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

Francesco Lupi (), Nelson Freitas (), Miguel Arvana (), Andre Dionisio Rocha (), Antonio Maffei (), José Barata () and Michele Lanzetta ()
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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

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 13, 4734 pages

Abstract: 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.

Keywords: Autonomous vision systems; Computer-aided design; Inspection; Reconfigurable manufacturing; Flexible manufacturing; Digital manufacturing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02456-6

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