMatthias.NUTTE@umons.ac.be

**Keywords:** Machining, Milling, CFRP, Flexible Parts, Form Error

**Abstract.** Fiber-reinforced polymers (FRPs) are a widely used and growing material in industry, thanks to their excellent mechanical properties. Manufactured FRPs parts usually have thin walls. These parts also require finishing operations such as edge trimming. Problems like those encountered when machining thin metal parts are also encountered with FRPs: form error, chatter vibrations and poor surface finish. However, the study and numerical modelling of thin FRP parts are not well developed up to now. The aim of this paper is to demonstrate the feasibility of adapting a numerical model for metals to FRPs. The modelling of the shape error during the thinning of a CFRP (Carbon Fiber Reinforced Polymers) part is studied in this paper using a quasi-static analysis. Compared to metals, two adaptations are introduced here for the FRPs. First, the material properties are adapted from isotropic to orthotropic. Secondly, a mechanical model was applied to calculate cutting forces for FRPs. The results of the study show the feasibility of this adaptation and examination of form error in the case of FRPs.

**Introduction**

Fiber-reinforced polymers (FRPs) are excellent material owning a high strength to weight ratio and the performance of high toughness, good fatigue resistance and shock absorption, and strong designability [1]. However, FRPs forming techniques often result in the production of thin-walled parts. It is therefore important to consider the parts' flexibility when machining them, particularly when milling. On the one hand, the numerical simulation of flexible metal parts has been explored in numerous studies [2–4]. On the other hand, other studies have explored the numerical simulation of the machining of FRPs parts ([1] published a review on this subject in 2021). In addition, some articles, for example [5], have studied subjects already well known for metals, such as chattering, in the case of composites. However, to the authors' knowledge, no article has studied the numerical simulation of milling operations on flexible composite parts. Ciecielag et al. [6] studied the milling of flexible composite parts, but only experimentally.

To simulate the behaviour of a thin plate during milling, it is necessary to compute the cutting forces. There are 4 categories of models for calculating cutting forces in milling for unidirectional FRPs (UD-FRPs): mechanistic models, macro-mechanical models, micro-mechanical models, and numerical models. Mechanistic models are semi-empirical models. They are the most intuitive method to optimize cutting parameters and tool geometries, but they require a lot of time-consuming experiments to ensure the accuracy and applicability [7]. However, the calculation time after obtaining the cutting coefficients is short. Macro-mechanical models consider the material as an equivalent homogeneous material. They can help to optimize cutting parameters and to reduce

<figure><img src="image_1922.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
cutting forces as models can describe the interaction “cutting tool/FRP”, but they cannot describe the microchip formation process [7]. Micro-mechanical models consider the composite in all its details (fibres and matrix). Compared with macro-mechanical models and mechanistic models, micro-mechanical models can predict the cutting force by the physical essence of cutting of FRPs based on microscopic characteristics of FRPs, but the models need many parameters and details, which are difficult to be calibrated by measurements [7]. Thus, applications of these models have great limitations. Numerical models are mainly based on finite element methods (FEM) [8, 9] (discrete element models (DEM) do exist but are in the minority [10]). The main advantages of these models are that they save a lot of manpower and material resources, and that the cutting process can be described more accurately, but numerical models are limited by computer power and finite element theory [7]. Indeed, these models require more resources and computing time than the other models.

In this study, a quasi-static approach and a mechanistic model are used. This quasi-static approach is intended to deal with form error during chatter-free machining operations. It is used to compute form error (vibration problems are negligible). The dynamic system is considered as purely static to predict deformation during machining [4]. A quasi-static method is used to reduce calculation time compared with full dynamic simulation. To achieve this, the finite element modeling of the thin plate and the modeling of cutting forces for an orthotropic material such as FRPs need to be implemented. A mechanistic cutting forces model will be considered. The aim of this article is to demonstrate the feasibility of studying the form error in CFRPs milling process.

