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Artificial Intelligence techniques and Internet of things sensors for tool condition monitoring in milling: A review
FERRISI Stefania, AMBROGIO Giuseppina, GUIDO Rosita, UMBRELLO Domenico
download PDFAbstract. Milling is a machining process that involves removing material from a workpiece using a rotating cutting tool. During the milling process, the cutter is affected by a progressive degradation due to the grinding of the workpiece which results in a decline in product quality and a sharp increase in energy consumption and production costs. For these reasons, Tool Condition Monitoring has emerged as the essential approach in the machining industry. The application of Artificial Intelligence (AI) systems that incorporate the use of various Internet of Things sensors (IoT) to support the tool condition monitoring in the milling process has recently been a subject of great interest to researchers. It helps to achieve goals required in the modern manufacturing industries in terms of sustainability, cost reduction, and quality improvement. This review article focuses on the application of IoT sensors for recording acoustic emissions, to conduct tool condition monitoring of the milling cutting tools based on AI techniques. The discussion includes an analysis of the principal sensory systems and their main advantages and disadvantages for the milling process. Moreover, trends and problems of applied AI techniques for tool condition monitoring are highlighted.
Keywords
Tool Condition Monitoring, Artificial Intelligence, Milling Process
Published online 4/24/2024, 11 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: FERRISI Stefania, AMBROGIO Giuseppina, GUIDO Rosita, UMBRELLO Domenico, Artificial Intelligence techniques and Internet of things sensors for tool condition monitoring in milling: A review, Materials Research Proceedings, Vol. 41, pp 2000-2010, 2024
DOI: https://doi.org/10.21741/9781644903131-221
The article was published as article 221 of the book Material Forming
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.
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
[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
[27] F. Letford, M. Rogers, X. Xu, and Y. Lu, “Machine Learning to Empower a Cyber-Physical Machine Tool,” in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), IEEE, Aug. 2020, pp. 989–994. https://doi.org/10.1109/CASE48305.2020.9216842
[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 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: https://ti.arc.nasa.gov/project/prognostic-data-repository
[67] Information on: https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge/
