Grinding parameters prediction under different cooling environments using machine learning techniques

References

• Wang, J.; Xu, J.; Wang, X.; Zhang, X.; Song, X.; Chen, X. A Comprehensive Study on Surface Integrity of Nickel-Based Superalloy Inconel 718 Under Robotic Belt Grinding. Mater. Manuf. Process. 2019, 34(1), 61–69. DOI: 10.1080/10426914.2018.1512137.
   Web of Science ®Google Scholar
• Mahesh, K.; Philip, J. T.; Joshi, S. N.; Kuriachen, B. Machinability of Inconel 718: A Critical Review on the Impact of Cutting Temperatures. Mater. Manuf. Process. 2021, 36(7), 753–791. DOI: 10.1080/10426914.2020.1843671.
   Web of Science ®Google Scholar
• Srirangarajalu, N.; Vijayakumar, R.; Rajesh, M. Multi Performance Investigation of Inconel-625 by Abrasive Aqua Jet Cutting. Mater. Manuf. Process. 2021, 1–12. DOI:10.1080/10426914.2021.2006225.
   Google Scholar
• Nikouei, S. M.; Razfar, M. R.; Khajehzadeh, M. Influence of Nanoparticles’ Size on Inconel 718 Machining Induced Residual Stresses. Mater. Manuf. Process. 2022, 37(9), 1003–1012. DOI: 10.1080/10426914.2021.2016821.
   Web of Science ®Google Scholar
• Reyes, L. A.; Garza, C.; Delgado, M.; Guerra-Fuentes, L.; López, L.; Zapata, O.; Cabriales, R. Cellular Automata Modeling for Rotary Friction Welding of Inconel 718. Mater. Manuf. Process. 2022, 37(8), 877–885. DOI: 10.1080/10426914.2021.2001514.
   Web of Science ®Google Scholar
• Kv, A.; Hariharan, P. Performance Comparison of EDM Oil and Biodiesel Flushing Media While Μed Milling of Inconel 718. Mater. Manuf. Process. 2022, 1–18. DOI:10.1080/10426914.2022.2089888.
   Google Scholar
• Anburaj, R.; Pradeep Kumar, M. Influences of Cryogenic CO2 and LN2 on Surface Integrity of Inconel 625 During Face Milling. Mater. Manuf. Process. 2021, 36(16), 1829–1839. DOI: 10.1080/10426914.2021.1914850.
   Web of Science ®Google Scholar
• Pinheiro, C.; Kondo, M. Y.; Amaral, S. S.; Callisaya, E. S.; De Souza, J. V. C.; De Sampaio Alves, M. C.; Ribeiro, M. V. Effect of Machining Parameters on Turning Process of Inconel 718. Mater. Manuf. Process. 2021, 36(12), 1421–1437. DOI: 10.1080/10426914.2021.1914839.
   Web of Science ®Google Scholar
• Hagiwara, Y.; Ishida, M.; Oka, T.; Watanabe, R.; Koji, S. Development of Nickel-Base Superalloy for Exhaust Valves. SAE Tech. Pap. 1991, 100, 484–494. DOI: 10.4271/910429.
   Google Scholar
• Varalakshmi, M. P.; Upendar, K.; Ranjani, M. P.; Goud, N. R.; Khan, P. A. A Review Paper on Inconel Alloys. 2020, 8(7), 3180. www.ijcrt.org
   Google Scholar
• Seleznev, A.; Pinargote, N. W. S.; Smirnov, A. Ceramic Cutting Materials and Tools Suitable for Machining High‐temperature Nickel‐based Alloys: A Review. Metals (Basel). 2021, 11(9), 1–18. DOI: 10.3390/met11091385.
   Web of Science ®Google Scholar
• Koseki, S.; Inoue, K.; Usuki, H. Damage of Physical Vapor Deposition Coatings of Cutting Tools During Alloy 718 Turning. Precis. Eng. 2016, 44, 41–54. DOI: 10.1016/j.precisioneng.2015.09.012.
   Web of Science ®Google Scholar
• Costes, J. P.; Guillet, Y.; Poulachon, G.; Dessoly, M. Tool-Life and Wear Mechanisms of CBN Tools in Machining of Inconel 718. Int. J. Mach. Tools Manuf. 2007, 47(7), 1081–1087. DOI: 10.1016/j.ijmachtools.2006.09.031.
   Web of Science ®Google Scholar
• Akhyar Ibrahim, G.; Che Haron, C. H.; Abdul Ghani, J.; Said, A. Y. M.; Abu Yazid, M. Z. Performance of PVD-Coated Carbide Tools When Turning Inconel 718 in Dry Machining. Adv. Mech. Eng. 2011. DOI: 10.1155/2011/790975.
   Google Scholar
• Arun, A.; Rameshkumar, K.; Unnikrishnan, D.; Sumesh, A. Tool Condition Monitoring of Cylindrical Grinding Process Using Acoustic Emission Sensor. Mater. Today Proc. 2018, 5(5), 11888–11899. DOI: 10.1016/j.matpr.2018.02.162.
