Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System
Autograding systems are becoming more prevalent to address the challenges inherent in teaching and learning assessment. Over the past few decades, technological advancements have increased image processing techniques, including pattern recognition research. The Mahalanobis-Taguchi System (MTS) is a...
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2024
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my.uniten.dspace-346892024-10-14T11:21:46Z Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System Harudin N. Norizhar M.A.H. Marlan Z.M. Selamat F.E.B. 56319654100 58202195300 57223885180 57194168333 Autograding Feature extraction Image Processing technique Mahalanobis Distance Mahalanobis-Taguchi System Weld Bead Surface Quality Extraction Feature extraction Image processing Surface properties Welds Autograding Bead surface quality Classifieds Features extraction Image processing technique Mahalanobis distances Mahalanobis-taguchi systems Teaching and learning Weld bead Weld bead surface quality Grading Autograding systems are becoming more prevalent to address the challenges inherent in teaching and learning assessment. Over the past few decades, technological advancements have increased image processing techniques, including pattern recognition research. The Mahalanobis-Taguchi System (MTS) is a technique for assessing system performance by analyzing multivariate data to make quantitative choices via the development of a multivariate measurement scale. This study intends to develop a grading tool that combines a feature extraction technique with MTS theory, which instructors will utilize at the UNITEN Manufacturing Processes Laboratory to assess the quality of weld bead surface work prepared by UNITEN students. The Mahalanobis Distance (MD) will distinguish between normal and abnormal extracted image patterns from workpieces and transform them into a measurable scale. The samples defined better grading with lower MD. A jig was developed to collect consistent and accurate image data for the image-capturing process. The results showed that out of 10 test samples, 2 samples were classified as normal with a grading range between 75% to 82%. Another sample was classified as gray regions, with grading ranges between 65% and 74%. The remaining 6 samples were classified as abnormal, with a grading range between 40% to 64%. An autograding tool for evaluating welding surface quality utilizing MD scales was established. � 2023 IEEE. Final 2024-10-14T03:21:46Z 2024-10-14T03:21:46Z 2023 Conference Paper 10.1109/ICSCA57840.2023.10087789 2-s2.0-85153856624 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153856624&doi=10.1109%2fICSCA57840.2023.10087789&partnerID=40&md5=9e810cd86c0ab7540c1d8a5f19cda432 https://irepository.uniten.edu.my/handle/123456789/34689 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Autograding Feature extraction Image Processing technique Mahalanobis Distance Mahalanobis-Taguchi System Weld Bead Surface Quality Extraction Feature extraction Image processing Surface properties Welds Autograding Bead surface quality Classifieds Features extraction Image processing technique Mahalanobis distances Mahalanobis-taguchi systems Teaching and learning Weld bead Weld bead surface quality Grading |
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Autograding Feature extraction Image Processing technique Mahalanobis Distance Mahalanobis-Taguchi System Weld Bead Surface Quality Extraction Feature extraction Image processing Surface properties Welds Autograding Bead surface quality Classifieds Features extraction Image processing technique Mahalanobis distances Mahalanobis-taguchi systems Teaching and learning Weld bead Weld bead surface quality Grading Harudin N. Norizhar M.A.H. Marlan Z.M. Selamat F.E.B. Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| description |
Autograding systems are becoming more prevalent to address the challenges inherent in teaching and learning assessment. Over the past few decades, technological advancements have increased image processing techniques, including pattern recognition research. The Mahalanobis-Taguchi System (MTS) is a technique for assessing system performance by analyzing multivariate data to make quantitative choices via the development of a multivariate measurement scale. This study intends to develop a grading tool that combines a feature extraction technique with MTS theory, which instructors will utilize at the UNITEN Manufacturing Processes Laboratory to assess the quality of weld bead surface work prepared by UNITEN students. The Mahalanobis Distance (MD) will distinguish between normal and abnormal extracted image patterns from workpieces and transform them into a measurable scale. The samples defined better grading with lower MD. A jig was developed to collect consistent and accurate image data for the image-capturing process. The results showed that out of 10 test samples, 2 samples were classified as normal with a grading range between 75% to 82%. Another sample was classified as gray regions, with grading ranges between 65% and 74%. The remaining 6 samples were classified as abnormal, with a grading range between 40% to 64%. An autograding tool for evaluating welding surface quality utilizing MD scales was established. � 2023 IEEE. |
| author2 |
56319654100 |
| author_facet |
56319654100 Harudin N. Norizhar M.A.H. Marlan Z.M. Selamat F.E.B. |
| format |
Conference Paper |
| author |
Harudin N. Norizhar M.A.H. Marlan Z.M. Selamat F.E.B. |
| author_sort |
Harudin N. |
| title |
Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| title_short |
Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| title_full |
Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| title_fullStr |
Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| title_full_unstemmed |
Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System |
| title_sort |
development of an autograding system for weld bead surface quality using feature extraction and mahalanobis-taguchi system |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2024 |
| _version_ |
1814061067377049600 |
| score |
13.222552 |