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Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System

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dc.citedby1
dc.contributor.authorHarudin N.en_US
dc.contributor.authorNorizhar M.A.H.en_US
dc.contributor.authorMarlan Z.M.en_US
dc.contributor.authorSelamat F.E.B.en_US
dc.contributor.authorid56319654100en_US
dc.contributor.authorid58202195300en_US
dc.contributor.authorid57223885180en_US
dc.contributor.authorid57194168333en_US
dc.date.accessioned2024-10-14T03:21:46Z
dc.date.available2024-10-14T03:21:46Z
dc.date.issued2023
dc.description.abstractAutograding 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSCA57840.2023.10087789
dc.identifier.scopus2-s2.0-85153856624
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85153856624&doi=10.1109%2fICSCA57840.2023.10087789&partnerID=40&md5=9e810cd86c0ab7540c1d8a5f19cda432
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34689
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleInternational Conference on Smart Computing and Application, ICSCA 2023
dc.subjectAutograding
dc.subjectFeature extraction
dc.subjectImage Processing technique
dc.subjectMahalanobis Distance
dc.subjectMahalanobis-Taguchi System
dc.subjectWeld Bead Surface Quality
dc.subjectExtraction
dc.subjectFeature extraction
dc.subjectImage processing
dc.subjectSurface properties
dc.subjectWelds
dc.subjectAutograding
dc.subjectBead surface quality
dc.subjectClassifieds
dc.subjectFeatures extraction
dc.subjectImage processing technique
dc.subjectMahalanobis distances
dc.subjectMahalanobis-taguchi systems
dc.subjectTeaching and learning
dc.subjectWeld bead
dc.subjectWeld bead surface quality
dc.subjectGrading
dc.titleDevelopment of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi Systemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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