Wear Value Prediction of CNC Turning Tools based on  ν-GSVR with A New Hybrid Evolutionary Algorithm

Min-Liang  Huang; Jr-syu  Yang; Jheng-yu  Wu; Shih-Hsing  Chang

doi:10.6180/jase.202006_23(2).0021

Wear Value Prediction of CNC Turning Tools based on ν-GSVR with A New Hybrid Evolutionary Algorithm

Mechanical Engineering Electro-Mechanical Engineering

Min-Liang Huang¹, Jr-syu Yang¹, Jheng-yu Wu This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Shih-Hsing Chang This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., 25137, New Taipei City, Taiwan (R.O.C.)

Received: October 30, 2019
Accepted: April 4, 2020
Publication Date: June 1, 2020

Download Citation: ||https://doi.org/10.6180/jase.202006_23(2).0021

ABSTRACT

The dimensional accuracy of the workpiece will exceed the tolerance, therefore, to predict how many workpieces have been cut, the turning tool must be replaced is the important issue in machining field. To deal well with the normally distributed random error existed in the wear value prediction of CNC turning tools, this paper introduces the ν-Support Vector Regression (ν-GSVR) model with the Gaussian loss function to the prediction field of short-term wear value. A new hybrid evolutionary algorithm (namely CCGA) is established to search the appropriate parameters of the ν-GSVR, coupling the Chaos Map, Cloud model and Genetic Algorithm. Consequently, a new forecasting approach for the short-term wear value prediction of CNC turning tools, combining ν-GSVR model and CCGA algorithm, is proposed. The forecasting process considers the wear value prediction of CNC turning tools during the first few time intervals, the turning tool wear value for the spindle revolution, cutting depth and feed rate. It is used to verify the forecasting performance of the proposed model. The experiment indicates that the model yield more accurate results than the compared models in forecasting the short-term wear value on the turning tools. In this way, we can figure out how many turning tools to prepare for similar workpieces, which can reduce the stock of turning tools, and reduce the labor costs on quality inspection of workpieces during this period.

Keywords: Wear Value Prediction; CNC Turning Tools; Support Vector Machine; Support Vector Regression; Gaussian loss function; Genetic Algorithm; Chaos Map; Cloud model.

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