Improved Welding Quality Prediction for Metal Inert Gas Welding using Artificial Intelligence

Authors

  • Mohmmad Qamar  Department Mechanical Engineering, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India
  • Dharmendra Kumar Singh  Department Mechanical Engineering, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET207635

Keywords:

ANFIS, ANN, Welding Quality, MIG Welding

Abstract

Welding is widely used by manufacturing engineers and production personnel to quickly and effectively set up manufacturing processes for new products. The MIG welding parameters are the most important factors affecting the quality, productivity and cost of welding. This paper presents the influence of welding parameters like welding current, welding voltage, Gas flow rate, wire feed rate, etc. on weld strength, ultimate tensile strength, and hardness of weld joint, weld pool geometry of various metal material during welding. By using DOE method, the parameters can be optimize and having the best parameters combination for target quality. The analysis from DOE method can give the significance of the parameters as it give effect to change of the quality and strength of product.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-12-30

Issue

Volume 7, Issue 6, November-December-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mohmmad Qamar, Dharmendra Kumar Singh "Improved Welding Quality Prediction for Metal Inert Gas Welding using Artificial Intelligence " International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 6, pp.275-285, November-December-2020. Available at doi : https://doi.org/10.32628/IJSRSET207635