Research on the prediction model of micro-milling surface roughness
by Xinxin Wang; Xiaohong Lu; Zhenyuan Jia; Xv Jia; Guangjun Li; Wenyi Wu
International Journal of Nanomanufacturing (IJNM), Vol. 9, No. 5/6, 2013

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.

Online publication date: Mon, 31-Mar-2014

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