Modelling of cutting parameters for Nilo 36 superalloy with machine learning methods and developing an interactive interface

Year 2021, , 79 - 86, 15.04.2021

Gültekin Basmacı , İsmail Kırbaş , Mustafa Ay

https://doi.org/10.35860/iarej.805124

Cited By: 3

Abstract

Superalloys have become increasingly used in the machining sector due to their high strength, temperature and machinability. One of these alloys, Nilo (Invar) 36, has a low thermal expansion and its use is rapidly increasing in areas where high temperature and expansion are not required, especially in composite mould applications, such as aerospace, electronics, measuring instruments and aerospace. In this study, a mathematical model based on artificial intelligence and an interactive visual interface in MATLAB software were developed according to the test results obtained from surface roughness Ra, cutting methods, rotational speeds, cooling method and cutting speed of Nilo 36 alloy. For the mathematical analysis of the measurements, the number of experiments to be performed by using Minitab program and Taguchi method was reduced to 32. The measurement results were modelled by Response Surface Design method and the factors affecting the surface roughness were determined in order of importance. A high-performance feed-forward artificial neural network has been developed using experimental data and an interactive interface has been prepared based on the developed model. Thus, the user can easily observe the cutting forces and surface roughness values for different cutting parameters with high accuracy.

Keywords

cutting forces, interface development, machine learning, Nilo 36 superalloy, Response Surface Design

Supporting Institution

Marmara University

Project Number

FEN-E 090517-0273

Thanks

Experiments were carried out by using the experimental equipment taken Depertmant of Mechanical Engineering within the scope of FEN-E 090517-0273 project supported by BAPKO of Marmara University, Turkey.

