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 ci...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.展开更多
In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear mod...In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear model: Y<sub>n×m</sub>=X<sub>n×p</sub> +ε E（ε）=0, Cov（ ）=σ<sup>2</sup>U<sub>n×n</sub> V<sub>m×m</sub>, n≥m. In general, the general optimality of a parametric matrix follows analogously. The above X, S, U≥0 and V≥0 （but V≠0） are known matrix, and σ<sup>2</sup>】0 are unknown parameters, =（ε<sub>1</sub>’, ε<sub>2</sub>’, …, ε<sub>n</sub>’）’, where ε<sub>i</sub> is the ith row of ε, U V denotes the展开更多
文摘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.
文摘In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear model: Y<sub>n×m</sub>=X<sub>n×p</sub> +ε E（ε）=0, Cov（ ）=σ<sup>2</sup>U<sub>n×n</sub> V<sub>m×m</sub>, n≥m. In general, the general optimality of a parametric matrix follows analogously. The above X, S, U≥0 and V≥0 （but V≠0） are known matrix, and σ<sup>2</sup>】0 are unknown parameters, =（ε<sub>1</sub>’, ε<sub>2</sub>’, …, ε<sub>n</sub>’）’, where ε<sub>i</sub> is the ith row of ε, U V denotes the