## Case study

The case studied is the lateral milling (up or down milling) of a thin unidirectional carbon fiber reinforced polymer (UD-CFRP) laminated plate. It is a plate thinning operation.

The plate, of thickness *e*, consists of a superposition of plies of thickness *e*<sub>p</sub>. Each ply is made up of unidirectional carbon fiber (represented in blue in Fig. 1) and of matrix in epoxy (surrounding the fibers). The fibers are oriented along the longitudinal axis *l*. The *t* axis is transverse to the fibers. The axes *l* and *t* form the local frame *lt*. The fiber orientation angle *ψ* is the angle between the local frame *lt* and the global frame *XZ* (Fig. 1). In this case, the fiber orientation angle *ψ* is 0°: the fibers are oriented horizontally. The local frames *lt* of the plies are therefore merged with the global frame *XZ*.

<figure><img src="image_1928.png" /><figcaption>Figure 1: UD-FRP ply, frame convention</figcaption></figure>
cutting forces as models can describe the interaction “cutting tool/FRP”, but they cannot describe the microchip formation process [7]. Micro-mechanical models consider the composite in all its details (fibres and matrix). Compared with macro-mechanical models and mechanistic models, micro-mechanical models can predict the cutting force by the physical essence of cutting of FRPs based on microscopic characteristics of FRPs, but the models need many parameters and details, which are difficult to be calibrated by measurements [7]. Thus, applications of these models have great limitations. Numerical models are mainly based on finite element methods (FEM) [8, 9] (discrete element models (DEM) do exist but are in the minority [10]). The main advantages of these models are that they save a lot of manpower and material resources, and that the cutting process can be described more accurately, but numerical models are limited by computer power and finite element theory [7]. Indeed, these models require more resources and computing time than the other models.

In this study, a quasi-static approach and a mechanistic model are used. This quasi-static approach is intended to deal with form error during chatter-free machining operations. It is used to compute form error (vibration problems are negligible). The dynamic system is considered as purely static to predict deformation during machining [4]. A quasi-static method is used to reduce calculation time compared with full dynamic simulation. To achieve this, the finite element modeling of the thin plate and the modeling of cutting forces for an orthotropic material such as FRPs need to be implemented. A mechanistic cutting forces model will be considered. The aim of this article is to demonstrate the feasibility of studying the form error in CFRPs milling process.

## Case study

The case studied is the lateral milling (up or down milling) of a thin unidirectional carbon fiber reinforced polymer (UD-CFRP) laminated plate. It is a plate thinning operation.

The plate, of thickness *e*, consists of a superposition of plies of thickness *e*<sub>p</sub>. Each ply is made up of unidirectional carbon fiber (represented in blue in Fig. 1) and of matrix in epoxy (surrounding the fibers). The fibers are oriented along the longitudinal axis *l*. The *t* axis is transverse to the fibers. The axes *l* and *t* form the local frame *lt*. The fiber orientation angle *ψ* is the angle between the local frame *lt* and the global frame *XZ* (Fig. 1). In this case, the fiber orientation angle *ψ* is 0°: the fibers are oriented horizontally. The local frames *lt* of the plies are therefore merged with the global frame *XZ*.

<figure><img src="image_1923.png" /><figcaption>Figure 1: UD-FRP ply, frame convention</figcaption></figure>
The plate, contained in the XZ plane, is made up of n plies of thickness $e_p$ (Fig. 2). A cross-section of the plate in the XY plane is shown in Fig. 3. Each ply (shown in a shade of grey in Fig. 2) has a carbon fiber (shown in blue in Fig. 2) oriented along the X axis (since $\psi = 0°$).

<figure><img src="image_1929.png" /><figcaption>Figure 2: Ply stacking and view in the XY plane of milling</figcaption></figure>

The feed direction is along the X axis (Fig. 3). The feed direction is therefore the same as the direction of orientation of the fibers. In other words, the cutter's axis is perpendicular to the fibers' axis for each ply. This is the major assumption of the developed model in link with the cutting forces calculation model. The plate is clamped by its base. The radial depth of cut $a_e$ is less than the thickness of the plate $e$.

