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