Prediction of tool wear based cutting forces during end milling of Inconel 718 using artificial neural networks

• Autorzy: Felusiak-Czyryca Agata , Twardowski Paweł

Identyfikatory

DOI

10.12913/22998624/204203

• Języki publikacji: EN

Abstrakty

EN

During the research, correlation between the input parameters (cutting parameters and cutting forces measure like peak to peak, root mean square and root mean square of ripple) and the variables were searched for, and the sensitivity of the network to input parameters was determined. In this paper artificial neural networks (ANNs) to prediction of tool wear based on cutting forces were used. Multilayer perceptron (MLP) networks with backward error propagation were used. The research shows that for the tested material and in the tested range, the cutting parameters are not diagnostically significant for the prediction of VBC (band width of the corner wear). The authors of this article focus on simplifying the model and analyzing the influence of variables on the prediction error. Neural networks show a correlation of about 95% for test sets.

Słowa kluczowe

EN

Inconel 718; machining; cutting forces; tool wear; prediction; artificial neural network; ANN

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 7
• Strony: 394--405
• Opis fizyczny: Bibliogr. 21 poz., fig., tab.

Twórcy

autor

Felusiak-Czyryca Agata

• Faculty of Mechanical Engineering, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznan, Poland

• https://orcid.org/0000-0002-4927-4120

autor

Twardowski Paweł

• Faculty of Mechanical Engineering, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznan, Poland

• https://orcid.org/0000-0002-1823-6279

Bibliografia

• 1. Wojciechowski, S.; Przestacki, D.; Chwalczuk, T. The evaluation of surface integrity during machining of Inconel 718 with various laser assistance strategies. MATEC Web of Conferece 2017; 136, 01006-1–01006-6.
• 2. Zhou, J.; Bushlya, V., Peng, R.L.; Johansson, S.; Stahl, J.E. Analysis of subsurface microstructure and residual stresses in machined Inconel 718 with PCBN and Al2O3-SiCw tools. Procedia CIRP 2014; 13, 150–155.
• 3. Xavior, M.A.; Manohar, M.; Madhukar, P.M.; Jeyapandiarajan, P. Experimental investigation of work hardening, residual stress and microstructure during machining Inconel 718. J. of Mechanical Science and Technology 2017; 31(10), 4789–4794.
• 4. Ren, X.-P.; Liu, Z.-Q. Microstructure refinement and work hardening in a machined surface layer induced by turning Inconel 718 super alloy. Int. J. of Minerals, Metallurgy and Materials 2018; 25(8), 937–949.
• 5. Liao, Y.S.; Lin, H.M.; Wang, J.H. Behaviors of end milling Inconel 718 superalloy by cemented carbide tools. J. of Materials Processing Technology 2008; 201(3–1), 460–465.
• 6. Musfirah, A.H.; Ghani, J.A.; Haron, C.H.C. Tool wear and surface integrity of inconel 718 in dry and cryogenic coolant at high cutting speed. Wear 2017; 376–377, 125–133.
• 7. Li, W.; Guo, Y.B.; Barkey, M.E.; Jordon, J.B. Effect tool wear during end milling on the surface integrity and fatigue life of inconel 718. Procedia CIRP 2014; 14, 546–551.
• 8. Mrkvica, I.; Špalek, F.; Szotkowski, T. Influence of cutting tool wear when milling Inconel 718 on resulting roughness. Manufacturing Technology 2018; 18(3), 457–461.
• 9. Drouillet, C.; Karandikar, J.; Nath, C.; El Mansori, M.; Kurfess, T. Tool life predictions in milling using spindle power with the neural network technique. J. of Manufacturing Processes 2016; 22, 161–168.
• 10. Twardowski, P.; Wiciak-Pikula, M. Prediction of tool wear using artificial neural networks during turning of hardened steel. Materials 2019; 12(19), 3091-1–3091-15.
• 11. Liu, M.; Yao, X.; Zhang, J.; Jing, X.; Wang, K. Multisensor data fusion for remaining useful life prediction of machining tools by iabc-bpnn in dry milling operations. Sensors 2020; 20(17), 4657, 1–24.
• 12. Lu, X.; Jia, Z.; Wang, H.; Wang, X.; Jia. Surface roughness prediction model of micro-milling Inconel 718 with consideration of tool wear. Int. J. of Nanomanufacturing 2016; 12(1), 93–108.
• 13. Khorasani, A.M.; Yazdi, M.R.S. Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int. J. of Advanced Manufacturing Technology 2017; 93(1–4), 141–151.
• 14. Prasad, M.V.R.D.; Sravya, Y.; Tejaswi, K.S. Study of the influence of process parameters on surface roughness when Inconel 718 is dry turned using CBN cutting tool by artificial neural network approach. Int. J. of Mat., Mech. and Manuf. 2014; 2(4), 335–338.
• 15. D’Addona, D.; Segreto, T.; Simeone, A.; Teti, R. ANN tool wear modelling in the machining of nickel superalloy industrial products. CIRP Journal of Manufacturing Science and Technology 2011; 4(1), 33–37.
• 16. Leone, C.; D’Addona, D.; Teti, R. Tool wear modelling through regression analysis and intelligent methods for nickel base alloy machining. CIRP Journal of Manufacturing Science and Technology 2011; 4(3), 327–331.
• 17. Kalra, G.; Gupta, A.K. Mathematical modeling to estimate machining time during milling of Inconel 718 workpiece using ANN. Materials Today: Proceedings 2023; 78, 546–554.
• 18. Sen, B.; Mia, M.; Mandal, U.K.; Mondal, S.P. GEP- and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690. International Journal of Advanced Manufacturing Technology 2019; 105(1–4), 395–410.
• 19. Kaya, B.; Oysu, C.; Ertunc, H.M. Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Advances in Engineering Software 2011; 42(3), 76–84.
• 20. Bagga, P.J.; Chavda, B.; Modi, V.; Makhesana, M.A.; Patel, K.M. Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining. Materials Today: Proceedings 2022; 51–55.
• 21. Han, C.; Kim, K.B.; Lee, S.W.; Jun, M.B.-G.; Jeong, Y.H. International Journal of Precision Engineering and Manufacturing 2021; 22(9), 1527–1536.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).