Modeling of tool wear for ball end milling cutter based on shape mapping

Tool wear is thus of great importance to understand and quantitatively predict tool life. In this paper, a tool wear model for ball end milling cutter is established with considering the joint effect of machining conditions for predicting tool wear. The modelling process of tool wear is given and discussed according to the specific conditions. In order to determine coefficients of the established tool wear model a new tool wear estimation method based on shape mapping is used to measure tool wear which is suitable to prepare tool wear data for the established model. So tool wear for each experiment can be obtained from the tool wear estimation method and be used to fit the proposed tool wear model by using multiple linear regression method. Experimental work and validation are performed on five-axis high speed machining centre for cemented carbide cutting tool milling stainless steel. Experimental results indicate that tool wear can be predicted within 10 % on an average using the established tool wear model and the established tool wear model is suitable to predict tool wear at certain range of cutting conditions for milling operation.

[1]  Qiang Liu,et al.  On-line monitoring of flank wear in turning with multilayered feed-forward neural network , 1999 .

[2]  Álisson Rocha Machado,et al.  Influence of the number of inserts for tool life evaluation in face milling of steels , 2004 .

[3]  Dongfeng Shi,et al.  Tool wear predictive model based on least squares support vector machines , 2007 .

[4]  Chen Ming,et al.  On-line tool breakage monitoring in turning , 2003 .

[5]  Malika Boudraa,et al.  Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring , 2008, Prod. Eng..

[6]  Janez Kopac,et al.  New approach in tool wear measuring technique using CCD vision system , 2005 .

[7]  Gary W. Fischer,et al.  A novel approach to quantifying tool wear and tool life measurements for optimal tool management , 2003 .

[8]  Xiaoli Li,et al.  A brief review: acoustic emission method for tool wear monitoring during turning , 2002 .

[9]  Joseph C. Chen,et al.  An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .

[10]  Samy E. Oraby,et al.  Tool life determination based on the measurement of wear and tool force ratio variation , 2004 .

[11]  Liu Qiang.  Partial Least Squares Regressive Analysis and Modeling for Tool Wear , 2000 .

[12]  Wen-Tung Chien,et al.  The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel , 2003 .

[13]  Laishui Zhou,et al.  A new tool wear estimation method based on shape mapping in the milling process , 2011 .

[14]  J. Paulo Davim,et al.  Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts , 2007 .

[15]  Y. Koren.  Flank Wear Model of Cutting Tools Using Control Theory , 1978 .

[16]  Enrique Alegre,et al.  On-line tool wear monitoring using geometric descriptors from digital images , 2007 .

[17]  Debasis Sengupta,et al.  Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques , 2007 .