Closed loop dimensional quality control for an assembly system entails controlling process parameters based on dimensional quality measurement data to ensure that products conform to quality requirements. Effective closed-loop quality control reduces machine downtime and increases productivity, as well as enables efficient predictive maintenance and continuous improvement of product quality. Accurate estimation of dimensional variations on the final part is a key requirement, in order to detect and correct process faults, for effective closed-loop quality control. Nowadays, this is often done by experienced process engineers, using a trial-and-error approach, which is time-consuming and can be unreliable. In this paper, a novel model to estimate process parameters error variations using high-density cloud-of-point measurement data captured by 3D optical scanners is proposed. The proposed model termed as PointDevNet uses 3D convolutional neural networks (CNN) that leverage the deviations of key nodes and their local neighbourhood to estimate the process parameter variations. These process parameters variation estimates are leveraged for root cause isolation as a necessary but currently missing step needed for the development of closed-loop quality control framework. The proposed model is compared with an existing state-of-the-art linear model under different scenarios such as a single and multiple root causes, and the presence of measurement noise. The state-of-the-art model is evaluated under different point selections and results are compared to the proposed model with consideration to an industrial case study involving a sheet metal part, i.e. window reinforcement panel.
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.