Image Based Detection of Coating Wear on Cutting Tools with Machine Learning

• Autorzy: Wolf Jan , Bandaru Nithin Kumar , Dienwiebel Martin , Möhring Hans-Christian

Identyfikatory

DOI

10.36897/jme/196725

• Języki publikacji: EN

Abstrakty

EN

Wear of cutting tools is known to affect the surface integrity of the workpiece and significantly contributes to machine downtime. To establish wear-resistant cutting tools, several coating strategies have been introduced. It has been shown that the wear rate increases dramatically once the coating is worn through. Detecting coating layer loss is therefore a good indicator of the remaining useful life of the cutting tool. Based on cutting experiments conducted with a TiN/AlTiN-coated cutting tool, an image dataset was generated and pre-processed using multiple algorithms, such as Canny edge detection and Hough line transforms. For the classification task four machine learning Algorithms consisting of Random Forests, Decision Trees, Support Vector Machines and a Feed Forward Neural Network were implemented. The results demonstrate that all four algorithms lead to a good classification performance, with Decision Trees showing the best performance with a F1-score of 0.95. Therefore, this research provides an efficient data processing and classification framework for detecting coating wear on cutting tools.

Słowa kluczowe

EN

wear detection; coated cutting tools; machine learning

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2025
• Tom: Vol. 25, No. 1
• Strony: 57--67
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Wolf Jan

• Institute for Machine Tools (IfW), University of Stuttgart, Germany

autor

Bandaru Nithin Kumar

• Institute for Applied Materials (IAM), Karlsruhe Institute of Technology, Germany

autor

Dienwiebel Martin

• Institute for Applied Materials (IAM), Karlsruhe Institute of Technology, Germany

