Multi-criteria selection of image acquisition technologies for automatic optical inspection in assembly lines

Abstract Ensuring product conformity in industrial assembly lines is becoming increasingly complex due to high production rates, as well as the growing complexity and customisation of products. Zero-Defect Manufacturing (ZDM) aims to eliminate production defects by integrating advanced quality assurance technologies, thereby enhancing processes efficiency and sustainability. Automatic Optical Inspection (AOI), powered by Computer Vision (CV), has emerged as a key enabler of ZDM, automating defect detection and enhancing quality control. However, the industrial adoption of AOI systems is still in its early stages, and there is a need for a structured methodology to assess their technical feasibility and select the most suitable image-acquisition technology. This paper proposes a novel multi-criteria decision-making (MCDM) methodology for selecting image-acquisition technologies in AOI systems. The approach integrates Analytic Hierarchy Process (AHP) and the Weighted Sum Model (WSM) to evaluate alternatives based on key factors such as product dimensions, inspection area dispersion, visibility constraints, and system flexibility requirements. The methodology is validated through two real-world case studies in the automotive sector, demonstrating its effectiveness in supporting technology selection and guiding AOI implementation in real production environments.