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

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 intelligencebased 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.

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

“…An artificial Neural network (ANN) is implemented to predict bead geometry for robotic Gas Metal Arc Welding (GMAW)-based rapid manufacturing. A genetic algorithm is implemented to quantify and optimize Wire Arc Additive Manufacturing (WAAM) process parameters [63]. Bayesian Classification Model is implemented for detecting the layers or sub-regions which have less quality of defects or fusion [64].…”

Influence of Process Parameters in Gas-Metal Arc Welding (GMAW) of Carbon Steels

“…The welding quality characteristics that define the GMAW welds include: weld bead geometry (bead width and penetration), heat-affected zone (HAZ), microstructure and mechanical properties (strength, ductility, and hardness) of the weldment), and discontinuities (porosity and spatter) [20,36,37]. These weld quality characteristics-which are discussed in detail in the proceeding subsections-are influenced by input (process) parameters such as welding speed, current, wire feed speed, contact tip to workpiece distance, and voltage [1,5,19,24].…”

“…Model predictive control (MPC): MPC is an advanced effective control algorithm used in industrial applications to predict or estimate the future output of a system from a set output by employing the dynamic model [64,72]. The MPC is robust in modeling uncertainty and has the capability of dealing with system constraints [73].…”

Evolução dos Processos de Automação em Células de Soldagem: Uma Revisão da Literatura

“…No trabalho de [102], a ferramenta de ANN mapeia as coordenadas (x, y) da linha de solda obtidas por meio do processamento de imagens e gera os valores de saída para as coordenadas (x, y, z) das juntas do robô. Já em [103] um software de tomada de decisão ANN foi combinado com um sistema de visão de modo a se obter um controle adaptativo para o processo de soldagem GMAW, baseado em AI (Artificial Intelligence). O sistema de visão utiliza sensores laser para escanear o segmento de solda e obter as informações do perfil da solda e da junta.…”