Parametric Study and Optimization for Welding Processes Using Machine Learning
Abstract
Optimization facilitates in attainment of maximum strength, efficiency, reliability, productivity and longevity. In this work, data from three material joining processes - Ultrasonic welding of polymers, arc welding as Metal Inert Gas and Tungsten Inert Gas are analysed for establishing quantitative relationship between the process parameters and for prediction of weld features using Multivariate Linear Regression algorithm. The various dependency coefficients and characteristics generated with the ML algorithms are in agreement with the inherent dependency as obtained from experimental data and simulation results. This investigation is a preliminary attempt with a limited set of data to manifest the suitability of machine learning techniques; nevertheless, the results are far from conclusive owing to small data set and hence may be extended to precisely model joining processes with higher number of process parameters, degree of freedom and responses.
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Papers accepted for publication become the copyrighted property of the Annals of "Dunarea de Jos" University of Galati, Fascicle XII, Welding Equipment and Technology. No part of the publication may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, except in limited quantities for the non-commercial purposes of scientific or educational advancement, without permission in writing from the Editorial Board.
