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The modern manufacturing paradigm is characterised by an increased level of competition, growing demand for customisable or one-of-a-kind products, and stricter sustainability requirements. To maintain their competitiveness, manufacturing companies must adapt their processes frequently and efficiently while providing high-quality products. Given the importance of establishing flexible and reconfigurable systems, different advanced manufacturing technologies, such as industrial robotics, have seen a drastic increase in usage. However, no system is perfect or free from uncertainties (defects). To achieve Zero-Defect Manufacturing (ZDM, i.e., no defective products leave the manufacturing system, four strategies ‘detect’, ‘predict’, ‘prevent’, and ‘repair’ are needed. However, traditional quality methods, such as quality inspection (detect), suffer from significant limitations in highly customised small batch production.

The objective of this thesis is to facilitate the design of robotic in-line quality inspection systems for ZDM. To achieve the objective, this thesis follows a mixed methods research approach, and its foundation is based on two extensive systematic literature reviews and four case studies in close collaboration with manufacturing companies to investigate how robotic in-line quality inspection is perceived and used. This thesis contributes to the research area of quality management.

Through its findings, this research revealed the unexplicit and partial usage of the ZDM principles in research studies. Thus, this thesis characterises robotic in-line quality inspection, identifies its challenges, and pinpoints its enablers. Robotic in-line quality inspection systems are characterised as ‘connected’, ‘fast’, ‘accurate’, ‘reliable’, ‘holistic’, ‘flexible’, and ‘intelligent’. Several challenges to performing robotic in-line quality inspection have been encountered during this research. As part of the control system, as well as the manufacturing system, performance is highly dependent on its integration with ‘people’, ‘processes’, and ‘technologies’. For example, people need certain competences, time, communication, and participation in the development of ZDM; processes such as ZDM standards are lacking; and available technologies need to be balanced between equipment footprint, interoperability, measurement speed and accuracy, and reliability. Finally, to align all physical, digital, or cognitive components and characteristics, two frameworks and a design flowchart are proposed to help practitioners establish ZDM.