INSTANTANEOUS PARAMETER IDENTIFICATION FOR MILLING FORCE MODELS USING BAYESIAN OPTIMIZATION

The comparison between measured and simulated machining forces enables the evaluation of workpiece quality, process stability, tool wear condition. To compute that occur, mechanistic cutting force models are typically used. coefficients (CFCs) directly linked to mechanics chip formation and, thus, depend on tool-workpiece combination prevailing conditions. CFCs usually identified via average identification method, which requires execution tests under defined test Hence, determining for different conditions is time-consuming expensive. In this paper, performance an instantaneous CFC approach based Bayesian Optimization during arbitrary geometries studied. well suited global optimization problems with computationally expensive cost functions. calculated using a dexel-based cutter engagement simulation actual dynamometer. Thus, efficient could be achieved.