A1 Journal article (refereed), original research

Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application


Open Access hybrid publication

Publication Details
Authors: Penttilä Sakari, Kah Paul, Ratava Juho, Eskelinen Harri
Publisher: Springer Verlag (Germany)
Publication year: 2019
Language: English
Related Journal or Series Information: International Journal of Advanced Manufacturing Technology
Volume number: 105
Issue number: 7-8
Start page: 3369
End page: 3385
Number of pages: 17
ISSN: 0268-3768
eISSN: 1433-3015
JUFO-Level of this publication: 1
Open Access: Open Access hybrid publication

Abstract

Intelligent welding parameter control is fast becoming a key instrument for attaining quality consistency in automated welding. Recent scientific breakthroughs in intelligent systems have turned the focus of adaptive welding control to artificial intelligence based welding parameter control. The aim of this study is to combine artificial neural network (ANN) decision-making software and a machine vision system to develop an adaptive artificial intelligence (AI)-based gas metal arc welding (GMAW) parameter control system. The machine vision system uses a laser sensor to scan the upcoming seam and gather seam profile data. Based on further processing of the seam profile data, welding parameters are optimized by the decision-making system. In this work, the developed system is tested in a multivariable welding condition environment and its performance is evaluated. The quality of the welds was consistent and surpassed the required quality level. Additionally, the heat-affected zone (HAZ) was evaluated by microscopy, X-ray, and scanning electron microscope (SEM) imaging. It is concluded that the developed ANN system is suitable for implementation in automated applications, can improve quality consistency and cost efficiency, and reduce required workpiece preparation and handling.


Last updated on 2020-20-03 at 10:03