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

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@Article{chatterjee:2022:JBSMSE,

• author = "Suman Chatterjee and Siba Sankar Mahapatra and Luciano Lamberti and Catalin I. Pruncu",
• title = "Prediction of welding responses using {AI} approach: adaptive neuro-fuzzy inference system and genetic programming",
• journal = "Journal of the Brazilian Society of Mechanical Sciences and Engineering",
• year = "2022",
• volume = "44",
• number = "2",
• pages = "Article number: 53",
• keywords = "genetic algorithms, genetic programming, MGGP, Laser welding, Nd, YAG laser, ANFIS, Titanium alloy, Stainless steel",
• URL = "http://link.springer.com/article/10.1007/s40430-021-03294-w",
• DOI = "doi:10.1007/s40430-021-03294-w",
• size = "15 pages",
• 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 percent of the experimental data constitutes the training set whereas remaining 30 percent 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 percent for MGGP model, while RMSE for testing data set lies 18 to 35 percent 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.",
• notes = "Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela, India",

}

Genetic Programming entries for Suman Chatterjee Siba Sankar Mahapatra Luciano Lamberti Catalin I Pruncu

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