autor

Möhring Hans-Christian

• Institute for Machine Tools (IfW), University of Stuttgart, Germany

Bibliografia

• [1] KURADA S., BRADLEY C., 1997, A Review of Machine Vision Sensors for Tool Condition Monitoring, Computers in Industry, 28/1, 55–72, https://doi.org/10.1016/S0166-3615(96)00075-9.
• [2] BERGS T., HOLST C., GUPTA P., AUGSPURGER, T., 2020, Digital image processing with deep learning for automated cutting tool wear detection, Procedia Manufacturing, 48, 947–958. https://doi.org/10.1016/j.promfg. 2020.05.134
• [3] BONIFACIO M., DINIZ A., 1994, Correlating Tool Wear, Tool Life, Surface Roughness and Tool Vibration in Finish Turning wit Coated Carbide Tools, Wear, 173, 137–144, https://doi.org/10.1016/0043-1648(94)90266-6.
• [4] CHEHREHZAD M., KECIBAS G., BESIROVA C., URESIN U., Irican M., Lazoglu, I., 2024, Tool Wear Prediction Through AI-Assisted Digital Shadow Using Industrial Edge Device, Journal of Manufacturing Processes, 113, 117–130, https://doi.org/10.1016/j.jmapro.2024.01.052
• [5] WOLF J., REEBER T., BANDARU NK., DIENWIEBEL M., MÖHRING H.-C., 2024, Transient Wear Modelling of Coated Cutting Tools, Procedia CIRP x
• [6] METHON G., COURBON C., SAOUBI R., GIRINON M., RECH J., 2021, Numerical Modeling of Wear in Orthogonal Cutting of Multilayer Coated Tools, Procedia CIRP, 102, 67–72, https://doi.org/10.1016/j.procir. 2021.09.012.
• [7] ATTANASIO A., FAINI F., OUTEIRO JC., 2017, FEM Simulation of Tool Wear in Drilling, Procedia CIRP, 58, 440–444, https://doi.org/10.1016/j.procir.2017.03.249.
• [8] BINDER M., KLOCKE F., DOEBBLER B., 2017, An Advanced Numerical Approach on Tool Wear Simulation for Tool and Process Design in Metal Cutting, Simulation Modelling Practice and Theory, 70, 65–82, https://doi.org/10.1016/j.simpat.2016.09.001.
• [9] MARTINEZ-ARELLANO G., TERAZZAS G., RATCHEV S., 2019, Tool Wear Classification Using Time Series Imaging and Deep Learning, Int Journal of Advanced Manufacturing Technology, 104, 3547–3662, https://doi.org/10.1007/s00170-019-04090-6.
• [10] WANG M., Wang J., 2012, CHMM for tool condition monitoring and remaining useful life prediction, International Journal of Advanced Manufacturing Technology, 59, 463-471. https://doi.org/10.1007/s00170-011-3536-7.
• [11] REEBER T., HENNINGER J., WEINGARZ N., SIMON P., BERNDT M., GLATT M., KIRSCH B., EISSELER R., AURICH J., MÖHRING H.-C., 2024 Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data, Procedia Cirp, 121, 216-221. https://doi.org/10.1016/j.procir.2023. 08.066.
• [12] MUNARO R., ATTANASIO A., DEL PRETE A., 2023, Tool Wear Monitoring with Artificial Intelligence Methods: A Review, Journal of Manufacturing and Material Processing, 7, 129. https://doi.org/10.3390/ jmmp7040129.
• [13] COLANTONIO L., EQUETER L., DEHOMBREUX P., DUCOBU F., 2021, A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques, Machines, 9, 351. https://doi.org/10.3390/machines9120351.
• [14] MIAO H., ZHAO Z., SUN C., LI B., YAN R., 2021, A U-Net-Based Approach for Tool Wear Area Detection and Identification. IEEE Transactions on Instrumentation and Measurement, 70, 1–10, https://doi.org/10.1109/TIM.2020.3033457.
• [15] HOLST C., YAVUZ T., GUPTA P., GANSER P., BERGS T., 2022, Deep Learning and Rule-Based Image Processing Pipeline for Automated Metal Cutting Tool Wear Detection and Measurement, IFAC PapersOnLine, 55, 534-539, https://doi.org/10.1016/j.ifacol.2022.04.249.
• [16] GUPTA J., PATHAL S., KUMAR G., 2022, Deep Learning (CNN) and Transfer Learning: A Review, Journal of Physics: Conference Series, 2273, https://doi.org/10.1088/1742-6596/2273/1/012029.
• [17] MÖHRING H-C., WIEDERKEHR P., ERKORKMAZ K., KAKINUMA Y., 2020, Self-Optimizing Machining Systems, CIRP ANNALS – Manufacturing Technology, 69, 740-763, https://doi.org/10.1016/j.cirp.2020.05.007.
• [18] DUDA R., HART E., 1972, Use of the Hough Transformation to Detect Lines and Curves in Pictures, Communications of the ACM, 15, 11-15, https://doi.org/10.1145/361237.361242.
• [19] BRADSKI G., 2000, The OpenCV Library, Dr. Dobb's J Software Tools.
• [20] BLOCKEEL H., DEVOS L., FRENAY B., NANFACK G., NIJSSEN S., 2023, Decision Trees: from Efficient Predicition to Responsible AI, Frontiers in Artificial Intelligence, 6, 1124553. https://doi.org/10.3389/frai. 2023.1124553.
• [21] FLETSCHER S., ISLAM Z., 2019, Decision Tree Classification with Differential Privacy: A Survey, ACM Computing Surveys, 52/4, 83, https://doi.org/10.1145/3337064.
• [22] BREIMAN L., 2001, Random Forests, Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324.
• [23] KULKARNI V., SINHA P., 2013, Random Forest Classifiers: A Survey and Future Research Directions, International Journal of Advanced Computing, 36, 1144-1153.
• [24] BELGIU M., DRAGUT L., 2016, Random Forest in Remote Sensing: A Review of Applications and Future Direction, ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. http://dx.doi.org/10.1016/ j.isprsjprs.2016.01.011.
• [25] CERVANTES J., GARCIA-LAMONT F., RODRIGUEZ-MAZAHUA L., LOPZ A., 2020, A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends, Neurocomputing, 408, 189–215, https://doi.org/10.1016/j.neucom.2019.10.118.
• [26] SRIAVASTAVA D., BHAMBHU L., 2009, Data Classification Using Support Vector Machine, Journal of Theoretical and Applied Information Technology, 12, 1–7.
• [27] HSU C.-W., CHANG C.-C., LIN C.-J., 2016, A Practical Guide to Support Vector Classification, https://www.csie.ntu. edu.tw/~cjlin/papers/guide/guide.pdf (last accessed 21.10.2024).
• [28] REEBER T., WOLF J., MÖHRING, H-C., 2024, A Data-Driven Approach for Cut-ting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines, Journal of Manufacturing and Materials Processing, 8, 107, https://doi.org/10.3390/jmmp8030107.
• [29] MEHLIG B., 2021, Machine Learning with Neural Networks, Cambridge University Press, Cambridge, UK,. https://doi.org/10.1017/9781108860604.