International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-132 November-2025
  1. 4774
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  2. 2.8
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Autonomous augmented reality projection system for industrial weld seam inspection

Thanapat Kemthong1, Thanyapisit Kangsathien1, Dhamwarich Satapanakul1, Kantawatchr Chaiprabha2, Ratchatin Chancharoen2 and Gridsada Phanomchoeng2, 3

Program of Robotics and Artificial Intelligence,International School of Engineering, Faculty of Engineering, Chulalongkorn University,Bangkok 10330,Thailand1
Department of Mechanical Engineering,Faculty of Engineering, Chulalongkorn University,Bangkok 10330,Thailand2
Human-Robot Collaboration and Systems Integration Research Unit,Faculty of Engineering, Chulalongkorn University,Bangkok 10330,Thailand3
Corresponding Author : Thanapat Kemthong

Recieved : 01-June-2025; Revised : 24-November-2025; Accepted : 25-November-2025

Abstract

This paper presents an autonomous augmented reality (AR) projection system developed for weld seam inspection and guidance in industrial applications. The system integrates depth-based contour detection, automatic focus adjustment using Michelson contrast, and keystone correction via AR University of Córdoba Marker (ArUco) homography into a unified self-calibrating pipeline. Implemented on an embedded hardware platform with a multi-threaded software architecture and a web-based interface, the system operates in real time without the need for manual recalibration. Experimental evaluations demonstrated that the system-maintained projection accuracy within sub-millimeter visual tolerance and delivered stable focus performance across varying projection depths. Edge reproduction deviation (ERD) analysis and comparative studies confirmed that depth filtering, homography correction, and spline interpolation significantly enhanced overlay fidelity, while ambient illumination and surface finish introduced minor but manageable variations in clarity. Two case studies validated the system under realistic industrial conditions: weld-path projection on stainless-steel trays achieved a mean absolute error of 0.7 mm with 93% of samples within a 2 mm band, and structured-light projection on perpendicular joints enabled three-dimensional (3D) weld-seam reconstruction and reliable classification of subtle curvature differences between visually similar workpieces. These results confirm that the proposed system provides a robust, contactless, and self-calibrating solution for projection-assisted inspection, demonstrating strong potential for deployment in flexible manufacturing environments.

Keywords

Augmented reality (AR), Weld seam inspection, Depth-based contour detection, Keystone correction, Embedded vision systems, Projection-assisted manufacturing.

Cite this article

Kemthong T, Kangsathien T, Satapanakul D, Chaiprabha K, Chancharoen R, Phanomchoeng G. Autonomous augmented reality projection system for industrial weld seam inspection. International Journal of Advanced Technology and Engineering Exploration. 2025;12(132):1624-1645. DOI : 10.19101/IJATEE.2025.121220738

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