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Mechanical Engineering

Cutting-edge tool wear monitoring in AISI4140 steel hard turning using least square-support vector machine

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Pages 492-507 | Received 31 Aug 2023, Accepted 22 Mar 2024, Published online: 02 May 2024

References

  • Aouici, H., M. A. Yallese, K. Chaoui, T. Mabrouki, and J. F. Rigal. 2012. “Analysis of Surface Roughness and Cutting Force Components in Hard Turning with CBN Tool: Prediction Model and Cutting Conditions Optimization.” Measurement 45 (3): 344–353. doi: 10.1016/j.measurement.2011.11.011.
  • Asiltürk, I., and H. Akkuş. 2011. “Determining the Effect of Cutting Parameters on Surface Roughness in Hard Turning Using the Taguchi Method.” Measurement 44 (9): 1697–1704. doi: 10.1016/j.measurement.2011.07.003.
  • Aslan, E. 2005. “Experimental Investigation of Cutting Tool Performance in High Speed Cutting of Hardened X210 Cr12 Cold-Work Tool Steel (62 HRC).” Materials & Design 26 (1): 21–27. doi: 10.1016/j.matdes.2004.04.004.
  • Balwan, V. R., B. Dabade, and L. Wankhade. 2022. “Influence of Hard Turning Parameters on Cutting Forces of EN 353 Steel.” Materials Today: Proceedings 63 (January): 149–156. doi: 10.1016/j.matpr.2022.02.425.
  • Bartarya, G., and S. K. Choudhury. 2012. “State of the Art in Hard Turning.” International Journal of Machine Tools and Manufacture 53 (1): 1–14. doi: 10.1016/j.ijmachtools.2011.08.019.
  • Brezak, D., D. Majetic, T. Udiljak, and J. Kasac. 2012. “Tool Wear Estimation Using an Analytic Fuzzy Classifier and Support Vector Machines.” Journal of Intelligent Manufacturing 23 (3): 797–809. doi: 10.1007/s10845-010-0436-x.
  • Chen, D., G. Zhu, X. Huo, and X. Liu. 2017. “Quasi-Static Crushing of Different Configurations of Metal-Composites Hybrid Tubes Under Axiallateral Loading.” In 21st International Conference on Composite Materials (ICCM-21), Xian, China, August 20-25, 2017: 2996. China: Chinese Society for Composite Materials.
  • Cho, S., S. Asfour, A. Onar, and N. Kaundinya. 2005. “Tool Breakage Detection Using Support Vector Machine Learning in a Milling Process.” International Journal of Machine Tools and Manufacture 45 (3): 241–249. doi: 10.1016/j.ijmachtools.2004.08.016.
  • Das, S. R., D. Dhupal, and , and A. Kumar. 2015. “Experimental Investigation into Machinability of Hardened AISI 4140 Steel Using TiN Coated Ceramic Tool.” Measurement 62 (February): 108–126. doi: 10.1016/j.measurement.2014.11.008.
  • Demirpolat, H., R. Binali, A. D. Patange, S. S. Pardeshi, and S. Gnanasekaran. 2023. “Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape During Sustainable Turning of Bearing Steel.” Materials 16 (12): 4408. doi: 10.3390/ma16124408.
  • Dimla, D. E., and P. M. Lister. 2000a. “On-Line Metal Cutting Tool Condition Monitoring.: I: Force and Vibration Analyses.” International Journal of Machine Tools and Manufacture 40 (5): 739–768. doi: 10.1016/S0890-6955(99)00084-X.
  • Dimla, D. E., and P. M. Lister. 2000b. “On-Line Metal Cutting Tool Condition Monitoring.: II: Tool-State Classification Using Multi-Layer Perceptron Neural Networks.” International Journal of Machine Tools and Manufacture 40 (5): 769–781. doi: 10.1016/S0890-6955(99)00085-1.
