Quality assurance with AI and machine learning

Joanna Rosak-Szyrocka and Radoslaw Wolniak

Chapter 3 in Quality 4.0 and Artificial Intelligence, 2026, pp 41-64 from Edward Elgar Publishing

Abstract: In Chapter 3, the contributions of AI and ML to QA are discussed, as well as their potential to turn reactive end-of-line inspection into proactive, predictive and autonomous processes. Various learning paradigms including, but not limited to, supervised, unsupervised, reinforcement, semi-supervised, and self-supervised are summarised with their roles, desiderata and industrial applications. In particular, we focus on deep learning (DL) which can provide the capability for reliable interpretation of images, signals and sensor data in automatic defect detection and anomaly prediction. Machine vision technologies including 2D and 3D inspection, hyperspectral imaging and thermal imaging are examined as important enablers of such CLQC and self-healing factories. Apart from operational efficiency, the chapter demonstrates how AI-driven QA can be integrated with environmental, social and governance (ESG) frameworks and describes that predictive defect analytics, anomaly detection and explainable AI (XAI) lead to waste reduction, energy optimisation, adherence to regulations and improved transparency. By fusing this technical innovation with sustainability pressures, AI-powered QA offers a sustainable paradigm shift that doesn’t just offer a competitive edge, but becomes the keystone of advanced digital manufacturing in the age of Industry 4.0 and beyond.

Keywords: AI; ML; ESG; DL; Machine vision; QA; CLQC; Greenwashing; Closed-loop quality control; Deep learning (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035397068
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