Title: Modelling and optimisation of cutting parameters on surface roughness in micro-milling Inconel 718 using response surface methodology and genetic algorithm

Authors: Xiaohong Lu; Furui Wang; Xinxin Wang; Likun Si

Addresses: Key Laboratory for Precision and Nontraditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 Linggong Road, Dalian, Liaoning Province, China ' Key Laboratory for Precision and Nontraditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 Linggong Road, Dalian, Liaoning Province, China ' Key Laboratory for Precision and Nontraditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 Linggong Road, Dalian, Liaoning Province, China ' Key Laboratory for Precision and Nontraditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 Linggong Road, Dalian, Liaoning Province, China

Abstract: In recent years, micro-milling techniques have attracted great attention and interest from academia and industry. Inconel 718 is a nickel-based superalloy with good tensile, fatigue, creep and rupture strength and can find great application in nuclear and aerospace industry. In this paper, the response surface methodology (RSM) was applied to develop the model for predicting surface roughness in micro-milling Inconel 718. The magnitudes of cutting parameters affecting the surface roughness, which were depth of cut, spindle speed, and feed rate, were analysed by the analysis of variance (ANOVA). The validity of the surface roughness prediction model was proved due to the tiny error between the measured values and the prediction results. Then, genetic algorithm (GA) was used to determine the optimal cutting parameters achieving minimum surface roughness in micro-milling Inconel 718 process. All experiments show that the optimised results agree well with the test ones.

Keywords: micro-milling; Inconel 718; surface roughness; response surface methodology; RSM; genetic algorithm.

DOI: 10.1504/IJNM.2018.089178

International Journal of Nanomanufacturing, 2018 Vol.14 No.1, pp.34 - 50

Received: 19 Apr 2016
Accepted: 07 Nov 2016
Published online: 09 Jan 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article