   Google Scholar
• Gopan, V.; Ragavanantham, S.; Sampathkumar, S. Condition Monitoring of Grinding Process Through Machine Vision System. Int. Conf. Mach. Vis. Image Process. 2012, 177–180. DOI: 10.1109/MVIP.2012.6428789.
   Google Scholar
• Liao, T. W.; Hua, G.; Qu, J.; Blau, P. J. Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods. Mach. Sci. Technol. 2007, 10(4), 511–538. DOI: 10.1080/10910340600996175.
   Web of Science ®Google Scholar
• Jafarian, F.; Umbrello, D.; Golpayegani, S.; Darake, Z. Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining. Mater. Manuf. Process. 2016, 31(13), 1683–1691. DOI: 10.1080/10426914.2015.1090592.
   Web of Science ®Google Scholar
• Taewan Lee, E.; Fan, Z.; Sencer, B. Real-Time Grinding Wheel Condition Monitoring Using Linear Imaging Sensor. Procedia Manuf. 2020, 49, 139–143. DOI: 10.1016/j.promfg.2020.07.009.
   Google Scholar
• Baseri, H. Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance. Mater. Manuf. Process. 2011, 26(5), 757–763. DOI: 10.1080/10426911003636951.
   Web of Science ®Google Scholar
• Hamed, A.; Ashtiani, A. S.; Rahimi, A. In-Process Monitoring of Nickel-Based Super Alloy Grinding Using the Acoustic Emission Method. Russ. J. Nondestruct. Test. 2019, 55(12), 909–917. DOI: 10.1134/S1061830919120027.
   Web of Science ®Google Scholar
• Konda, N.; Verma, R.; Jayaganthan R. Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively ManufacturedTi6al4v. Metals. 2021, 12(1), 50. DOI: 10.3390/met12010050.
   Web of Science ®Google Scholar
• Mirifar, S.; Kadivar, M.; Azarhoushang, B. First Steps Through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors. J. Manuf. Mater. Process. 2020, 4(2). DOI: 10.3390/jmmp4020035.
   Google Scholar
• Alajmi, M. S.; Almeshal, A. M. Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method. Mater. (Basel). 2020, 13(13), 1–23. DOI: 10.3390/ma13132986.
   Web of Science ®Google Scholar
• Guo, W.; Wu, C.; Ding, Z.; Zhou, Q. Prediction of Surface Roughness Based on a Hybrid Feature Selection Method and Long Short-Term Memory Network in Grinding. Int. J. Adv. Manuf. Technol. 2021, 112(9), 2853–2871. DOI: 10.1007/s00170-020-06523-z.
   Web of Science ®Google Scholar
• Liu, Y.; Song, S.; Zhang, Y.; Li, W.; Xiao, G. Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on Rlsom-Rbf. Mater. (Basel). 2021, 14(19). DOI: 10.3390/ma14195701.
   Web of Science ®Google Scholar
• Venkata Rao, R.; Kalyankar, V. D. Parameter Optimization of Machining Processes Using a New Optimization Algorithm. Mater. Manuf. Process. 2012, 27(9), 978–985. DOI: 10.1080/10426914.2011.602792.
   Web of Science ®Google Scholar
• Sauter, E.; Sarikaya, E.; Winter, M.; Wegener, K. In-Process Detection of Grinding Burn Using Machine Learning. Int. J. Adv. Manuf. Technol. 2021, 115(7), 2281–2297. DOI: 10.1007/s00170-021-06896-9.
   Web of Science ®Google Scholar
• Safarzadeh, H.; Leonesio, M.; Bianchi, G.; Monno, M. Roundness Prediction in Centreless Grinding Using Physics-Enhanced Machine Learning Techniques. Int. J. Adv. Manuf. Technol. 2021, 112(3), 1051–1063. DOI: 10.1007/s00170-020-06407-2.
   Web of Science ®Google Scholar
• Dörr, M.; Ott, L.; Matthiesen, S.; Gwosch, T. Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning. Sensors. 2021, 21(21), 7147. DOI: 10.3390/s21217147.
   PubMed Web of Science ®Google Scholar
• Ai, Q.; Khosravi, J.; Azarhoushang, B.; Daneshi, A.; Becker, B. Digital Light Processing-Based Additive Manufacturing of Resin Bonded SiC Grinding Wheels and Their Grinding Performance. Int. J. Adv. Manuf. Technol. 2022, 118(5), 1641–1657. DOI: 10.1007/s00170-021-08016-z.
   Web of Science ®Google Scholar
• Sauter, E.; Winter, M.; Wegener, K. Analysis of Robustness and Transferability in Feature-Based Grinding Burn Detection. Int. J. Adv. Manuf. Technol. 2022, 120(3), 2587–2602. DOI: 10.1007/s00170-02208834-9.
   Web of Science ®Google Scholar
• Kishore, K.; Sinha, M. K.; Singh, A.; Archana; Gupta, M. K.; Korkmaz, M. E. A Comprehensive Review on the Grinding Process: Advancements, Applications and Challenges. Proc. Inst. Mech. Eng. Part C. 2022. DOI: 10.1177/09544062221110782.
   PubMed Web of Science ®Google Scholar