Title: A surface roughness prediction model using response surface methodology in micro-milling Inconel 718

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

Addresses: Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 LingGong Road, DaLian, LiaoNing Province, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 LingGong Road, DaLian, LiaoNing Province, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 LingGong Road, DaLian, LiaoNing Province, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 LingGong Road, DaLian, LiaoNing Province, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, No. 2 LingGong Road, DaLian, LiaoNing Province, China

Abstract: In this paper, a surface roughness prediction model of micro-milling Inconel 718 by applying response surface methodology (RSM) is presented. The experiments based on centre composite rotatable design (CCRD) are designed to conduct the experiments. The cutting parameters considered are depth of cut, spindle speed and feed rate. Statistical methods, analysis of variance (ANOVA), are used to analyse the adequacy of the predictive model. The influence of each micro-milling parameter on surface roughness is analysed; also the magnitude order of parameters is determined. Depth of cut is found to be the critical influence factor. At last, the parameters interaction on surface roughness of micro-milling Inconel 718 is discussed by graphical means through MATLAB.

Keywords: surface roughness; response surface methodology; RSM; micro-milling; analysis of variance; ANOVA; Inconel 718.

DOI: 10.1504/IJMMM.2017.084006

International Journal of Machining and Machinability of Materials, 2017 Vol.19 No.3, pp.230 - 245

Received: 21 Dec 2015
Accepted: 14 Mar 2016
Published online: 02 May 2017 *

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