Title: Surface roughness prediction model of micro-milling Inconel 718 with consideration of tool wear

Authors: Xiaohong Lu; Zhenyuan Jia; Hua Wang; Likun Si; Xinxin Wang

Addresses: Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China ' Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China

Abstract: During micro-milling Inconel 718, relationship between surface roughness and cutting parameters is studied. Taking the spindle speed, feed per tooth, axial depth of cut and cutting time into consideration, a prediction model, based on the orthogonal test, has been established to predict the surface roughness of nickel-base superalloy Inconel 718 by micro-milling. Neural network method is used to build surface roughness prediction model. As the cutting time changes, the surface roughness value of Inconel 718 under different cutting parameters changes, and the variation trend is able to provide reference for changing tools in time to ensure the surface quality of parts. The research on nickel-base superalloy micro milling, which could help us figure out the change regulation between micro groove surface roughness along with the cutting parameters and machining time, provides significant guidance for deep research on surface quality of micro-milling nickel-base superalloy Inconel 718 machining mechanism.

Keywords: micromilling; nickel-base superalloys; Inconel 718; surface roughness; tool wear; prediction modelling; micromachining; neural networks; spindle speed; feed per tooth; axial depth of cut; cutting time; microgrooves; surface quality.

DOI: 10.1504/IJNM.2016.076161

International Journal of Nanomanufacturing, 2016 Vol.12 No.1, pp.93 - 108

Received: 05 Nov 2015
Accepted: 27 Nov 2015
Published online: 27 Apr 2016 *

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