Prediction of welding responses using AI approach : adaptive neuro-fuzzy inference system and genetic programming

Chatterjee, Suman and Mahapatra, Siba Sankar and Lamberti, Luciano and Pruncu, Catalin I. (2022) Prediction of welding responses using AI approach : adaptive neuro-fuzzy inference system and genetic programming. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44 (2). 53. ISSN 1806-3691 (https://doi.org/10.1007/s40430-021-03294-w)

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Abstract

Laser welding of thin sheets has widespread application in various fields such as battery manufacturing, automobiles, aviation, electronics circuits and medical sciences. Hence, it is very essential to develop a predictive model using artificial intelligence in order to achieve high-quality weldments in an economical manner. In the present study, two advanced artificial intelligence techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP), were implemented to predict the welding responses such as heat-affected zone, surface roughness and welding strength during joining of thin sheets using Nd:YAG laser. The study attempts to develop an appropriate predictive model for the welding process. In the proposed methodology, 70% of the experimental data constitutes the training set whereas remaining 30% data is used as testing set. The results of this study indicated that the root-mean-square error (RMSE) of tested data set ranges between 7 and 16% for MGGP model, while RMSE for testing data set lies 18–35% for ANFIS model. The study indicates that the MGGP predicts the welding responses in a superior manner in laser welding process and can be applied for accurate prediction of performance measures.

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