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Research Article

In-process monitoring of the ultraprecision machining process with convolution neural networks

, , &
Pages 37-54 | Received 16 Dec 2022, Accepted 14 Jun 2023, Published online: 27 Jun 2023

References

  • Abdul, Z. K., and A. K. Al-Talabani. 2022. “Mel Frequency Cepstral Coefficient and Its Applications: A Review. IEEE Access. 10, 122136–122158. http://doi.org/10.1109/ACCESS.2022.3223444.
  • AlDahoul, N., H. Abdul Karim, and A. Saleh Ba Wazir. 2021. “Model Fusion of Deep Neural Networks for Anomaly Detection.” Journal of Big Data 8 (1): 1–18. https://doi.org/10.1186/s40537-021-00496-w.
  • Atalay, M., U. Murat, B. Oksuz, A. Merve Parlaktuna, E. Pisirir, and M. Caner Testik. 2022. “Digital Twins in Manufacturing: Systematic Literature Review for Physical–Digital Layer Categorization and Future Research Directions.” International Journal of Computer Integrated Manufacturing 35 (7): 679–705. https://doi.org/10.1080/0951192X.2021.2022762.
  • Azizur Rahman, M., M. Rahman, and A. Senthil Kumar. 2018. “Influence of Relative Tool Sharpness (RTS) on Different Ultra-Precision Machining Regimes of Mg Alloy.” The International Journal of Advanced Manufacturing Technology 96 (9): 3545–3563. https://doi.org/10.1007/s00170-018-1599-4.
  • Beyca, O. F., P. K. Rao, Z. Kong, S. T. Bukkapatnam, and R. Komanduri. 2015. “Heterogeneous Sensor Data Fusion Approach for Real-Time Monitoring in Ultraprecision Machining (UPM) Process Using Non-Parametric Bayesian Clustering and Evidence Theory.” IEEE Transactions on Automation Science and Engineering 13 (2): 1033–1044. https://doi.org/10.1109/TASE.2015.2447454.
  • Boddapati, V., A. Petef, J. Rasmusson, and L. Lundberg. 2017. “Classifying Environmental Sounds Using Image Recognition Networks.” Procedia Computer Science 112:2048–2056. https://doi.org/10.1016/j.procs.2017.08.250.
  • Cha, Y., W. Choi, and O. Büyüköztürk. 2017. “Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks.” Computer‐Aided Civil and Infrastructure Engineering 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
  • Cha, Y., W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk. 2018. “Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types.” Computer‐Aided Civil and Infrastructure Engineering 33 (9): 731–747. https://doi.org/10.1111/mice.12334.
  • Cheng, K., D. Huo 2013. Micro-Cutting: Fundamentals and Applications: 2013. London, United Kingdom: John Wiley & Sons Ltd. https://doi.org/10.1002/9781118536605.
  • Cheng, K., Z.-C. Niu, R. C. Wang, R. Rakowski, and R. Bateman. 2017. “Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives.” Chinese Journal of Mechanical Engineering 30 (5): 1162–1176. https://doi.org/10.1007/s10033-017-0183-4.
  • Cheng, C., A. Sa-Ngasoongsong, O. Beyca, T. Le, H. Yang, Z. Kong, and S. T. Bukkapatnam. 2015. “Time Series Forecasting for Nonlinear and Non-Stationary Processes: A Review and Comparative Study.” IIE Transactions 47 (10): 1053–1071. https://doi.org/10.1080/0740817X.2014.999180.
  • Chen, S., Y. Meng, H. Tang, Y. Tian, H. Niao, and C. Shao. 2020. “Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery.” IEEE/ASME Transactions on Mechatronics 25 (5): 2167–2176. https://doi.org/10.1109/TMECH.2020.3007441.
  • Choi, K., G. Fazekas, M. Sandler, and K. Cho. 2018. “A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging.” Paper presented at the 2018 26th European Signal Processing Conference (EUSIPCO). Rome, Italy.
  • Dubey, A. K., and V. Jain. 2019. “Comparative Study of Convolution Neural Network’s Relu and Leaky-Relu Activation Functions.” In Applications of Computing, Automation and Wireless Systems in Electrical Engineering, 873–880. Springer Singapore. https://doi.org/10.1007/978-981-13-6772-4_76.
  • Epp, T., and Y.-J. Cha. 2016. “Air-Coupled Impact-Echo Damage Detection in Reinforced Concrete Using Wavelet Transforms.” Smart Materials and Structures 26 (2): 025018. https://doi.org/10.1088/1361-665X/26/2/025018.
  • Gao, R. X., and R. Yan. 2010. Wavelets: Theory and Applications for Manufacturing. Berlin/Heidelberg, Germany: Springer Science & Business Media.
