Abstract
Quality Assurance (QA) has traditionally relied on rule-based inspections, statistical quality control, and manual auditing processes. While effective in stable production environments, conventional QA systems often struggle to handle increasing product complexity, high variability, and rapid production cycles associated with Industry 4.0. Artificial Intelligence (AI) has emerged as a powerful enabler that augments quality assurance systems by introducing data-driven decision-making, predictive capabilities, and adaptive learning mechanisms. AI-augmented Quality Assurance systems integrate machine learning, computer vision, natural language processing, and predictive analytics to enhance defect detection, process monitoring, root cause analysis, and continuous improvement. This paper presents a comprehensive review of AI-augmented QA systems, covering their architecture, core technologies, applications across industries, benefits, limitations, and future research directions. Emphasis is placed on how AI complements rather than replaces human quality engineers, enabling proactive and intelligent quality management. The paper also discusses implementation challenges related to data quality, explainability, workforce skills, and ethical concerns. AI-augmented QA is positioned as a critical component of smart manufacturing and digital transformation strategies.
Keywords: Artificial Intelligence, Quality Assurance, Machine Learning, Smart Manufacturing, Predictive Quality, Industry 4.0
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