Restructuring Industrial Cognition: An Ai-Enabled Perceptual Alignment Framework For Quality Inspection

Abstract

Artificial intelligence (AI) is increasingly embedded in manufacturing systems, reshaping not only operational efficiency but also the epistemological foundations of quality decision-making (Brynjolfsson & McAfee, 2017; Porter & Heppelmann, 2014). While prior studies have emphasized the technical advantages of AI-driven inspection systems, limited attention has been given to how such systems transform perceptual judgment and redefine the boundary between human and machine cognition in industrial contexts (Gambino et al., 2020; Longoni et al., 2019). Addressing this gap, this study proposes an AI-enabled perceptual alignment framework for spherical surface inspection, integrating skeleton-based feature matching with three-dimensional (3D) pose estimation to mitigate perceptual uncertainty in high-precision manufacturing environments. Drawing on impression integration theory (Anderson, 1981; Kunda & Thagard, 1996), this research conceptualizes quality inspection as a perceptual integration process in which cognitive and visual cues are synthesized to form defect judgments. Traditional inspection systems—heavily reliant on human operators—are prone to variability due to fatigue, subjective bias and the inherent complexity of curved surfaces. Using a real-world case of golf ball surface inspection, this study demonstrates how AI mediated perception can standardize judgment by reconstructing object orientation, correcting geometric distortions and enabling consistent feature recognition under variable conditions. Empirical findings indicate that the proposed framework significantly reduces inspection variability, lowers false detection rates and enhances yield stability. More importantly, the results reveal a structural shift from human-centric to AI-mediated decision-making, in which perceptual authority is redistributed from individual operators to algorithmic systems. This transformation contributes to the emergence of more data-driven forms of quality governance and more traceable decision processes within manufacturing systems. Theoretically, this study extends impression integration theory to human–AI interaction in industrial settings by demonstrating how AI systems not only replicate but also recalibrate perceptual judgment processes. Practically, it offers a scalable and modular solution for spherical object inspection with broader applicability across precision manufacturing domains. By positioning AI as a mediator of perception, this research provides new insights into the evolving role of intelligent systems in shaping industrial cognition and decision-making.