Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line. (April 2019)

• Record Type: Journal Article
• Authors: Oh, YeongGwang; Ransikarbum, Kasin; Busogi, Moise; Kwon, Daeil; Kim, Namhun
• Is Part Of: Reliability engineering & system safety. Volume 184(2019)
• Page Start: 202
• Page End: 212
• Publication Date: 2019-04
• Subjects: Quality assessment system -- Infrared thermography (IRT) -- Support vector machine (SVM) -- Machine learning -- Automotive industry; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.03.020
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A real-time quality monitoring process using an adaptive SVM algorithm is proposed. The quality assessment performance can improve over time by A-SVM. The proposed system is verified and validated in the primer sealer manufacturing. The performance of A-SVM is better than other machine-learning methods. Abstract: 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.