Cutting forces parameters evaluation in milling using genetic algorithm

Simulation of the milling process is a widespread method to improve productivity in the machining process. Several phenomena can be studied and controlled by this mean. All the simulation methods need parameters that characterize the interaction between the tool and the workpiece in order to evaluate the cutting forces. Many models were developed to link efforts to macroscopic parameters (depth of cut, feed rate), but the coefficients of these models are often difficult to find out from intrinsic properties of the materials (Young's modulus, yield strength, hardness,...). Optimisation algorithms are thus necessary to retrieve those coefficients from cutting force measurement. For linear relationships the method can be fairly simple but the non linear models require more complex optimization algorithms. The aim of this article is to set out different methods to retrieve cutting parameters for several cutting forces models. Linear models are studied with simple least square fitting method. Genetic algorithms are tested on nonlinear cutting forces models. The optimization methods are validated using both simulated and measured cutting forces in order to demonstrate its use in practice. The agreement between simulation and measure is good so the methods can be used to give input parameters for the simulation of the whole machining process.