IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i7d10.1007_s10845-024-02456-6.html

Next-generation Vision Inspection Systems: a pipeline from 3D model to ReCo file

Author

Listed:
  • Francesco Lupi

    (University of Pisa)

  • Nelson Freitas

    (NOVA University)

  • Miguel Arvana

    (NOVA University)

  • Andre Dionisio Rocha

    (NOVA University)

  • Antonio Maffei

    (KTH Royal Institute of Technology)

  • José Barata

    (University of Pisa)

  • Michele Lanzetta

    (University of Pisa)

Abstract

This paper proposes and implements a novel pipeline for the self-reconfiguration of a flexible, reconfigurable, CAD-based, and autonomous Vision Inspection System (VIS), expanding upon the modular framework theoretically outlined in (Lupi, F., Maffei, A., & Lanzetta, M. (2024). CAD-based Autonomous Vision Inspection Systems. Procedia Computer Science, 232, 2127–2136. https://doi.org/10.1016/J.PROCS.2024.02.033 .). The pipeline automates the extraction and processing of inspection features manually incorporated by the designer into the Computer Aided Design (CAD) 3D model during the design stage, in accordance with Model Based Design (MBD) principles, which, in turn, facilitate virtuous approaches such as concurrent engineering and design for (Dfx), ultimately minimizing the time to market. The enriched CAD, containing inspection annotations (textual or dimensional) attached to geometrical entities, serving as the pipeline’s input, can be exported in a neutral file format, adhering to the Standard for Product Data Exchange (STEP) Application Protocol (AP)242, regardless of the modeling software used. The pipeline’s output is a Reconfiguration (ReCo) file, enabling the flexible hardware (e.g., robotic inspection cell) and software components of the VIS to be reconfigured via software (programmable). The main achievements of this work include: (i) demonstrating the feasibility of an end-to-end (i.e., CAD-to-ReCo file) pipeline that integrates the proposed software modules via Application Programming Interfaces (API)s, and (ii) formally defining the ReCo file. Experimental results from a demonstrative implementation enhance the clarity of the paper. The accuracy in defect detection achieved a 96% true positive rate and a 6% false positive rate, resulting in an overall accuracy of 94% and a precision of 88% across 72 quality inspection checks for six different inspection features of two product variants, each tested on six samples.

Suggested Citation

  • Francesco Lupi & Nelson Freitas & Miguel Arvana & Andre Dionisio Rocha & Antonio Maffei & José Barata & Michele Lanzetta, 2025. "Next-generation Vision Inspection Systems: a pipeline from 3D model to ReCo file," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4711-4734, October.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02456-6
    DOI: 10.1007/s10845-024-02456-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02456-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-024-02456-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    2. Anupma Yadav & S.C. Jayswal, 2018. "Modelling of flexible manufacturing system: a review," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2464-2487, April.
    3. Keyur D. Joshi & Vedang Chauhan & Brian Surgenor, 2020. "A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 103-125, January.
    4. Nikolas Antzoulatos & Elkin Castro & Lavindra de Silva & André Dionisio Rocha & Svetan Ratchev & José Barata, 2017. "A multi-agent framework for capability-based reconfiguration of industrial assembly systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2950-2960, May.
    5. João Dias-Ferreira & Luis Ribeiro & Hakan Akillioglu & Pedro Neves & Mauro Onori, 2018. "BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1659-1682, October.
    6. Ahmad Barari & Marcos Sales Guerra Tsuzuki & Yuval Cohen & Marco Macchi, 2021. "Editorial: intelligent manufacturing systems towards industry 4.0 era," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1793-1796, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    2. Jože M. Rožanec & Luka Bizjak & Elena Trajkova & Patrik Zajec & Jelle Keizer & Blaž Fortuna & Dunja Mladenić, 2024. "Active learning and novel model calibration measurements for automated visual inspection in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 1963-1984, June.
    3. Yuzhuo Qiu & Mikhail Cherniavskii & Boris Goldengorin & Panos M. Pardalos, 2026. "A Computational Study of the Tool Replacement Problem," INFORMS Journal on Computing, INFORMS, vol. 38(1), pages 86-101, January.
    4. Hamed Khosravi & Taofeeq Olajire & Ahmed Shoyeb Raihan & Imtiaz Ahmed, 2024. "A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4087-4112, December.
    5. Edgar Chacón & Luis Alberto Cruz Salazar & Juan Cardillo & Yenny Alexandra Paredes Astudillo, 2021. "A control architecture for continuous production processes based on industry 4.0: water supply systems application," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2061-2081, October.
    6. Santosh Kumar Srivastava & Surajit Bag, 2023. "Recent Developments on Flexible Manufacturing in the Digital Era: A Review and Future Research Directions," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 483-516, December.
    7. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    8. Junhui Ge & Licheng Liu & Junxi Sun & Hong Zhao & Langming Zhou & Tianle Cheng & Changyan Xiao, 2023. "Automatic recognition of hot spray marking dot-matrix characters for steel-slab industry," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 869-884, February.
    9. Simon Li & Bahareh Eshragh & Akposeiyifa Joseph Ebufegha, 2023. "Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    10. Delorme, Xavier & Cerqueus, Audrey & Gianessi, Paolo & Lamy, Damien, 2023. "RMS balancing and planning under uncertain demand and energy cost considerations," International Journal of Production Economics, Elsevier, vol. 261(C).
    11. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    12. Chris Turner & John Oyekan, 2023. "Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    13. Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.
    14. Xu, Xiumei & Lyu, Jun & Zhan, Bowen, 2026. "Integrated emergy and exergy research through co-citation analysis: Knowledge bases, evolutionary trajectories and frontier frameworks," Ecological Modelling, Elsevier, vol. 514(C).
    15. Elvira Chebotareva & Maksim Mustafin & Ramil Safin & Tatyana Tsoy & Edgar A. Martinez-García & Hongbing Li & Evgeni Magid, 2025. "Camera-based safety system for collaborative assembly," Journal of Intelligent Manufacturing, Springer, vol. 36(8), pages 5593-5611, December.
    16. Hongquan Jiang & Deyan Yang & Zelin Zhi & Qiangzheng Jing & Jianmin Gao & Chenyue Tao & Zhixiang Cheng, 2024. "A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 217-233, January.
    17. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    18. Shagufta Parveen & Paolo Coccorese & Muhammad Umer & Abdul Haseeb Tahir, 2025. "Synergizing Financial Inclusion and Green Finance: Advancing Sustainable Development Goals 2030 and 2050 in Alignment With COP 29 Commitments," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(S1), pages 48-76, November.
    19. Ermias Wubete Fenta & Assefa Asmare Tsegaye & Aerimias Enyew Abere & Girma Tsegaye Tefera, 2025. "Opportunities in Flexible Manufacturing Systems in the Near Future," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 26(2), pages 247-267, June.
    20. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02456-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.