<figure><img src="image_1930.png" /><figcaption>Figure 3: Milling of a thin CFRP laminated plate</figcaption></figure>

## Modelling of the milling process

The milling model for the case studied is based on a milling simulator for isotropic metallic thin plates. This simulator named DyStaMill was developed by Prof. Rivière-Lorphèvre and is presented in Huynh et al. [11]. It was therefore necessary to adapt this simulator for the case study: machining a thin composite plate. Two essential points had to be adapted:

1. The finite element thin plate model for an orthotropic material such as FRPs.

2. The cutting model for unidirectional composite milling.
The plate, contained in the XZ plane, is made up of n plies of thickness $e_p$ (Fig. 2). A cross-section of the plate in the XY plane is shown in Fig. 3. Each ply (shown in a shade of grey in Fig. 2) has a carbon fiber (shown in blue in Fig. 2) oriented along the X axis (since $\psi = 0^\circ$).

<figure><img src="image_1924.png" /><figcaption>Figure 2: Ply stacking and view in the XY plane of milling</figcaption></figure>

The feed direction is along the *X* axis (Fig. 3). The feed direction is therefore the same as the direction of orientation of the fibers. In other words, the cutter's axis is perpendicular to the fibers' axis for each ply. This is the major assumption of the developed model in link with the cutting forces calculation model. The plate is clamped by its base. The radial depth of cut $a_e$ is less than the thickness of the plate *e*.

<figure><img src="image_1925.png" /><figcaption>Figure 3: Milling of a thin CFRP laminated plate</figcaption></figure>

Modelling of the milling process

The milling model for the case studied is based on a milling simulator for isotropic metallic thin plates. This simulator named DyStaMill was developed by Prof. Rivière-Lorphèvre and is presented in Huynh et al. [11]. It was therefore necessary to adapt this simulator for the case study: machining a thin composite plate. Two essential points had to be adapted:

1. The finite element thin plate model for an orthotropic material such as FRPs.

2. The cutting model for unidirectional composite milling.
Mechanical properties of CFRPs: The mechanical properties of a UD-FRP ply can be expressed directly as a function of the mechanical characteristics of the fibres (f) and the matrix (m) (Eq. 1-5) and the volume of fiber (Vf) using mixing laws [12]. The modulus of elasticity in the fibre direction E_l and in the transverse direction E_t are expressed in Eq. 1 and Eq. 2. These properties are a function of the modulus of elasticity of the fibre in the longitudinal E_f,l and transverse E_f,t directions and the modulus of elasticity of the matrix E_m.

$$E_l = E_{f,l}V_f + E_m(1 - V_f) \qquad (1)$$

$$E_t = \frac{E_m}{(1 - V_f) + \frac{E_m V_f}{E_{f,t}}} \qquad (2)$$

The Poisson's ratio $v_{lt}$ is expressed in Eq. 3. It is a function of the Poisson's ratio of the fibre $v_f$ and the matrix $v_m$.

$$v_{lt} = v_f V_f + v_m (1 - V_f) \qquad (3)$$

By symmetry property, the Poisson's ratio $v_{lt}$ is expressed in Eq. 4:

$$v_{tl} = v_{lt} \frac{E_t}{E_l} \qquad (4)$$

The shear modulus $G_{lt}$ is expressed in Eq. 5. It is a function of the fibre shear modulus $G_{f,lt}$ and the matrix shear modulus $G_m$.

$$G_{lt} = \frac{G_m}{(1 - V_f) + \frac{G_m V_f}{G_{f,lt}}} \qquad (5)$$

Dynamic response of thin orthotropic plate: The finite element model developed in DyStaMill is based on the thin plate model presented by Zienkiewicz [13]. This finite element model allows to compute the dynamic response of the thin plate subjected to bending. The main assumption is to consider only the flexibility of the workpiece. Indeed, the CNC milling machine has a greater rigidity than the workpiece. The dynamic behavior of the tool/CNC milling machine can therefore be neglected. 2D plate type elements are used. They are composed of four nodes with three degrees of freedom for each of them (out of plane displacements and bending).