  • He, M., M. Petering, P. LaCasse, W. Otieno, and F. Maturana. 2023. “Learning with Supervised Data for Anomaly Detection in Smart Manufacturing.” International Journal of Computer Integrated Manufacturing:1–14. https://doi.org/10.1080/0951192X.2023.2177747.
  • Hung, C.-W., C.-H. Lee, C.-C. Kuo, and S.-X. Zeng. 2022. “SoC-Based Early Failure Detection System Using Deep Learning for Tool Wear.” Ieee Access 10:70491–70501. https://doi.org/10.1109/ACCESS.2022.3187043.
  • Jáuregui, J. C., R. Juvenal, S. T. Reséndiz, T. Szalay, Á. Jacsó, and M. Takács. 2018. “Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring.” Ieee Access 6:6400–6410. https://doi.org/10.1109/ACCESS.2018.2797003.
  • Kan, C., C. Cheng, and H. Yang. 2016. “Heterogeneous Recurrence Monitoring of Dynamic Transients in Ultraprecision Machining Processes.” Journal of Manufacturing Systems 41:178–187. https://doi.org/10.1016/j.jmsy.2016.08.007.
  • Krishnakumar, P., K. Rameshkumar, and K. I. Ramachandran. 2018. “Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach.” International Journal of Computational Intelligence and Applications 17 (3): 1850017. https://doi.org/10.1142/S1469026818500177.
  • Lattanzi, L., R. Raffaeli, M. Peruzzini, and M. Pellicciari. 2021. “Digital Twin for Smart Manufacturing: A Review of Concepts Towards a Practical Industrial Implementation.” International Journal of Computer Integrated Manufacturing 34 (6): 567–597. https://doi.org/10.1080/0951192X.2021.1911003.
  • Lee, H., R. Grosse, R. Ranganath, and A. Y. Ng. 2009. “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.” Paper presented at the Proceedings of the 26th annual international conference on machine learning Montreal, Canada.
  • Liao, Y., I. Ragai, Z. Huang, and S. Kerner. 2021. “Manufacturing Process Monitoring Using Time-Frequency Representation and Transfer Learning of Deep Neural Networks.” Journal of Manufacturing Processes 68:231–248. https://doi.org/10.1016/j.jmapro.2021.05.046.
  • Liu, J., J. Zheng, P. Rao, and Z. Kong. 2020. “Machine Learning–Driven in situ Process Monitoring with Vibration Frequency Spectra for Chemical Mechanical Planarization.” The International Journal of Advanced Manufacturing Technology 111 (7–8): 1873–1888. https://doi.org/10.1007/s00170-020-06165-1.
  • Manjunath, K., S. Tewary, and N. Khatri. 2022. “Surface Roughness Prediction in Milling Using Long-Short Term Memory Modelling.” Materials Today: Proceedings Ghaziabad, India. 64:1300–1304.
  • Manjunath, K., S. Tewary, N. Khatri, and K. Cheng. 2021. “Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review.” Machines 9 (12): 369. https://doi.org/10.3390/machines9120369.
  • Marsh, E. R., and A. J. Schaut. 1998. “Measurement and Simulation of Regenerative Chatter in Diamond Turning.” Precision Engineering 22 (4): 252–257. https://doi.org/10.1016/S0141-6359(98)00020-8.
  • Meyer, P. A., S. C. Veldhuis, and M. A. Elbestawi. 2009. “Predicting the Effect of Vibration on Ultraprecision Machining Surface Finish as Described by Surface Finish Lobes.” International Journal of Machine Tools and Manufacture 49 (15): 1165–1174. https://doi.org/10.1016/j.ijmachtools.2009.08.006.
  • Mourtzis, D. 2021. Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology. Amsterdam, Netherlands: Elsevier.
  • Mourtzis, D., and J. Angelopoulos. 2020. “An Intelligent Framework for Modelling and Simulation of Artificial Neural Networks (ANNs) Based on Augmented Reality.” The International Journal of Advanced Manufacturing Technology 111 (5–6): 1603–1616. https://doi.org/10.1007/s00170-020-06192-y.
  • Mourtzis, D., J. Angelopoulos, A. Nektarios Arvanitis, and N. Panopoulos. 2022. “Automating Quality Control Based on Machine Vision Towards Automotive 4.0.” Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action: IFIP WG 5.7 International Conference, APMS 2022,September25–29, 2022. Springer Cham. 25–29. Gyeongju, South Korea.
  • Mourtzis, D., J. Angelopoulos, and N. Panopoulos. 2020. “A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions Based on Convolutional Neural Networks.” Procedia CIRP 93:977–982. https://doi.org/10.1016/j.procir.2020.04.130.
