Smart Electric Resistance Welding based on Artificial Intelligence (AI) based on Real-Time Adaptive Statistical Features Completed with Bibliometric Analysis

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Publication Details

Author list: Fufon K.; Notesiri U.; Lekchaum K.; Chomsuwan K.; Tanitteerapan T.; Puengsungewan S.

Publisher: Universitas Pendidikan Indonesia

Publication year: 2025

Journal acronym: AJSE

Volume number: 5

Issue number: 1

Start page: 173

End page: 198

Number of pages: 26

ISSN: 2776-6098

eISSN: 2776-5938

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004242489&doi=10.17509%2fajse.v5i1.82231&partnerID=40&md5=b77f422a59a02c5cb2922f4b6d9109e3

Languages: English-Great Britain (EN-GB)

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Abstract

Electric Resistance Welding (ERW) is a crucial technology in the automotive tubing manufacturing industry. However, setting welding parameters still relies on the operator’s experience, resulting in variability in weld quality and production efficiency. This research develops an Internet of Things integrated predictive model incorporating real-time thermal imaging, Adaptive Statistical Features, and Edge AI on an ESP32 microcontroller. The system captures weld temperature distributions, extracts 12 statistical features (such as contrast, entropy, skewness), and utilizes machine learning for predictive parameter optimization. Experimental results demonstrate that the Artificial Neural Network model achieves 84.4% defect detection accuracy, 6,666 inferences per second, and consumes only 36.87 kB of memory. By reducing human dependence and enabling real-time decision making, this system aligns with Industry 4.0 objectives, enhancing production efficiency and resource utilization in high-frequency ERW. The proposed system provides a cost-effective, scalable solution for industrial applications, fostering intelligent and sustainable manufacturing. © 2025 Universitas Pendidikan Indonesia.

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