The description of the dynamic system can be expressed in a global equation (Eq. 6) and can be solved as explained in [4]:

$$[M]\{\ddot{x}\} + [C]\{\dot{x}\} + [K]\{x\} = \{f\} \qquad (6)$$

$[M]$, $[C]$ and $[K]$ are mass, damping and rigidity matrices, $\{x\}$ is the vector containing configuration parameters (the three degrees of freedom per node as described previously) and $\{f\}$ the forces acting on the system.

For a quasi-static analysis, the equation becomes (Eq. 7):

$$[K]\{x\} = \{f\} \qquad (7)$$

It is possible to build the stiffness matrix K for a rectangular element (Eq. 8) [13]:

$$K = \frac{1}{60ab} L \{D_x K_1 + D_z K_2 + D_1 K_3 + D_{xz} K_4\} L \qquad (8)$$
Mechanical properties of CFRPs: The mechanical properties of a UD-FRP ply can be expressed directly as a function of the mechanical characteristics of the fibres (f) and the matrix (m) (Eq. 1-5) and the volume of fiber (Vf) using mixing laws [12]. The modulus of elasticity in the fibre direction E_l and in the transverse direction E_t are expressed in Eq. 1 and Eq. 2. These properties are a function of the modulus of elasticity of the fibre in the longitudinal E_f,l and transverse E_f,t directions and the modulus of elasticity of the matrix E_m.

$$E_l = E_{f,l}V_f + E_m(1 - V_f) \qquad (1)$$

$$E_t = \frac{E_m}{(1 - V_f) + \frac{E_m}{E_{f,t}}V_f} \qquad (2)$$

The Poisson's ratio $v_{lt}$ is expressed in Eq. 3. It is a function of the Poisson's ratio of the fibre $v_f$ and the matrix $v_m$.

$$v_{lt} = v_f V_f + v_m (1 - V_f) \qquad (3)$$

By symmetry property, the Poisson's ratio $v_{lt}$ is expressed in Eq. 4:

$$v_{tl} = v_{lt} \frac{E_t}{E_l} \qquad (4)$$

The shear modulus $G_{lt}$ is expressed in Eq. 5. It is a function of the fibre shear modulus $G_{f,lt}$ and the matrix shear modulus $G_m$.

$$G_{lt} = \frac{G_m}{(1 - V_f) + \frac{G_m}{G_{f,lt}}V_f} \qquad (5)$$

Dynamic response of thin orthotropic plate: The finite element model developed in DyStaMill is based on the thin plate model presented by Zienkiewicz [13]. This finite element model allows to compute the dynamic response of the thin plate subjected to bending. The main assumption is to consider only the flexibility of the workpiece. Indeed, the CNC milling machine has a greater rigidity than the workpiece. The dynamic behavior of the tool/CNC milling machine can therefore be neglected. 2D plate type elements are used. They are composed of four nodes with three degrees of freedom for each of them (out of plane displacements and bending).

The description of the dynamic system can be expressed in a global equation (Eq. 6) and can be solved as explained in [4]:

$$[M]\{\ddot{x}\} + [C]\{\dot{x}\} + [K]\{x\} = \{f\} \qquad (6)$$

$[M]$, $[C]$ and $[K]$ are mass, damping and rigidity matrices, $\{x\}$ is the vector containing configuration parameters (the three degrees of freedom per node as described previously) and $\{f\}$ the forces acting on the system.