  • Namoano, B., A. Starr, C. Emmanouilidis, and R. Carcel Cristobal. 2019. “Online Change Detection Techniques in Time Series: An Overview.” Paper presented at the 2019 IEEE international conference on prognostics and health management (ICPHM) San Francisco, CA, USA.
  • Narayanan, B., and M. Sreekumar. 2022. “Design, Modelling, Optimisation and Validation of Condition-Based Maintenance in IoT Enabled Hybrid Flow Shop.” International Journal of Computer Integrated Manufacturing 35 (9): 927–941. https://doi.org/10.1080/0951192X.2022.2028011.
  • Ni, C., L. Zhu, Z. Zheng, J. Zhang, Y. Yang, J. Yang, Y. Bai, C. Weng, L. Wen Feng, and H. Wang. 2020. “Effect of Material Anisotropy on Ultra-Precision Machining of Ti-6Al-4V Alloy Fabricated by Selective Laser Melting.” Journal of Alloys and Compounds 848:156457. https://doi.org/10.1016/j.jallcom.2020.156457.
  • Ocaña Moreno, J. L., F. Cordovilla Baró, Á. García Beltrán, J. de Vicente Oliva, J. Antonio Porro González, M. Díaz Muñoz, S. Tapia Fernández, J. D. Ignacio Angulo Ramonell, and P. Alvarez. 2019. “Integrated Analysis and Quality Control of Laser-Based Additive Manufacturing Processes. 3rd International Conference on 3D Printing Technology and Innovations. March 25-26. 1–45. Italy.
  • Rahman, M. A., M. Raihan Amrun, M. Rahman, and A. Senthil Kumar. 2017. “Variation of Surface Generation Mechanisms in Ultra-Precision Machining Due to Relative Tool Sharpness (RTS) and Material Properties.” International Journal of Machine Tools and Manufacture 115:15–28. https://doi.org/10.1016/j.ijmachtools.2016.11.003.
  • Rao, P. K. 2013. Sensor-Based Monitoring and Inspection of Surface Morphology in Ultraprecision Manufacturing Processes. Oklahoma State University.
  • Rao, P., S. Bukkapatnam, O. Beyca, Z. James Kong, and R. Komanduri. 2014. “Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process.” Journal of Manufacturing Science and Engineering 136 (2). https://doi.org/10.1115/1.4026210.
  • Sawangsri, W., and K. Cheng. 2016. “An Innovative Approach to Cutting Force Modelling in Diamond Turning and Its Correlation Analysis with Tool Wear.” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 230 (3): 405–415. https://doi.org/10.1177/0954405414554020.
  • Selvaraj, V., X. Zhicheng, and S. Min. 2023. “Intelligent Operation Monitoring of an Ultra-Precision Cnc Machine Tool Using Energy Data.” International Journal of Precision Engineering and Manufacturing-Green Technology 10 (1): 59–69. https://doi.org/10.1007/s40684-022-00449-5.
  • Shamsan, A., and C. Cheng. 2019. “Intrinsic Multiplex Graph Model Detects Incipient Process Drift in Ultraprecision Manufacturing.” Journal of Manufacturing Systems 50:81–86. https://doi.org/10.1016/j.jmsy.2018.12.005.
  • Shi, C., G. Panoutsos, B. Luo, H. Liu, B. Li, and X. Lin. 2018. “Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing.” IEEE Transactions on Industrial Electronics 66 (5): 3794–3803.
  • Sizemore, N. E., M. L. Nogueira, N. P. Greis, and M. A. Davies. 2022. “Application of Machine Learning for Improved Surface Quality Classification in Ultra-Precision Machining of Germanium.” Journal of Manufacturing Systems 65:296–316. https://doi.org/10.1016/j.jmsy.2022.09.001.
  • Stavropoulos, P., T. Souflas, C. Papaioannou, H. Bikas, and D. Mourtzis. 2023. “An Adaptive, Artificial Intelligence-Based Chatter Detection Method for Milling Operations.” The International Journal of Advanced Manufacturing Technology 124 (7–8): 2037–2058. https://doi.org/10.1007/s00170-022-09920-8.
  • Teti, R., D. Mourtzis, D. M. D’Addona, and A. Caggiano. 2022. “Process Monitoring of Machining.” CIRP Annals 71 (2): 529–552. https://doi.org/10.1016/j.cirp.2022.05.009.
  • Thoř, T., A. Procházková, F. Procháska, R. Doleček, M. Špína, J. Václavík, T. Sedlmaier, and M. Mulser. 2021. “Development of an Ultraprecision Metal Mirror on Additively Manufactured Ti-6Al-4V.” Applied Optics 60 (31): 9919–9924. https://doi.org/10.1364/AO.436311.
