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.
