Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners
The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/12/1/24 |
| _version_ | 1797342504269381632 |
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| author | Thi-Thu-Huyen Vu Tai-Woo Chang Haejoong Kim |
| author_facet | Thi-Thu-Huyen Vu Tai-Woo Chang Haejoong Kim |
| author_sort | Thi-Thu-Huyen Vu |
| collection | DOAJ |
| description | The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management. |
| first_indexed | 2024-03-08T10:34:29Z |
| format | Article |
| id | doaj.art-7985df219d9440da9490124bb47b5660 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-08T10:34:29Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-7985df219d9440da9490124bb47b56602024-01-26T18:39:51ZengMDPI AGSystems2079-89542024-01-011212410.3390/systems12010024Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on MicrofastenersThi-Thu-Huyen Vu0Tai-Woo Chang1Haejoong Kim2Department of Industrial and Management Engineering, Kyonggi University, Suwon 16227, Republic of KoreaDepartment of Industrial and Management Engineering, Kyonggi University, Suwon 16227, Republic of KoreaDepartment of Industrial and Management Engineering, Kyonggi University, Suwon 16227, Republic of KoreaThe management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management.https://www.mdpi.com/2079-8954/12/1/24manufacturing quality controlmanufacturing defect detectiondeep learning-based systemdefect detection systemdefect detection on microfasteners |
| spellingShingle | Thi-Thu-Huyen Vu Tai-Woo Chang Haejoong Kim Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners Systems manufacturing quality control manufacturing defect detection deep learning-based system defect detection system defect detection on microfasteners |
| title | Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners |
| title_full | Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners |
| title_fullStr | Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners |
| title_full_unstemmed | Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners |
| title_short | Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners |
| title_sort | enhancing quality control in battery component manufacturing deep learning based approaches for defect detection on microfasteners |
| topic | manufacturing quality control manufacturing defect detection deep learning-based system defect detection system defect detection on microfasteners |
| url | https://www.mdpi.com/2079-8954/12/1/24 |
| work_keys_str_mv | AT thithuhuyenvu enhancingqualitycontrolinbatterycomponentmanufacturingdeeplearningbasedapproachesfordefectdetectiononmicrofasteners AT taiwoochang enhancingqualitycontrolinbatterycomponentmanufacturingdeeplearningbasedapproachesfordefectdetectiononmicrofasteners AT haejoongkim enhancingqualitycontrolinbatterycomponentmanufacturingdeeplearningbasedapproachesfordefectdetectiononmicrofasteners |