  • Tran, T., and J. Lundgren. 2020. “Drill Fault Diagnosis Based on the Scalogram and Mel Spectrogram of Sound Signals Using Artificial Intelligence.” Ieee Access 8:203655–203666. https://doi.org/10.1109/ACCESS.2020.3036769.
  • Tran, T., N. Truong Pham, and J. Lundgren. 2022. “A Deep Learning Approach for Detecting Drill Bit Failures from a Small Sound Dataset.” Scientific Reports 12 (1): 1–13. https://doi.org/10.1038/s41598-022-13237-7.
  • Verstraete, D., A. Ferrada, E. López Droguett, V. Meruane, and M. Modarres. 2017. “Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings.” Shock and Vibration 2017:1–17. https://doi.org/10.1155/2017/5067651.
  • Wang, Z., S. T. Bukkapatnam, S. R. Kumara, Z. Kong, and Z. Katz. 2014. “Change Detection in Precision Manufacturing Processes Under Transient Conditions.” CIRP Annals 63 (1): 449–452. https://doi.org/10.1016/j.cirp.2014.03.123.
  • Wang, Z., and Y.-J. Cha. 2021. “Unsupervised Deep Learning Approach Using a Deep Auto-Encoder with a One-Class Support Vector Machine to Detect Damage.” Structural Health Monitoring 20 (1): 406–425. https://doi.org/10.1177/1475921720934051.
  • Wang, S., X. Chen, T. Sandy, X. Chen, Q. Liu, and J. Liu. 2017. “Modelling and Prediction of the Effect of Cutting Strategy on Surface Generation in Ultra-Precision Raster Milling.” International Journal of Computer Integrated Manufacturing 30 (9): 895–909. https://doi.org/10.1080/0951192X.2016.1239029.
  • Wang, T., L. Jiakun, Y. Deng, C. Wang, H. Snoussi, and F. Tao. 2021. “Digital Twin for Human-Machine Interaction with Convolutional Neural Network.” International Journal of Computer Integrated Manufacturing 34 (7–8): 888–897. https://doi.org/10.1080/0951192X.2021.1925966.
  • Wang, B., F. Tao, X. Fang, C. Liu, Y. Liu, and T. Freiheit. 2021. “Smart Manufacturing and Intelligent Manufacturing: A Comparative Review.” Engineering 7 (6): 738–757. https://doi.org/10.1016/j.eng.2020.07.017.
  • Wang, S., S. Xia, H. Wang, Z. Yin, and Z. Sun. 2020. “Prediction of Surface Roughness in Diamond Turning of Al6061 with Precipitation Effect.” Journal of Manufacturing Processes 60:292–298. https://doi.org/10.1016/j.jmapro.2020.10.070.
  • Wu, L., J. Leng, and J. Bingfeng. 2021. “Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review.” Symmetry 13 (9): 1717. https://doi.org/10.3390/sym13091717.
  • Yan, R., R. X. Gao, and X. Chen. 2014. “Wavelets for Fault Diagnosis of Rotary Machines: A Review with Applications.” Signal Processing 96:1–15. https://doi.org/10.1016/j.sigpro.2013.04.015.
  • Yip, W. S., and T. Suet. 2018. “Ductile and Brittle Transition Behavior of Titanium Alloys in Ultra-Precision Machining.” Scientific Reports 8 (1): 3934. https://doi.org/10.1038/s41598-018-22329-2.
  • Yip, W. S., T. Suet, and H. Zhou. 2022. “Current Status, Challenges and Opportunities of Sustainable Ultra-Precision Manufacturing.” Journal of Intelligent Manufacturing 33: 2193–2205. https://doi.org/10.1007/s10845-021-01782-3.
  • Zhou, J., L. Peigen, Y. Zhou, B. Wang, J. Zang, and L. Meng. 2018. “Toward New-Generation Intelligent Manufacturing.” Engineering 4 (1): 11–20. https://doi.org/10.1016/j.eng.2018.01.002.
  • Zhou, H., W. Sze Yip, J. Ren, and T. Suet. 2022. “Thematic Analysis of Sustainable Ultra-Precision Machining by Using Text Mining and Unsupervised Learning Method.” Journal of Manufacturing Systems 62:218–233. https://doi.org/10.1016/j.jmsy.2021.11.013.
  • Židek, K., J. Piteľ, M. Balog, A. Hošovský, V. Hladký, P. Lazorík, A. Iakovets, and J. Demčák. 2021. “CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots.” Applied Sciences 11 (9): 4269. https://doi.org/10.3390/app11094269.

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