For a quasi-static analysis, the equation becomes (Eq. 7):

$$[K]\{x\} = \{f\} \qquad (7)$$

It is possible to build the stiffness matrix K for a rectangular element (Eq. 8) [13]:

$$K = \frac{1}{60ab} L\{D_x K_1 + D_z K_2 + D_1 K_3 + D_{xz} K_4\}L \qquad (8)$$
a and b are related to the dimensions of the rectangular element. The matrices $K_1, K_2, K_3, K_4$ and L are intermediate matrices in the construction of the stiffness matrix $K$. $D_x, D_z, D_{xz}$ and $D_l$ are elements of the elasticity matrix $D_{xz}$ in the global reference XZ (fig. 3). The matrix $D_{XY}$ is defined for an orthotropic material by Eq. 9 [14]:

$$D_{xz} = \begin{bmatrix} D_x & D_{1,xz} & 0 \\ D_{1,xz} & D_z & 0 \\ 0 & 0 & D_{xz} \end{bmatrix} \qquad (9)$$

The elasticity matrix $D_{lt}$ of the ply in its local frame lt is expressed by Eq. 10. In the case studied, as the fibre orientation angle $\psi$ is zero, the elasticity matrices in the local and global reference frames are identical.

$$D_{lt} = D_{xz} = \begin{bmatrix} D_l & D_{1,lt} & 0 \\ D_{1,lt} & D_t & 0 \\ 0 & 0 & D_{lt} \end{bmatrix} \qquad (10)$$

The components of the elasticity matrix are defined by Eq. 11. These expressions are valid for the case of a laminate of thickness $e$ with all its plies oriented horizontally (Fig. 3).

$$D_l = \frac{E_l}{1 - v_{tl} v_{lt}} \frac{e^3}{12}, \quad D_t = \frac{E_t}{1 - v_{tl} v_{lt}} \frac{e^3}{12}, \quad D_{lt} = G_{lt} \frac{e^3}{12}, \quad D_{1,lt} = \frac{v_{lt} E_l}{1 - v_{tl} v_{lt}} \frac{e^3}{12} \qquad (11)$$

Cutting force model for composite material: The new mechanistic cutting forces model implemented in the DyStaMill software is the model developed by Mullin et al. [15]. The expression of the cutting force applied in radial (Eq. 12) and tangential (Eq. 13) directions is similar to Altintas' model [16].

$$dF_r(\varphi) = K_{rc}(\beta) h dz + K_{re}(\beta) dS \qquad (12)$$

$$dF_t(\varphi) = K_{tc}(\beta) h dz + K_{te}(\beta) dS \qquad (13)$$

Altintas' model considers two contributions in both directions (radial $r$ and tangential $t$). The first ones are related to friction in the primary shear zone: a function of the radial and tangential cutting force coefficient, respectively $K_{rc}$ and $K_{tc}$, the instantaneous uncut chip thickness $h$ and the height of a tool edge $dz$. The second ones consider the effect of friction in the secondary shear zone and is a function of the radial and tangential edge force coefficient, respectively $K_{re}$ and $K_{te}$, and the local segment $dS$ of the cutting edge. However, cutting ($K_{rc}$ and $K_{tc}$) and edge ($K_{re}$ and $K_{te}$) coefficients vary according to the instantaneous fiber cutting angle $\beta$ (Eq. 14).

$$K_{ij}(\beta) = C_{0}^{ij} + C_{1}^{ij} \cos(2\beta) + S_{1}^{ij} \sin(2\beta) \quad \text{with } i = t, r \text{ and } j = c, e \qquad (14)$$

The method for identifying the coefficients and their values is given in [15]. These values are only valid for the tool/material pair used in [15]: a 2-flute Tungsten Carbide end mill with 9.525 mm (3/8 inch) diameter and 3° normal rake angle and UD-CFRP (alternating 0° and 90° directions).

## Numerical results

The composite used is a UD-CFRP with a “High Resistance” (HR) and an epoxy resin. The mechanical properties of the fibres and the matrix studied are given in Tab. 1.