Abstract
Tool wear is frequently considered in the modern CNC (computer numerical control) 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 proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) are considered to optimize the tool wear for finish turning based on orthogonal array. Additionally, nine fuzzy control rules using triangle membership function with respective to five linguistic grades for tool wear are constructed. Considering four input and twenty output intervals, the defuzzification using center of gravity is then completed and introduced as the S/N (signal-to-noise) ratio. Thus, the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the tool wear ratio from the fuzzy deduction optimization parameters is significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach to tool wear in CNC turning with profound insight.
Highlights
Machining operations have been the core of the manufacturing industry since the industrial revolution [1]
This paper definitely proposes a fuzzy deduction general optimization approach and satisfactory fuzzy linguistic technique for improving tool wear in CNC turning with profound insight
To verify the applicability of the optimum result achieved by our proposed general optimization technique, the machining operations under both fuzzy Taguchi deduction optimization parameters and benchmark parameters; A (Medium), B (Medium), C (Medium), D
Summary
Machining operations have been the core of the manufacturing industry since the industrial revolution [1]. The existing optimization researches for CNC (computer numerical controlled) turning were either simulated within particular manufacturing circumstances [2,3,4,5], or achieved through numerous frequent equipment operations [6,7]. These are regarded as computing simulations, and the applicability to real world industry is still uncertain. It is mentioned that the tool nose run-off will affect the performance of the machining process [8]
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