[1]
N.R. Baddoo, Stainless steel in construction: a review of research, applications, challenges and opportunities. Journal of Construction Steel Research, 64 (2008) 1199-1206.
DOI: 10.1016/j.jcsr.2008.07.011
Google Scholar
[2]
S.N. Prasad, M.N. Rao, Stainless steel- a versatile engineering material for critical applications, Advanced Material Research, 794 (2013) 44-49.
DOI: 10.4028/www.scientific.net/amr.794.44
Google Scholar
[3]
N. Sharma, K. Gupta, Influence of Coated and Uncoated Carbide Tools on Tool Wear and Surface Quality during Dry Machining of Stainless Steel 304, Materials Research Express, 6 (2019) 086585.
DOI: 10.1088/2053-1591/ab1e59
Google Scholar
[4]
G.M. Krolczyk, P. Nieslony, R.W. Maruda, S. Wojciechowski, Dry cutting effect in turning of a duplex stainless-steel as a key factor in clean production. Journal of Cleaner Production, 142 (2017) 3343-3354.
DOI: 10.1016/j.jclepro.2016.10.136
Google Scholar
[5]
K. Gupta, R.F. Laubscher, Sustainable Machining of Titanium Alloys- A Critical Review", Proc. IMechE, Part B: Journal of Engineering Manufacture, 231:14 (2017) 2543-2560.
DOI: 10.1177/0954405416634278
Google Scholar
[6]
S. Debnath, M.M. Reddy, Q.S. Yi, Environmental friendly cutting fluids and cooling techniques in machining: a review. Journal of cleaner production 83 (2014), 33-47.
DOI: 10.1016/j.jclepro.2014.07.071
Google Scholar
[7]
B.A. Beatrice, E. Kirubakaran, P.R. Jeba Thangaiah, K.L. Dev Wins. Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application. Proced. Eng. 97 (2014) 205-211.
DOI: 10.1016/j.proeng.2014.12.243
Google Scholar
[8]
M. Sarıkaya, A. Gullu, Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J. Clean. Prod. 65 (2014) 604-616.
DOI: 10.1016/j.jclepro.2013.08.040
Google Scholar
[9]
J. Sharma, B.S. Sidhu, Investigation of effects of dry and near dry machining on AISI D2 steel using vegetable oil. J. Clean. Prod. 66 (2014) 619-623.
DOI: 10.1016/j.jclepro.2013.11.042
Google Scholar
[10]
Kovač P. et al. Modelling and Optimization of Surface Roughness Parameters of Stainless Steel by Artificial Intelligence Methods. In: Durakbasa N., Gençyılmaz M. (eds) Proceedings of the International Symposium for Production Research 2019. ISPR 2019, ISPR 2019. Lecture Notes in Mechanical Engineering 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_1.
DOI: 10.1007/978-3-030-31343-2_1
Google Scholar
[11]
N.A. Zolpakar, S. Pathak, S. Pathak, M. Anand, Application of Multi-Objective Genetic Algorithm (MOGA) Optimization in Manufacturing Processes, 185-199. In: K. Gupta and M.K. Gupta (eds) Optimization of Manufacturing Processes, 2020, Springer.
DOI: 10.1007/978-3-030-19638-7_8
Google Scholar
[12]
S. Pathak, Intelligent Manufacturing, 2021, Springer.
Google Scholar
[13]
M. Gul, E. Celik, N. Aydin, A.T. Gumus, A.F. Guneri, A state of the art literature review of VIKOR and its fuzzy extensions on applications. Appl. Soft Comput.46 (2016) 60–89.
DOI: 10.1016/j.asoc.2016.04.040
Google Scholar
[14]
A. Khan, K. Maity, A novel MCDM approach for simultaneous optimization of some correlated machining parameters in turning of CP-titanium grade 2, I. J. Eng. Res. Afr. 22 (2016) 94.
DOI: 10.4028/www.scientific.net/jera.22.94
Google Scholar
[15]
J. Kennedy, R.C. Eberhart, Particle swarm optimization. In Proceedings of the International Conference on Neural Networks. IEEE, 4 (1995) 1942– (1948).
Google Scholar
[16]
Y. Shi, R.C. Eberhart, A modified particle swarm optimizer. In Proceedings of the International Conference on Evolutionary Computation. IEEE, (1998) 69–73.
DOI: 10.1109/icec.1998.699146
Google Scholar
[17]
X.Y. Gu, C.Y. Dong, T. Cheng, MPM simulations of high-speed machining of Ti6Al4V titanium alloy considering dynamic recrystallization phenomenon and thermal conductivity. Appl. Math. Modell, 56 (2018) 517–538.
DOI: 10.1016/j.apm.2017.12.028
Google Scholar