AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Lin, J.; Wang, J.; Feng, P.; Zhang, X.; Yu, D.; Zhang, J.
AI-aided Automated AR-Assisted Assembly Instruction Authoring and Generation method Journal Article
In: Journal of Manufacturing Systems, vol. 83, pp. 405–423, 2025, ISSN: 02786125 (ISSN), (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Ai-aided, Assembly, Assembly instructions, Assembly system, Assembly systems, Augmented Reality, Automatic programming, Computer aided instruction, Computer interaction, Generation method, Hand manipulation, Human computer interaction, human–computer interaction, Industrial assemblies, Intelligent method, Point cloud, Point-clouds, Real- time, Virtual Reality
@article{lin_ai-aided_2025,
title = {AI-aided Automated AR-Assisted Assembly Instruction Authoring and Generation method},
author = {J. Lin and J. Wang and P. Feng and X. Zhang and D. Yu and J. Zhang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017229936&doi=10.1016%2Fj.jmsy.2025.08.019&partnerID=40&md5=7957487b03f997dce9b6600e75481319},
doi = {10.1016/j.jmsy.2025.08.019},
issn = {02786125 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of Manufacturing Systems},
volume = {83},
pages = {405–423},
abstract = {While Augmented Reality (AR) offers the potential to provide real-time guidance, one of the barriers to its adoption in industrial assembly is the lack of fast, no-code, intelligent methods for generating AR-assisted assembly programs. This paper proposes an AI-aided AR-Assisted Assembly Instruction Authoring and Generation method (ARAIAG) to address these challenges. ARAIAG allows engineers, without coding expertise, to intuitively design AR-assisted assembly instructions based on assembly demonstrations captured through RGBD cameras. Based on ARAIAG, we propose a new algorithm considering hand manipulation and model characteristics to achieve spatial registration for models, virtual-physical fusion, and assembly direction recognition. Additionally, we employed a novel human–computer interaction method and Large Language Model (LLM)-assisted content generation to achieve the automatic creation of interactive and instructive AR-assisted assembly programs. Through this approach, we streamline program development and enable more efficient AR-assisted assembly in dynamic manufacturing environments. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Elsevier B.V.},
keywords = {Ai-aided, Assembly, Assembly instructions, Assembly system, Assembly systems, Augmented Reality, Automatic programming, Computer aided instruction, Computer interaction, Generation method, Hand manipulation, Human computer interaction, human–computer interaction, Industrial assemblies, Intelligent method, Point cloud, Point-clouds, Real- time, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
While Augmented Reality (AR) offers the potential to provide real-time guidance, one of the barriers to its adoption in industrial assembly is the lack of fast, no-code, intelligent methods for generating AR-assisted assembly programs. This paper proposes an AI-aided AR-Assisted Assembly Instruction Authoring and Generation method (ARAIAG) to address these challenges. ARAIAG allows engineers, without coding expertise, to intuitively design AR-assisted assembly instructions based on assembly demonstrations captured through RGBD cameras. Based on ARAIAG, we propose a new algorithm considering hand manipulation and model characteristics to achieve spatial registration for models, virtual-physical fusion, and assembly direction recognition. Additionally, we employed a novel human–computer interaction method and Large Language Model (LLM)-assisted content generation to achieve the automatic creation of interactive and instructive AR-assisted assembly programs. Through this approach, we streamline program development and enable more efficient AR-assisted assembly in dynamic manufacturing environments. © 2025 Elsevier B.V., All rights reserved.