Human-Centered Explainability for Industrial Inspection: A Scenario‑Responsive Framework

The Evolution of Quality Engineering: From Scripted Automation to AI Autonomy A Comprehensive Literature Review

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 that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that drives exploratory testing.

WrenchBuddy: A Governance Framework for Human-Centered Industrial AI Fault Diagnostics

Industrial artificial intelligence (AI) fault-diagnostic systems can identify plausible causes under uncertainty, but their outputs alone do not determine how diagnostic support should be delivered during maintenance. This paper presents WrenchBuddy, a human-centered governance framework that manages three decisions during a fault episode: how much diagnostic structure to expose, what assistance posture to provide, and which first confirmatory question to prioritize. The framework is method-agnostic. In this illustration, its roles are instantiated using capped Bayesian-network views, a Comprehensive Operational Cost burden proxy, Fault-Situation Difficulty, a scenario-level physiological readiness input, Data Envelopment Analysis, a Help/Escalate rule, and Value of Information query ranking. A maintenance corpus from approximately 300 remotely monitored uninterruptible power supply machines provides 429 tagged incidents, while an independent physiological dataset provides the scenario-level readiness input. The reconstructed cause–alarm graph has 35 nodes and 34 edges. The Bounded view retains approximately 85% of Baseline coverage with approximately 57% of its interpretability burden. At the selected operating point, three alarms are escalated; bootstrap analysis shows that the two highest-difficulty decisions are stable, whereas the third is a borderline result based on seven incidents. Because the two datasets are not synchronized, the study evaluates governance-policy behavior rather than operational-outcome improvement. WrenchBuddy therefore provides an auditable framework for governing diagnostic exposure, assistance, and first-query selection, while synchronized human-subject validation remains future work.

Grounding Persona-Driven Usability Simulation in Established Standards

From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving

Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe what the ADS must demonstrably do once deployed within that domain. This gap between operating condition specification and behavioral validation represents a critical unresolved challenge in ADS safety assurance. This paper presents a structured, standards-grounded taxonomy of 21 behavioral competencies organized across three operational domains-Highway (HWY), Urban (URB), and Hub (HUB)-derived systematically from the PEGASUS six-layer model-based ODD. Each behavior is decomposed along longitudinal and lateral control axes and characterized against a four-property framework: Safety (gap maintenance, conflict avoidance, kinematic stability), Compliance (legal rules and behavioral norms), Comfort (rider dynamics and trust), and Efficiency (mission completion and product-level metrics). We further demonstrate that the crossing of ODD layer parameterizations with behavioral competency specifications yields concrete scenario families suitable for systematic behavioral testing and SOTIF coverage evidence. The taxonomy is grounded in AVSC00008202111, SAE J3237, and SAE J3016, and is validated as an operational specification layer through its deployment in a rule-enforced trajectory optimization system. The Hub domain is identified as a structurally distinct, underspecified domain warranting dedicated research attention.

Measuring AI Automation Exposure for Sustainable Workforce Transitions: A Two-Mechanism Decomposition

Artificial intelligence (AI) automates work through two mechanisms: codification-based automation, which applies rule-based systems to structured tasks, and learning-based automation, which applies machine learning and generative AI to pattern- and language-intensive tasks. An account of task-based exposure must represent both, yet each existing measure captures a single mechanism or conflates the two. Using publicly available O*NET data, we decompose exposure into a Routine Task Intensity (RTI) measure for codification and a Machine Learning Capability Index (MLCI) for learning, combined into a composite AI Exposure Index (AEI). The two components correlate weakly, so no single index can stand in for both. Validating against O*NET’s incumbent-reported degree of automation across two periods (2011–2019 and 2020–2025), we find realized automation remains dominated by codification, while the learning channel registers only recently and faintly. The two components also interact: each predicts realized automation most strongly where the other is absent, so entering them jointly with their interaction outperforms either component alone and the composite. Because forecasts built on such indices inform where retraining and income support are directed, accurate mechanism attribution bears on decent work and inequality. The decomposition offers a more complete, transparent, and reproducible account of task-based exposure than any single-mechanism index.

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

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.