Modelling the Cutting Process and Cutting Performance in Abrasive Water jet Machining Using Genetic-Fuzzy Approach

Abstract

Abstract Unconventional machining processes are used only when no other traditional machining process can meet the necessary requirements efficiently and economically. Abrasive Waterjet Machining (AWJM) is one of the most recently developed mechanical type unconventional hybrid manufacturing technologies. It is superior to many other cutting techniques in processing various materials, particularly in processing difficult to cut materials. This technology is being increase used in various industries. Therefore, optimum choice of the process parameters is essential for the economic, efficient, and effective utilization of these processes. Process parameters of AWJM are generally selected either based on the experience, and expertise of the operator or from the propriety machining handbooks. In most of the cases, selected parameters are conservative and far from the optimum. This hinders optimum utilization of the process capabilities. Selecting optimum values of process parameters without optimization requires elaborate experimentation which is costly, time consuming, and tedious. Process parameters optimization of AWJM essential for exploiting their potentials and capabilities to the fullest extent economically. This paper presents a Fuzzy Logic (FL) - based modeling of AWJM process and optimization of its rule base, data base and consequent part utilizing a Genetic Algorithm (GA). A binary coded GA has been used for the said purpose. While modeling with FL, the output parameters, namely Material Removal Rate (MRR) and Surface Finish (Ra) have been predicted for different combinations of process parameters, such as waterjet pressure at the nozzle exit diameter of abrasive-water jet nozzle traverse or feed rate of the nozzle mass flow rate of water and mass flow rate of abrasives between nozzle and the work piece.