References

• 1. Ha, T.K. and Min, S.H., Effect of C Content on the Microstructure and Physical Properties of Fe-36Ni Invar Alloy, Eco-Materials Processing and Design XV, Trans Tech Publications Ltd, 2015. p. 293–296.
• 2. Maranhão, C. and Davim, J.P., Finite Element Modelling of Machining of AISI 316 Steel: Numerical Simulation and Experimental Validation, Simulation Modelling Practice and Theory, 2010. 18(2): p. 139–156.
• 3. Tekaslan, Ö., Gerger, N., Günay, M. and Şeker, U., Examination of the Cutting Forces of AISI 304 Austenitic Stainless Steel in the Turning Process with Titanium Carbide Coated Cutting Tools, Pamukkale Univiversitesi Muhendislik Bilim. Dergisi, 2007. 13(2): p. 135–144.
• 4. Li, D.W., Chen, H.T., Xu, M.H. and Zhong,C.M., Study on Turning Parameter Optimization of Austenitic Stainless Steel, Mechanical Engineering and Green Manufacturing, Trans Tech Publications Ltd, 2010. p. 1829–1833.
• 5. Diniz, A.E., Ferreira, J.R. and Filho, F.T., Influence of Refrigeration/Lubrication Condition on SAE 52100 Hardened Steel Turning at Several Cutting Speeds, International Journal of Machine Tools and Manufacture, 2003. 43(3): p. 317–326.
• 6. Kaladhar, M., Subbaiah, K. and Rao, Ch.S., Optimization of Surface Roughness and Tool Flank Wear in Turning of AISI 304 Austenitic Stainless Steel with CVD Coated Tool, Journal of Enginering Science Technology, 2013. 8: p 165–176.
• 7. Dirviyam, P.S. and Palanisamy, C., Optimization of Surface Roughness of AISI 304 Austenitic Stainless Steel in Dry Turning Operation Using Taguchi Design Method, Journal of Enginering Science Technology 2010. 5: p 1-9.
• 8. Basmaci, G., Ay, M. and Kırbaş, İ., Optimisation of Machining Parameters ın Turning 17-4 Ph Stainless Steel Using the Grey-Based Taguchi Method, Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2017. 10(2): p. 243–254.
• 9. Ay, M., Optimisation of Machining Parameters in Turning AISI 304L Stainless Steel by the Grey-Based Taguchi Method, Acta Physica Polonica A, 2017. 131: p. 349–354.
• 10. Basmaci, G., Ay, M., Optimization of Cutting Parameters, Condition and Geometry in Turning AISI 316L Stainless Steel Using the Grey-Based Taguchi Method, Acta Physica Polonica A, 2017. 131: p. 354–359.
• 11. Dhar, N.R., Kamruzzaman, M. and Ahmed, M., Effect of Minimum Quantity Lubrication (MQL) on Tool Wear and Surface Roughness in Turning AISI-4340 Steel, Journal of Matereials Processing Technoogy, 2006. 172(2): p. 299–304.
• 12. Itoigawa, F., Childs, T.H.C., Nakamura, T. and Belluco, W., Effects and Mechanisms in Minimal Quantity Lubrication Machining of an Aluminum Alloy, Wear., 2006. 260(3): p. 339–344.
• 13. Qin, S., Li, Z., Guo, G., An, Q., Chen, M. and Ming, W., Analysis of Minimum Quantity Lubrication (MQL) for Different Coating Tools during Turning of TC11 Titanium Alloy, Materials, 2016. 9(10): p. 1-13.
• 14. Sampaio, M.A., Machado, Á.R., Laurindo, C.A.H., Torres, R.D. and Amorim, F.L., Influence of Minimum Quantity of Lubrication (MQL) When Turning Hardened SAE 1045 Steel: A Comparison with Dry Machining, International Journal of Advanced Manufacturing Technology, 2018. 98(1):p. 959–968.
• 15. Basmaci, G., Kurt, M., Ay, M. and Bakir, B., Optimization of the Effects of Machining Parameters in Turning on Hastelloy C22 Composition through Taguchi Response Surface Methodology, Acta Physica Polonica A, 2018. 134, p. 28–31.
• 16. Mostafa, Y., Elbestawi, M.A., Veldhuis, S. C., Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L, Journal of Materials Processing Technology, 2019. 266: p. 397-420.
• 17. Kırbaş, İ., Peker, M., Basmaci, G. and Ay, M., Predictive Modeling and Optimization of Cutting Forces Through RSM and Taguchi Techniques in the Turning of ASTM B574 (Hastelloy C-22), Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering, IGI Global, 2018. p. 398–417,
• 18. Asiltürk, İ., Neşeli, S. and İnce, M.A., Optimisation of Parameters Affecting Surface Roughness of Co28Cr6Mo Medical Material during CNC Lathe Machining by Using the Taguchi and RSM Methods, Measurement, 2016. 78:p. 120–128.
• 19. Abou-El-Hossein, K.A., Kadirgama, K., Hamdi, M. and Benyounis, K.Y., Prediction of Cutting Force in End-Milling Operation of Modified AISI P20 Tool Steel, Journal of Materials Processing Technology, 2007. 182(1–3): p. 241–247.
• 20. Navneet, K., Gandhi, A., Nakum, B., Anil, S., Optimization And Analysis of Surface Roughness for Invar-36 End Milling Operations, Materials Today, 2018. 5: p. 5281-5288.
• 21. Dahbi, S., Ezzine, L., Moussami, H.E., Modeling of cutting performances in turning process using artificial neural networks, International Journal of Engineering Business Management, 2017. 9: p. 184-196.
• 22. Çakiroğlu, R., Yağmur, S., Acir, A., Şeker, U., Modelling of Drill Bit Temperature and Cutting Force in Drilling Process Using Artificial Neural Networks, 2017. 20(2): p. 333-340.
• 23. Kılıç, F., Effects of three drying methods on kinetics and energy consumption of carrot drying process and modeling with artificial neural networks, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020. p. 1-18.
• 24. Filippis, L.A.C.D., Serio, L.M., Facchini, F., Mummolo, G., ANN Modelling to Optimize Manufacturing Process, InTech, 2018. p. 32-38.
• 25. Gavin, H.P., The Levenberg-Marquardt algorithm fornonlinear least squares curve-fitting problems, Lecture Notes,DukeUniversity,2020.http://people.duke.edu/~hpgavin/ce281/lm.pdf.