Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r 2 and root mean square error (RMSE), performed on training as well as checking datasets. The direct contact between cutting tool, workpiece material, and the chips during machining operation imposes extreme thermal and mechanical stresses on the cutting tool. As a result, changes to the geometry, volume loss, and sharpness of the cutting tool, can occur either gradually or abruptly. These changes, which are known as tool wear, normally take place at the rates dependent upon machining conditions, workpiece material, as well as the cutting tool material or geometry. As discussed in earlier research study [1], abrasive wear on the flank face of the cutting tool has been the dominant wear mechanism that influences the tool sharpness during machining of glass fibre-reinforced polymer (GFRP) composites. On the basis of this, reduction of tool sharpness puts constraint on the dimensional accuracies and surface qualities of the composites product. Often, in-service or mechanical performance of poorly machined GFRPs degrades and under the worst circumstances, causes them rejected prior to the end applications. Similar to the case of metallic materials and their metal matrix composite counterparts, it is essential to develop accurate tool wear predictive models as monitoring its condition during machining can extend its useful life. There exists a significant body …