Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

Abstract

Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.

The Use of Machine Learning to Predict Steel Properties – A Review on the Latest Works

“…Abd Halim et al [21] created an application tool called Q-check that utilizes an open-source and customized algorithm based on artificial neural networks to predict parameters such as welding time, current, and electrode force, in relation to tensile shear load-bearing capacity [TSLBC] and weld quality classifications [WQC]. For this purpose, a supervised learning algorithm was implemented, encompassing the standard backpropagation neural network gradient descent [GD], stochastic gradient descent [SGD], and Levenberg-Marquardt [LM] methods, based on an 80% training and 20% test set.…”

Integration of the AI and ML Approaches for Prediction Analysis in Welding: Review

“…ML brough an innovative and efficient solution to overcome these problems. Based on the recent literatures, it is already showing promising advancements for prediction and quality control of welding [10]. Below some recent applications of the ML: a) Prediction of Welding Parameters: ML models have been used to predict optimal welding parameters based on various inputs such as material type, thickness, joint configuration, and welding process.…”