Title: Research on the prediction model of micro-milling surface roughness

Authors: Xinxin Wang; Xiaohong Lu; Zhenyuan Jia; Xv Jia; Guangjun Li; Wenyi Wu

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

Abstract: Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness-prediction model with high-precision is helpful to select the cutting parameters for micro-milling. Two prediction models are established by response surface method (RSM) and support vector machine regression (SVM) in this paper. Four cutting parameters are involved in the models (extended length of micro-milling tool, spindle speed, feed per tooth, and cutting depth in the axial direction). The models are established for material of brass. Experiments are carried out to verify the accuracy of the models. The results show that SVM prediction model has higher prediction accuracy, predict the variation law of micro-milling surface roughness better than RSM.

Keywords: micromilling; surface roughness; prediction modelling; response surface methodology; RSM; support vector machines; SVM; micromachining; surface quality; cutting parameters; tool length; spindle speed; feed per tooth; cutting depth; brass; modelling.

DOI: 10.1504/IJNM.2013.057595

International Journal of Nanomanufacturing, 2013 Vol.9 No.5/6, pp.457 - 467

Received: 28 Dec 2012
Accepted: 08 Mar 2013
Published online: 31 Mar 2014 *

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