Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. (2021)

• Record Type: Journal Article
• Authors: Panwar, Vishwanath; Kumar Sharma, Dilip; Pradeep Kumar, K.V.; Jain, Ankit; Thakar, Chetan
• Is Part Of: Materials today. Volume 46:Part 15(2021)
• Page Start: 6474
• Page End: 6481
• Publication Date: 2021
• Subjects: Surface roughness -- Response surface methodology -- CNC turning -- Genetic algorithm; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.03.642
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In the present analysis 15 experiments were performed in conjunction with the Box-Behnken architecture matrix based on the machining parameter's effect, like spindle speed, feed rate, and cutting width., A surface roughness mathematically framework was designed using the surface reaction methods of this model to aid a genetic algorithm. Which is used to decide the optimum machining parameters. Response surface methodology has been used in this paper due to certain advantages as compare to other methodology such as it needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. Finally, a genetic algorithm was used to determine the optimum setting of process parameters that maximize the rate of content removal. The best surface roughness response value obtained from single-objective genetic algorithm optimization was 1.19 μm.