Digital twins and predictive quality

Joanna Rosak-Szyrocka and Radoslaw Wolniak

Chapter 6 in Quality 4.0 and Artificial Intelligence, 2026, pp 102-117 from Edward Elgar Publishing

Abstract: This chapter discusses how predictive quality management is achieved by Industry 4.0 digital twins. Physical, virtual, data integration, control-feedback, cloud-storage, and human-machine interface layers of industrial digital twin systems are introduced sequentially. Using real-time capture of data, AI-based simulation, and machine learning-based analysis, digital twins ensure proactive instead of reactive quality control. The chapter elaborates that digital twins are able to predict deviations, simulate production dynamics, conduct root cause analysis, and implement closed-loop control functions to enhance product and process quality. Industrial applications are emphasised, and measurable benefits for automobile, aerospace, electronics, and packaging industries are provided with return on investment, defect elimination, and organisational adoption being elaborated in detail. The conversation concludes with scalability, interoperability, uncertainty modelling, and compliance regulation being identified as the key drivers of successful implementation of digital twins within quality-focused production environments.

Keywords: Digital twin; Quality 4.0; Real-time monitoring; Internet of things; Smart manufacturing; Predictive quality; Quality management; Quality control (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035397068
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