  • Dinakaran, D., S. Sampathkumar, and N. Sivashanmugam. 2009. “An Experimental Investigation on Monitoring of Crater Wear in Turning Using Ultrasonic Technique.” International Journal of Machine Tools and Manufacture 49 (15): 1234–1237. doi: 10.1016/J.IJMACHTOOLS.2009.08.001.
  • Duc, P. M., L. H. Giang, M. D. Dai, and D. T. Sy. 2020. “An Experimental Study on the Effect of Tool Geometry on Tool Wear and Surface Roughness in Hard Turning.” Advances in Mechanical Engineering 12 (9): 168781402095988. doi: 10.1177/1687814020959885.
  • Elsadek, A. A., A. M. Gaafer, S. S. Mohamed, and A. A. Mohamed. 2020. “Prediction and Optimization of Cutting Temperature on Hard-Turning of AISI H13 Hot Work Steel.” SN Applied Sciences 2 (4): 1–12. doi: 10.1007/s42452-020-2303-5.
  • Elsiti, N. M. I., and M. H. S. Elmunafi. 2023. “Corresponding Author: Nagwa Mejid Ibrahim Elsiti Optimization of Machining Parameters for Turning Process by Using Grey Relational Analysis.” World Journal of Advanced Research & Reviews 17 (1): 756–761. doi: 10.30574/wjarr.2023.17.1.0080.
  • Gutnichenko, O., M. Nilsson, R. Lindvall, V. Bushlya, and M. Andersson. 2021. “Improvement of Tool Utilization When Hard Turning with CBN Tools at Varying Process Parameters.” Wear 477:203900. doi: 10.1016/j.wear.2021.203900.
  • Kelmers, E., A. Szuba, S. W. King, J. Palan, S. Freear, H. G. Pandit, and B. H. van Duren. 2022. “Smart Knee Implants: An Overview of Current Technologies and Future Possibilities.” Indian Journal of Orthopaedics 57 (5): 635–642. doi: 10.1007/s43465-022-00810-5.
  • Khan, S. A., M. F. Ameer, G. M. Uddin, M. A. Ali, S. Anwar, M. U. Farooq, and A. Alfaify. 2022. “An In-Depth Analysis of Tool Wear Mechanisms and Surface Integrity During High-Speed Hard Turning of AISI D2 Steel via Novel Inserts.” International Journal of Advanced Manufacturing Technology 122 (9–10): 4013–4028. doi: 10.1007/s00170-022-10151-0.
  • Kumar, R., and S. Shelare. 2019. “Different Method of Fabrication of Composite Material-A Review.” Journal of Emerging Technologies in Accounting 6 (3): 530–538.
  • Mahir, A., Ö. Barış, and K. Fuat. 2023. “Effect of PVD-TiN and CVD-Al2O3 Coatings on Cutting Force, Surface Roughness, Cutting Power, and Temperature in Hard Turning of AISI H13 Steel.” Journal of Materials Engineering and Performance 32 (3): 1390–1401. doi: 10.1007/s11665-022-07190-9.
  • Manoj, I. V., H. Soni, S. Narendranath, P. M. Mashinini, and K. Fuat. 2022. “Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX.” Advances in Materials Science and Engineering 2022:1–9. doi: 10.1155/2022/5192981.
  • Osička, K., J. Zouhar, P. Sliwková, and J. Chladil. 2022. “Cutting Force When Machining Hardened Steel and the Surface Roughness Achieved.” Applied Sciences 12 (22): 11526. doi: 10.3390/app122211526.
  • Özel, T., and Y. Karpat. 2005. “Predictive Modeling of Surface Roughness and Tool Wear in Hard Turning Using Regression and Neural Networks.” International Journal of Machine Tools and Manufacture 45 (4–5): 467–479. doi: 10.1016/j.ijmachtools.2004.09.007.
