Sequential Estimation of Machinability Parameters for Adaptive Optimization of Machinability Data Base Systems

Abstract

Mathematical model type machinability data base systems require suitable model building procedures to estimate the model parameters. The estimation procedure should be capable of using subjective prior information about the models and must also be capable of adapting the model parameters to the particular machining environment for which the data are needed. In this paper, the sequential Maximum A Posteriori (MAP) estimation procedure is proposed as the mathematical tool for performing these functions. Mathematical details of this estimation procedure are presented. The advantages of this method over conventional regression analysis are discussed based on the analysis of an experimental tool life data set. Details regarding the selection of the various initial values needed for starting the sequential procedure are presented. The use of prior information about the models in order to improve the parameter estimates is investigated. The adaptive capability of the procedure is analyzed using simulated tool life data. The results of this analysis indicate that the proposed sequential estimation procedure is a valuable tool for estimating machinability parameters and for the adaptive optimization of machinability data base systems.