There are many ways to accomplish optimization of machining process. In the experimental part of this paper, the optimization of machining process is accomplished from the aspect of cutting conditions, which are independent variables (speed, feed, and cutting depth), whereas the dependent variables refer to the surface roughness, more precisely, arithmetic average of surface heights and resultant cutting force, including main cutting force, feed force and thrust force. Once the matrix of the experimental plan has been created, on the basis of a complete multifactor plan with eight points, with previously determined upper, middle and lower levels of cutting parameters for turning, the experiment followed. Once the results of surface roughness were measured by perthometer, as well as results of cutting forces by dynamometer, the optimization of machining process was treated by means of predetermined methodologies, described in this paper.
Stanić J. Metod inženjerskih mjerenja– Osnove matematičke teorije eksperimenata, Univerzitet u Beogradu, Mašinski fakultet. 1981.
2.
Suresh PVS, Rao PV, G S. Deshmukh: A genetic algorithmic approach for optimization of surface roughness prediction model; Mechanical Engineering Department. 2002.
3.
Ekinović S, Brdarević S. Elma Ekinović: The relation of percentage of lean to machining conditions and worpiece material microhardness, Faculty of Mechanical Engineering. 1998.
4.
Ch. Deva Raj CS, Rao PM, D. Suneel: Taguchi design optimization of cutting parameters for Surface Roughness in turning INCONEL. International Research Journal of Engineering and Technology. 2016.
5.
Zuperl U. Franci Cus: Optimization of cutting conditions during cutting byusing neural networks. Vol. 19, Robotics and Computer Integrated Manufacturing. 2003. p. 189–99.
6.
Nagarwala F, Pranav Mankikar VSK. Design of Experiments for optimization of Cutting Parameters for Turning of AA7075aluminium alloy. International Journal of Current Engineering and Technology. 2017.
7.
Borate R, Baliram Jadhv A. Chougule: Optimization Of Cutting Parameters To Minimize Machining Time In Cnc Turning Of 34CrNiMo6 Alloy Steel Using Taguchi Method; International.
8.
Mechanical And Production Engineering ISSN. Vol. 2. 2320.
9.
Stojanović B, Babić M, Veličković S, Blagojević J. Optimization of Wear Behaviour in Aluminium Hybrid Composites Using Taguchi Method. In: 14th International Conference on Tribology. 2015. p. 13–5.
10.
Begović E, Plančić I. Sabahudin Ekinović, Amer Sarajlić: FDM 3D Printing Process Parameters, Optimization Using Taguchi Method for Improving the Gear Strength.
11.
Vol. 10, International Journal of Advanced Research. 2022. p. 782-788,.
12.
Sreekanth DV, M. Sreenivasa Rao:Optimization of Process Parameters of Abrasive Jet Machining on Hastelloy through Response Surface Methodology. Vol. 14, Journal of the Institute of Engineering. 2018. p. 170-178,.
13.
Veli-Matti Tapani Taavitsainen: Experimental Optimization and Response Surfaces. 2012.
14.
Liu F, Shi Y. Lotfollah Najjar:Application of Design of Experiment Method for Sports Results Predicition. Vol. 122, Procedia Computer Science. 2017. p. 720-726,.
15.
Krishnaiah K, P. Shahabudeen: Applied Design of Experiments and Taguchi Methods, ISBN-978-81-203-4527-0. 2012.
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