  • Padhan, S., S. R. Das, A. Das, M. S. Alsoufi, A. M. M. Ibrahim, and A. Elsheikh. 2022. “Machinability Investigation of Nitronic 60 Steel Turning Using SiAlON Ceramic Tools Under Different Cooling/Lubrication Conditions.” Materials 15 (7): 2368. doi: 10.3390/ma15072368.
  • Pal, S., P. S. Heyns, B. H. Freyer, N. J. Theron, and S. K. Pal. 2011. “Tool Wear Monitoring and Selection of Optimum Cutting Conditions with Progressive Tool Wear Effect and Input Uncertainties.” Journal of Intelligent Manufacturing 22 (4): 491–504. doi: 10.1007/s10845-009-0310-x.
  • Qadri, S. I. A., G. A. Harmain, and M. F. Wani. 2020. “Influence of Tool Tip Temperature on Crater Wear of Ceramic Inserts During Turning Process of Inconel-718 at Varying Hardness.” Tribology in Industry 42 (2): 310–326. doi: 10.24874/ti.776.10.19.05.
  • Rizal, M., J. A. Ghani, M. Z. Nuawi, and C. H. C. Haron. 2013. “Online Tool Wear Prediction System in the Turning Process Using an Adaptive Neuro-Fuzzy Inference System.” Applied Soft Computing 13 (4): 1960–1968. doi: 10.1016/j.asoc.2012.11.043.
  • Sahoo, A. K., and B. Sahoo. 2013. “Performance Studies of Multi-Layer Hard Surface Coatings (TiN/TiCN/Al2O3/TiN) of Indexable Carbide Inserts in Hard Machining: Part-I (An Experimental Approach.” Measurement 46 (8): 2854–2867. doi: 10.1016/j.measurement.2013.03.024.
  • Scheffer, C., H. Kratz, P. S. Heyns, and F. Klocke. 2003. “Development of a Tool Wear-Monitoring System for Hard Turning.” International Journal of Machine Tools and Manufacture 43 (10): 973–985. doi: 10.1016/S0890-6955(03)00110-X.
  • Sick, B. 2002. “On-Line and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: A Review of More Than a Decade of Research.” Mechanical Systems and Signal Processing 16 (4): 487–546. doi: 10.1006/mssp.2001.1460.
  • Siddhpura, A., and R. Paurobally. 2012. “A Review of Flank Wear Prediction Methods for Tool Condition Monitoring in a Turning Process.” The International Journal of Advanced Manufacturing Technology 65 (1–4): 371–393. doi: 10.1007/S00170-012-4177-1.
  • Singh, J. I. P., P. Gulati, R. Dhiman, and M. Singh. 2019. “Flax Fiber Reinforced Polymer Composites: A Review.” SSRN Electronic Journal 6 (1): 828–835.
  • Suresh, R., S. Basavarajappa, and G. L. Samuel. 2012. “Some Studies on Hard Turning of AISI 4340 Steel Using Multilayer Coated Carbide Tool.” Measurement 45 (7): 1872–1884. doi: 10.1016/j.measurement.2012.03.024.
  • Touggui, Y., S. Belhadi, S. E. Mechraoui, A. Uysal, M. A. Yallese, and M. Temmar. 2020. “Multi-Objective Optimization of Turning Parameters for Targeting Surface Roughness and Maximizing Material Removal Rate in Dry Turning of AISI 316L with PVD-Coated Cermet Insert.” SN Applied Sciences 2 (8): 1360. doi: 10.1007/s42452-020-3167-4.
  • Wang, X., W. Wang, Y. Huang, N. Nguyen, and K. Krishnakumar. 2008. “Design of Neural Network-Based Estimator for Tool Wear Modeling in Hard Turning.” Journal of Intelligent Manufacturing 19 (4): 383–396. doi: 10.1007/s10845-008-0090-8.
  • Zhang, C., and H. Zhang. 2015. “Modelling and Prediction of Tool Wear Using LS-SVM in Milling Operation.” International Journal of Computer Integrated Manufacturing 29 (1): 76–91. doi: 10.1080/0951192X.2014.1003408.

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