Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line

Quality assessment in many production processes typically relies on manual inspections due to a lack of reference data and an effective method to classify defects in a systematic way. Recently, the real-time, automated approach for product quality assessment has been regarded an important aspect for smart manufacturing applications, such as in the automotive industry. In this research, we suggest a framework to pre-process the data for SVM-based decision making and implement the algorithm in the self-evolving quality assessment system based on the adaptive support vector machine (ASVM) model. An adaptive process is a feedback control that ensures the effectiveness of the support vector machine (SVM) algorithm over time and enables the improvement of SVM-based quality assessment in the real production process. Next, an industrial case study of a primer-sealer dispensing process in a sunroof assembly line of an automobile is illustrated with statistical analysis to verify and validate the applicability and effectiveness of the proposed ASVM-based quality assessment system. Defective patterns are then analyzed using an infrared thermal image of primer-sealer dispensing in a manufacturing process, which contains multi-modal data of dimensional information and temperature deviation from the dispending patterns in our study.