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

• Published in: Scientific reports Vol. 13; no. 1; p. 3013
• Main Authors: Abd Halim, Suhaila, Manurung, Yupiter H. P., Raziq, Muhamad Aiman, Low, Cheng Yee, Rohmad, Muhammad Saufy, Dizon, John R. C., Kachinskyi, Vladimir S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.02.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/988; 639/705/1042; Algorithms; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Stochasticity; User interface; Welding
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-29906-0

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.