Fuzzy Deduction Material Removal Rate Optimization for Computer Numerical Control Turning

Abstract

Problem statement: Material Removal Rate (MRR) is often a major consideration in the modern Computer Numerical Control (CNC) turning industry. Most existing optimization researches for CNC finish turning were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme is deemed to be necessary proposed for the industry. Approach: In this study, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) were considered to optimize the MRR in finish turning based on L9(34) orthogonal array. Additionally, nine fuzzy control rules using triangle membership function with respective to five linguistic grades for the MRR is constructed. Considering four input and twenty output intervals, the defuzzification using center of gravity was thus completed for the Taguchi experiment. Therefore, the optimum general deduction parameters can then be received. Results: The confirmation experiment for optimum general deduction parameters was furthermore performed on an ECOCA-3807 CNC lathe. It was shown that the material removal rates from the fuzzy Taguchi deduction optimization parameters are all significantly advanced comparing to those from the benchmark. Conclusion: This study not only proposed a general deduction optimization scheme using orthogonal array, but also contributed the satisfactory fuzzy linguistic approach for the MRR in CNC turning with profound insight.