Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system
The Mahalanobis-Taguchi System (MTS) refers to a newly-developed technique based on statistics that integrates a number of mathematical concepts to be applied for classification and diagnosis purposes within systems that are comprised of multiple dimensions. The MTS has been proven to be an exceptio...
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| Format: | Conference Paper |
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American Institute of Physics Inc.
2023
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| _version_ | 1833413239493361664 |
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| author | Muhamad W.Z.A.W. Jamaludin K.R. Muhtazaruddin M.N. Yahya Z.R. Ramlie F. Harudin N. |
| author2 | 55860800560 |
| author_facet | 55860800560 Muhamad W.Z.A.W. Jamaludin K.R. Muhtazaruddin M.N. Yahya Z.R. Ramlie F. Harudin N. |
| author_sort | Muhamad W.Z.A.W. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The Mahalanobis-Taguchi System (MTS) refers to a newly-developed technique based on statistics that integrates a number of mathematical concepts to be applied for classification and diagnosis purposes within systems that are comprised of multiple dimensions. The MTS has been proven to be an exceptional technique that can be employed in numerous fields. In MTS, it is essential to choose the variables in order to enhance the accuracy in classifying via orthogonal array (OA) and Signal-to-Noise (S/N) ratios. However, the penalty for over-fitting or regularization is not included in the feature selection process for the MTS classifier. Hence, this paper investigated the combination between MTS and statistical pattern recognition approach applied to automotive crankshaft remanufacturing as an automated decision-making tool for classification purposes. The outcomes revealed that MTS displayed better classification performance for both training and test datasets, besides eliminating redundant and irrelevant parameters better than the conventional approach did. � 2018 Author(s). |
| format | Conference Paper |
| id | my.uniten.dspace-23665 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | American Institute of Physics Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-236652023-05-29T14:50:52Z Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system Muhamad W.Z.A.W. Jamaludin K.R. Muhtazaruddin M.N. Yahya Z.R. Ramlie F. Harudin N. 55860800560 26434395500 55578437800 50862369800 55982859700 56319654100 The Mahalanobis-Taguchi System (MTS) refers to a newly-developed technique based on statistics that integrates a number of mathematical concepts to be applied for classification and diagnosis purposes within systems that are comprised of multiple dimensions. The MTS has been proven to be an exceptional technique that can be employed in numerous fields. In MTS, it is essential to choose the variables in order to enhance the accuracy in classifying via orthogonal array (OA) and Signal-to-Noise (S/N) ratios. However, the penalty for over-fitting or regularization is not included in the feature selection process for the MTS classifier. Hence, this paper investigated the combination between MTS and statistical pattern recognition approach applied to automotive crankshaft remanufacturing as an automated decision-making tool for classification purposes. The outcomes revealed that MTS displayed better classification performance for both training and test datasets, besides eliminating redundant and irrelevant parameters better than the conventional approach did. � 2018 Author(s). Final 2023-05-29T06:50:52Z 2023-05-29T06:50:52Z 2018 Conference Paper 10.1063/1.5054230 2-s2.0-85054590137 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054590137&doi=10.1063%2f1.5054230&partnerID=40&md5=70a32b6078e1e1f059d522081576bce5 https://irepository.uniten.edu.my/handle/123456789/23665 2013 20031 American Institute of Physics Inc. Scopus |
| spellingShingle | Muhamad W.Z.A.W. Jamaludin K.R. Muhtazaruddin M.N. Yahya Z.R. Ramlie F. Harudin N. Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title | Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title_full | Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title_fullStr | Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title_full_unstemmed | Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title_short | Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system |
| title_sort | optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the mahalanobis-taguchi system |
| url_provider | http://dspace.uniten.edu.my/ |