Optimization of Machining Process for Improved Sustainability

Abstract:

Optimization procedures can be considered useful for machining applications. Most commonly, the optimization method is applied to search a trade-off between costs and profits, and it allows searching for the optimum cutting parameters to maximize the useful tool-life, minimize the time of production, etc. The selection of the optimal machining conditions which could maximize the process sustainability performance and the fatigue life of the machined product is the objective of the work presented. The numerical model developed is useful to have integrated results as input for the optimization algorithm in order to drastically reduce the number of experimental tests needed. In the present work, the optimization of the performance measures is carried out using a self-written Genetic Algorithm code implemented using the MATLAB software.

• Periodical: Key Engineering Materials (Volume 926)
• Pages: 1671-1677
• DOI: https://doi.org/10.4028/p-0804nf
• Online since: July 2022
• Authors: Giovanna Rotella, Rodolfo Franchi*, Antonio Del Prete
• Keywords: Genetic Algorithm, Machining, Optimization, Ti6Al4V

References

[1] J. Zamorano, M. Alfaro, V. M. De Oliveira, G. Fuertes, C. Duran, R. Ternero, J. Sabattin, M. Vargas, 2021, New manufacturing challenges facing sustainability, Manufacturing Letters, 30, 19-22. https://doi.org/10.1016/j.mfglet.2021.09.003.

DOI: 10.1016/j.mfglet.2021.09.003

[2] G. Rotella, 2012, Optimization of Machining Process for Improving Sustainability, PhD thesis.

[3] Jawahir, I. S. et al., Surface integrity in material removal processes: Recent advances, CIRP Ann. - Manuf. Technol., 60(2), 603–626, (2011),.

DOI: 10.1016/j.cirp.2011.05.002

[4] A.P.S. Lodhi, D. Kumar, T. Kaur, N. Singh, 2021, Development of lubricious, non-toxic, and corrosion-resistant metalworking fluid: A possible replacement for mineral oil-based fluids, Journal of Cleaner Production, 323, 129173.

DOI: 10.1016/j.jclepro.2021.129173

[5] Del Prete, A., Franchi, R., Cacace, S. Semeraro Q., (2020) Optimization of cutting conditions using an evolutive online procedure. J Intell Manuf 31, 481–499.

DOI: 10.1007/s10845-018-01460-x

[6] Franchi R., Del Prete A., Umbrello D., (2017) Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining, International Journal of Material Forming Volume 10, Issue 5, Pages 685 – 6951.

DOI: 10.1007/s12289-016-1311-x

[7] M. Tisza, I. Czinege, 2018, Comparative study of the application of steels and aluminium in lightweight production of automotive parts, International Journal of Lightweight Materials and Manufacture, 1-4, 229-238.

DOI: 10.1016/j.ijlmm.2018.09.001

[8] Rotella, G., Umbrello, D., 2014, Numerical simulation of surface modification in dry and cryogenic machining of AA7075 alloy, Procedia CIRP, 13:327–332,.

DOI: 10.1016/j.procir.2014.04.055

[9] Rotella, G., Dillon, O. W., Umbrello, D., Settineri, L., Jawahir, I. S., 2013, Finite element modeling of microstructural changes in turning of AA7075-T651 Alloy, Journal of Manufacturing Processes, 15/1:87–95,.

DOI: 10.1016/j.jmapro.2012.09.005

[10] Rotella G., 2019, Effect of surface integrity induced by machining on high cycle fatigue life of 7075-T6 aluminum alloy, Journal of Manufacturing Processes, 41, 83-91.

DOI: 10.1016/j.jmapro.2019.03.031

[11] Kardekar, A. D., 2005, Modeling and Optimization of Machining Performance Measures in Face Milling of Automotive Aluminum Alloy A380 under different Lubrication/Cooling Conditions for Sustainable Manufacturing. University of Kentucky.