Real-time quality monitoring and control system using an integrated cost effective support vector machine
- Title
- Real-time quality monitoring and control system using an integrated cost effective support vector machine
- Author
- 신동민
- Keywords
- Cost effectiveness; Cost of quality; Machine learning; Quality control; SVM
- Issue Date
- 2019-12
- Publisher
- KOREAN SOC MECHANICAL ENGINEERS
- Citation
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v. 33, No. 12, Page. 6009-6020
- Abstract
- The quality monitoring and control (QMC) has been an essential process in the manufacturing industries. With the advancements in data analytics, machine-learning based QMC has become popular in various manufacturing industries. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the cost-effective support vector machine (CESVM)-based automated QMC system (QMCS) is proposed. Unlike existing models, the proposed CESVM explicitly incorporates inspection-related expenses and error types in the SVM algorithm. The proposed automated QMCS is verified and validated using an automotive door-trim manufacturing process. Next, we perform a design of experiment to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be viewed as an alternative or complementary tool for the traditional quality inspection system.
- URI
- https://link.springer.com/article/10.1007%2Fs12206-019-1145-9https://repository.hanyang.ac.kr/handle/20.500.11754/122182
- ISSN
- 1738-494X; 1976-3824
- DOI
- 10.1007/s12206-019-1145-9
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML