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Meeting the demands of Industry 4.0 and Digital Manufacturing requires a transformative framework for achieving crucial manufacturing goals such as zero-defect production or right-first-time development. In essence, this necessitates the development of self-sustainable manufacturing systems which can simultaneously adapt to high product variety and system responsiveness; and remain resilient by rapidly recovering from faulty stages at the minimum cost. A Closed-Loop In-Process (CLIP) quality control framework is envisaged with the aim to correct and prevent the occurrence of quality defects, by fusing sensing techniques, data analytics and predictive engineering simulations. Although the development and integration of distributed sensors and big data management solutions, the flawless introduction of CLIP solutions is hindered specifically with respect to acquiring and providing in-process data streams at the required level of: (1) veracity (trustworthiness of the data); (2) variety (types of data generated in-process); (3) volume (amount of data generated in-process); and, (4) velocity (speed at which new data is generated in-process) as dictated by rapid introduction and evolution of coupled system requirements. This paper illustrates the concept of the CLIP methodology in the context of assembly systems and highlights the need for a holistic approach for data gathering, monitoring and in-process control. The methodology hinges on the concept of “Multi-Wave Light Technology” and envisages the potential use of light-based technology, thereby providing an unprecedented opportunity to enable in-process control with multiple and competing requirements. The proposed research methodology is presented and validated using the development of new joining process for battery busbar assembly for electric vehicles with remote laser welding.

Multi-wave light technology enabling closed-loop in-process quality control for automotive battery assembly with remote laser welding