Identification of Cutting Coefficients from Multiple Milling Tests

Abstract

The simulation of cutting forces in milling is a prerequisite for reliable process modeling. Among the different approaches used in the literature, the mechanistic models show good compromise between good precision and reasonable simulation time. The main challenge for these models is the identification of their parameters, such as the cutting coefficients, for a given tool/material couple.

This paper presents a method based on an inverse analysis to identify these parameters. At first, the preprocessing of the signal is made to automatically detect the time during which the cutter is actively engaged in the workpiece. The consistency of the input data is also checked to reject outliers. Then, to avoid the classical pitfalls of this approach such as the non-uniqueness of solution, the identification is made on a whole database of results using an iterative method. The use of optimization algorithm allows the identification of parameters having nonlinear effect on the results such as cutter runout.

A set of 57 milling tests in Ti6Al4V alloys have been used to demonstrate the effectiveness of this method. It allows a reduction of 5 to 20% of the root mean square error between the model and the measurements as compared to the use of coefficients identified on a single cutting test.