A research team affiliated with UNIST has unveiled an innovative, high-precision AI-powered quality inspection system that reduces inspection time from 12 minutes to just under 3 seconds. This cutting-edge technology facilitates real-time, micron-scale defect detection during continuous manufacturing processes, paving the way for fully automated, inline quality control in high-speed production environments.
According to the research team, led by Professor Im Doo Jung in the Department of Mechanical Engineering at UNIST, the reported system employs a machine learning-powered smart jig that enables micro-level dimensional quality monitoring during production without disrupting the ongoing manufacturing process. By integrating advanced anomaly detection algorithms with 3D-printed sensor caps, the technology can identify minute dimensional errors within seconds, greatly enhancing inspection efficiency and accuracy.
Led by Seobin Park and Taekyeong Kim, who served as first authors, this research is published in the Journal of Manufacturing Systems.
Dimensional mismatches—such as micro-level gaps between assembled parts—can undermine product strength and quality, often stemming from manufacturing tolerances or deformation during handling. These defects are difficult to correct after assembly, making early detection vital to prevent costly rework, delays, or recalls.
During assembly, the smart jig precisely holds components in position. A specially designed clamp, fitted with a soft 3D-printed sensor cap on its contact surface, subtly deforms in response to the component’s surface features. This deformation pattern is analyzed by AI algorithms to accurately detect anomalies, enabling instant assessment of whether a part has a dimensional defect.
Where traditional inspection methods took approximately 12 minutes per component, this new system completes inspection in just 2.79 seconds. It can detect defects as small as 500 micrometers, ensuring high-resolution quality control that operates seamlessly within automated production lines without halting operations.
Detected defects are visualized through easy-to-interpret heatmaps, allowing operators to quickly identify defect locations and severity levels, thereby facilitating immediate corrective measures and reducing rework time.
The AI model is trained exclusively on data from defect-free, normal products, eliminating the need for extensive defect datasets or manual labeling—common hurdles in manufacturing environments. Its low maintenance requirements and scalable design enable adaptation across diverse industries, including mobility, electronics, aerospace, and consumer appliances.
Professor Jung states, “This technology can be applied across high-precision assembly sectors, significantly reducing inspection personnel and time while boosting overall quality reliability. The system has the potential to save hundreds of millions of won annually by minimizing defects and streamlining inspection processes.”
More information:
Seobin Park et al, Quick dimensional inspection for continuous welding and assembly using machine learning-powered smart jig, Journal of Manufacturing Systems (2025). DOI: 10.1016/j.jmsy.2025.07.001
Ulsan National Institute of Science and Technology
Citation:
AI-driven smart jig can detect micro-level defects in just 2.79 seconds (2025, September 17)
retrieved 17 September 2025
from https://techxplore.com/news/2025-09-ai-driven-smart-jig-micro.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
