Prediction and Optimization of Mold Steel Hardness in Wire Cut Process Based on Fuzzy Inference System

علوم کاربردی و محاسباتی در مکانیک

Article 4, Volume 35, Issue 1 - Serial Number 31, March 2023, Pages 53-70   

Document Type: Original Article

DOI: 10.22067/jacsm.2022.79508.1145

Authors

Farshad Rabiei^{* 1}; Masoud Seidi²; Zahra Seidy³

¹Faculty of Engineering, Ilam University, Ilam, Iran.

²Faculty of Engineering, Ilam University, Ilam, Iran

³Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran

Abstract

Pressing molds are widely used in the industry due to the high speed of parts production. Due to the very low clearance between the mandrel and the matrix and having complex shapes, the production of these molds is very expensive and costly.  The hardness of the mold and subsequently their fatigue life should be high. One of the processes that has the ability to create surfaces with high dimensional accuracy and complex shapes is the wirecut process. In this research, in order to improve the performance and increase the life of cutting dies, the hardness of Mo40 steel is optimized. For this purpose, based on the method of the response procedure, the design of the experiment has been designed for the three parameters of wire feed speed, wire tension and generator power. Next, the obtained data are clustered and then fuzzy rules with three inputs (wire speed, tension and power) and output (hardness) are extracted. The obtained rules were entered in the toolbox of fuzzy inference systems of MATLAB software. Based on the defined fuzzy inference system, it is possible to predict the hardness based on the parameters of wire speed, wire tension and generator power. In the next phase, based on this system and within the range of available variables, the optimal value of hardness and corresponding variables were extracted. In the final phase, these values ​​were experimentally tested and their agreement with the obtained value was observed.

Keywords

Wire Cut; Wire Feed Speed; Wire Tension; Generator Power; Fuzzy Inference System; Prediction and Modeling; Optimization

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