Main Article Content

Vahid Jafarpour

Abstract

The present study has been conducted to model and optimize the spot welding. Thus, parameters including the current, electrode force, welding time and sheet thickness are considered as effective parameters in the welding process. First, using fuzzy-neural method, a fuzzy model with four inputs and one output is presented to estimate the weld strength. The purpose is to perform the process with the least amount of energy consumption, and at the same time having the highest strength. The laboratory data (obtained from 24 experiments using the central composite design method) were used to identify the welding model. The model obtained from genetic optimization has been used to increase the weld strength along with reducing the power consumption, after extracting the ANFIS (Adaptive Neuro Fuzzy Inference System) model. The results show that the neural fuzzy model accurately estimates the weld strength. The optimization also shows that with the obtained optimal parameters, in addition to reducing the amount of energy consumption, the weld strength is significantly increased.

Article Details

Keywords

Spot welding, ANFIS model, Optimization, Fuzzy-Neural

Refrences
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How to Cite

Jafarpour, V. (2022). Parameter Optimization of Spot-Welded Aluminum Plates Using the Adaptive Neuro-Fuzzy System with Genetic Algorithm. Mapta Journal of Mechanical and Industrial Engineering (MJMIE), 6(01), 10–17. https://doi.org/10.33544/mjmie.v6i01.188