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

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  • Oh, YeongGwang
  • Ransikarbum, Kasin
  • Busogi, Moise
  • Kwon, Daeil
  • Kim, Namhun

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.

Suggested Citation

  • Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:202-212
    DOI: 10.1016/j.ress.2018.03.020
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    References listed on IDEAS

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    1. Kasin Ransikarbum & Scott J. Mason, 2016. "Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 49-68, January.
    2. Moise Busogi & Kasin Ransikarbum & Yeong Gwang Oh & Namhun Kim, 2017. "Computational modelling of manufacturing choice complexity in a mixed-model assembly line," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5976-5990, October.
    3. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    4. ShiJie Ye & Zhi Xiao & Guangfu Zhu, 2015. "Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 3086-3103, May.
    5. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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    Cited by:

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