Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0924-0136(99)00066-7
Title: Predictive mapping system for tool wear in metal cutting
Authors: Li, X.P. 
Ng, H.H.
Lim, S.C. 
Issue Date: 19-May-1999
Source: Li, X.P.,Ng, H.H.,Lim, S.C. (1999-05-19). Predictive mapping system for tool wear in metal cutting. Journal of Materials Processing Technology 89-90 : 279-286. ScholarBank@NUS Repository. https://doi.org/10.1016/S0924-0136(99)00066-7
Abstract: In this paper, a new approach to generate wear maps representing the rates of tool wear in a two-dimensional space defined by the machining conditions in metal cutting is presented. This is to be done through theoretical modelling and simulation of the cutting processes. The predicted rates of tool wear are then used to generate the wear maps. This approach aims to establish a predictive mapping system for tool wear under a range of machining conditions. It is also intended to integrate a simulation system for metal cutting into this mapping system for tool wear prediction. As a first step in the development of this predictive mapping system, a theoretical tool wear model is modified for the prediction of the rate of tool flank wear in terms of tool and workpiece material properties, tool geometry and cutting conditions for uncoated tungsten carbide tools in the cutting of steels. The predicted wear rates are then presented in the form of a wear map. Comparisons between the wear maps generated by this approach and those obtained experimentally show good agreement in the trends of wear-rate variation against machining conditions. It is noted that further work is necessary to refine the theoretical models to improve the agreement between predictions and the actual measurements of tool wear. However, the good match in terms of wear-rate trends obtained presently suggests that this is a viable approach to achieve the objective of developing an integrated predictive system for tool wear in metal cutting.
Source Title: Journal of Materials Processing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/58632
ISSN: 09240136
DOI: 10.1016/S0924-0136(99)00066-7
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