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Micro-information-level AR instruction: a new visual representation supporting manual classification of similar assembly parts

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Abstract

In AR operation guidance training, for assembly parts with similar geometric shapes, there are still two problems in the visual representation of AR instructions: (1) AR instructions cannot accurately represent the micro-geometric differences between similar parts. (2) The AR instruction design specification that reflects the micro-geometric differences of similar parts has not been formulated. Based on such a problem, our team has carried out the following research work: First, the geometric features of parts are defined at the micro-geometric level and micro-information level, thereby explaining the relationships and differences between similar part features. From the above two levels. Secondly, a mathematical model of the geometric features of the parts is established, and the control parameters in the model are given to characterize the feature differences between similar parts. To verify the accuracy of the control parameters, we designed three AR instructions based on the control parameters and verified them through five hypotheses. Our team then analyzed the data from a case study and focused our discussion on test results that did not meet expectations. We have a more in-depth discussion by comparing the differences and analyzing the results. Finally, three implications of AR instructions in representing feature differences between similar parts are given, and future research directions for such work are indicated. It is provided guidance for future research.

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Notes

  1. https://www.microsoft.com/en-us/hololens

  2. https://www.magicleap.com/

  3. https://www.google.com/glass/start/

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Acknowledgments

We would like to appreciate the anonymous reviewers for their constructive suggestions for enhancing this paper. Besides, thanks to Zhishuo Xiong of the London School of Economics and Political Science for checking the English manuscript of the earlier version and he helped the author correct the grammatical errors in the paper. We particularly thank the CPILab VR / AR team of northwestern polytechnical university for its contribution to this study. We would also like to thank volunteers of university of shanghai for science and technology for participating in this experiment.

Funding

This work is partly supported by Defense Industrial Technology Development Program(No. XXXX2018213A001) and SASTIND China under Grant (JCKY2018205B021).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Zhuo Wang

  2. Cyber-Reality Innovation Centre (China), Nanjing, 211200, China

    Zhuo Wang, Xiaoliang Bai, Shusheng Zhang, Weiping He, Xiangyu Zhang, Shu Han & Yuxiang Yan

  3. Civil aircraft R & D Center, AVIC Xinxiang Aviation Industry (Group) Co., Ltd., XinXiang, 453049, China

    Yang Wang

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  1. Zhuo Wang
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Contributions

Yang Wang provided some valuable design solutions for this UX experiment. Jie Zhang and Yueqing Zhang established the basic hardware environment for our research. Yuxiang Yan broke through the technical difficulty of this research, and Xiangyu Zhang did a lot of work for the collection of experimental data. In particular, we would like to thank Prof. Weiping He and Associate Prof. Xiaoliang Bai for their constructive comments on the improvement of the experiment.

Corresponding author

Correspondence to Yang Wang.

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Our team declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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Wang, Z., Wang, Y., Bai, X. et al. Micro-information-level AR instruction: a new visual representation supporting manual classification of similar assembly parts. Multimed Tools Appl 82, 11589–11618 (2023). https://doi.org/10.1007/s11042-022-13574-9

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  • DOI: https://doi.org/10.1007/s11042-022-13574-9

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