教師資料查詢 | 類別: 期刊論文 | 教師: 楊智旭YANG JR-SYU (瀏覽個人網頁)

標題:Wear Value Prediction of CNC Turning Tools based on v-GSVR with A new Hybrid Evolutionary Algorithm
學年108
學期2
出版(發表)日期2020/06/01
作品名稱Wear Value Prediction of CNC Turning Tools based on v-GSVR with A new Hybrid Evolutionary Algorithm
作品名稱(其他語言)
著者Min-Liang Huang; Jr-syu Yang; Jheng-yu Wu; Shih-Hsing Chang
單位
出版者
著錄名稱、卷期、頁數淡江理工學刊 23(2),頁369-378
摘要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.
關鍵字Wear Value Prediction;CNC Turning Tools;Support Vector Machine;Support Vector Regression
語言英文(美國)
ISSN
期刊性質國內
收錄於EI;
產學合作
通訊作者M. L. Huang
審稿制度
國別中華民國
公開徵稿
出版型式,電子版,紙本
相關連結
SDGs
  • 負責任的消費與生產,尊嚴就業與經濟發展,產業創新與基礎設施
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