Jul 2026· International Journal of Business & Management Studies· Vol 07, pp. 52-63· 0 citations
TL;DR
An AI-enabled perceptual alignment framework for spherical surface inspection is proposed, integrating skeleton-based feature matching with three-dimensional pose estimation to mitigate perceptual uncertainty in high-precision manufacturing environments and demonstrates how AI mediated perception can standardize judgment.
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.
This survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification, and organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption.
Minjung Kim, Hwan-Sik Yoon· AI for Engineering· 0 citations
The use of AI-driven vision systems is rapidly becoming a core component of quality assurance in smart‑manufacturing environments. However, the usefulness of these systems depends not only on their detection performance but also on how their outputs are conveyed to operators on the production floor. This study introduces a scenario‑responsive Kansei–XAI interaction framework designed to align explainable visual feedback with human‑centered design attributes such as transparency, confidence building, and operational control. Drawing on insights from two practical industrial contexts, periodic tray inspections and continuous conveyor‑line monitoring, the framework defines a Scenario Rhythm–Risk Profile Matrix together with a risk‑modulated explanation strategy that adjusts the level of explanatory detail according to uncertainty levels and operational hazards. Two graphical interface prototypes demonstrate how the framework can be implemented, and a systematic evaluation methodology is outlined to support future deployment and validation efforts.
Manual industrial assembly remains essential in high-variety and customized production, but increasing product and process complexity places substantial cognitive demands on operators. Existing assistance systems, particularly augmented reality-based solutions, improve instruction visualization and task guidance, yet they often remain weakly connected to the real assembly state and limited in reasoning, adaptation, and decision support. This paper proposes a human-centered cognitive support framework for manual industrial assembly that integrates perception, agentic reasoning, knowledge grounding, and augmented reality guidance. The study is informed by a systematic search, bibliometric overview, and literature analysis of 129 Scopus-indexed documents. The analysis shows that augmented reality dominates current cognitive support approaches, while AI-based methods are increasingly used for object recognition, contextual interpretation, adaptive information delivery, and error detection. However, perception, reasoning, guidance, and knowledge grounding are still commonly treated as isolated functions. Based on these findings, 11 review-derived design requirements are formulated and used to develop a conceptual perception-cognition-guidance framework that represents cognitive support as a closed human-centered loop grounded in procedures, constraints, rules, and memory. The framework is then translated into a layered implementation architecture comprising physical, perception, cognitive, guidance, knowledge, and application layers. Structured data contracts clarify how sensory and interaction data can be transformed into perception evidence, structured assembly states, cognitive support decisions, and device-specific guidance commands. A toy-train assembly demonstrator illustrates how procedural state modeling, multi-camera perception, YOLO11-based object detection, projector-based guidance, and contract-based data exchange can connect physical assembly events with structured reasoning and operator-facing feedback.
Mariannys Rodriguez, Efrain Rodriguez, Sanderson César Macêdo Barbalho· The International Journal of...· 0 citations
Artificial intelligence (AI) is transforming smart manufacturing by enabling intelligent automation, data-driven decisions, and stronger collaboration between humans and manufacturing systems. The widespread adoption of collaborative robots, the industrial internet of things, and cyber-physical systems is driving demand for manufacturing environments that are safer, more flexible, and more efficient. Despite AI’s broad application in manufacturing, few studies have combined adaptive safety and intelligent task allocation within a single human-centered framework. This review offers a comprehensive look at AI applications that support these two complementary functions. Literature from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library was systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and analysed thematically. The findings show that machine learning, deep learning, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making. That said, challenges remain - such as interoperability, explainability, limited access to high-quality manufacturing data, industrial validation, and integrating multiple AI technologies. This review gives researchers and practitioners a holistic perspective and highlights integrated, human-centered AI frameworks as key enablers of resilient, efficient, and sustainable Industry 5.0 manufacturing systems.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
Software testing is moving away from rigid, hand-written scripts toward AI systems that can adapt on their own. This review traces how quality engineering has changed, from rule-based automation to self-adjusting test frameworks, and looks at the technology behind Autonomous Quality Agents: Large Language Models (LLMs) that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that drives exploratory testing. It also examines two ongoing problems: the difficulty of understanding how AI models make decisions, and the extra work needed to keep older, script-based automation running. The review closes with a proposed framework for where autonomous software assurance is headed next. This proposed framework, termed Autonomous Quality Assurance (AQA), is organised around three layers, perception (visual and DOM-based sensing), cognition (LLM-driven reasoning and test generation), and governance (interpretability and verification), intended to give practitioners and researchers a shared structure for locating where a given tool or technique sits today and what would need to mature before autonomous testing can be trusted at industrial scale.
Vanshita Agarwal· International journal for ad...· 0 citations
Reviews of artificial intelligence (AI) for mobile robots usually cover one competence—perception, SLAM, path planning, control, or reinforcement learning—and rarely show how these combine into a working system. We take the opposite view and treat autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning. We survey how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0. Using a structured, PRISMA-informed protocol with explicit search strings, inclusion criteria, and cross-embodiment transfer rules, we screen the literature, analyze a corpus drawn mainly from the last five years, and position it against prior surveys with a coverage matrix that exposes their single-block focus. Four findings stand out. Perception and localization approach engineering maturity through multimodal fusion and foundation vision models. Planning and control stay effective but computationally demanding. Decision-making, now driven by large language and vision–language–action models, is powerful yet unverifiable and fails under safety constraints. Lifelong learning is almost absent from deployed systems. The decisive weaknesses sit at the interfaces: at the perception–planning, planning–control, and control–decision handoffs the sim-to-real gap, limited on-robot compute, and scarce industrial data compound. We compare AI families by technology readiness, catalog datasets and benchmarks, examine the safety-certification barrier, and consolidate cross-cutting gaps. We close with a staged roadmap toward Industry 6.0 and a next-generation architecture coupling a foundation perception backbone, a world model and digital twin, a continual-learning memory, and a reasoning core wrapped by a safety monitor. The aim is to move from cataloging algorithms to engineering integrated autonomy.
Montaser N. A. Ramadan, Mohammed A. H. Ali, N. Ghazali· Machines· 0 citations
