Cutting force prediction is very important because phenomena occurring during cutting and their resulting outcomes can be predicted and controlled by knowing the cutting forces. The accuracy of predicted cutting forces depends on that of calibration data. Therefore, the accuracy of calibration data needs to be known to estimate the prediction accuracy for cutting forces. In the present study, it is experimentally shown that calibration data used for mechanistic models have a specific probability distribution. A number of calibration data are generated through Monte-Carlo simulation using this probability distribution, resulting in an error index of calibration data (MPE) and the relationship between error of the predicted cutting forces and the error index. The results from this error analysis are utilized to estimate the prediction accuracy of the cutting forces and to optimize the calibration procedure that ensures the error in the cutting forces is within a given tolerance. The error analysis made in the present study is very practical because the calibration data accumulated in industry for various cutting processes can be directly utilized for the analysis without any additional experiments.
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