Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld
Weld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2 laser welding....
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/952690 |
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| author | Giuseppe Casalino Sabina Luisa Campanelli Fabrizio Memola Capece Minutolo |
| author_facet | Giuseppe Casalino Sabina Luisa Campanelli Fabrizio Memola Capece Minutolo |
| author_sort | Giuseppe Casalino |
| collection | DOAJ |
| description | Weld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2 laser welding. The following tolerable defects were analysed: weld undercut, excess weld metal, excessive penetration, incomplete filled groove, root concavity, and lack of penetration. A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built up using the experimental data. Weld imperfections were connected to the welding parameters by feed forward neural networks. Then the imperfections were clustered using the C-means fuzzy clustering algorithm. The clusters were named after the ISO standard classification of the levels of imperfection for electron and laser beam welding of aluminium alloys and steels. Finally, a single-value metric was proposed for the assessment of the overall bead geometry quality. It combined an index for each defect and functioned according to the criterion “the-smallest-the-best.” |
| format | Article |
| id | doaj-art-e7157e330d0946bdb07c579288c4ce6a |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-e7157e330d0946bdb07c579288c4ce6a2025-02-03T06:07:55ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422013-01-01201310.1155/2013/952690952690Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt WeldGiuseppe Casalino0Sabina Luisa Campanelli1Fabrizio Memola Capece Minutolo2Department of Mechanics, Mathematics and Management, Politecnico di Bari, Viale Japigia 182, 70126 Bari, ItalyDepartment of Mechanics, Mathematics and Management, Politecnico di Bari, Viale Japigia 182, 70126 Bari, ItalyDepartment of Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyWeld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2 laser welding. The following tolerable defects were analysed: weld undercut, excess weld metal, excessive penetration, incomplete filled groove, root concavity, and lack of penetration. A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built up using the experimental data. Weld imperfections were connected to the welding parameters by feed forward neural networks. Then the imperfections were clustered using the C-means fuzzy clustering algorithm. The clusters were named after the ISO standard classification of the levels of imperfection for electron and laser beam welding of aluminium alloys and steels. Finally, a single-value metric was proposed for the assessment of the overall bead geometry quality. It combined an index for each defect and functioned according to the criterion “the-smallest-the-best.”http://dx.doi.org/10.1155/2013/952690 |
| spellingShingle | Giuseppe Casalino Sabina Luisa Campanelli Fabrizio Memola Capece Minutolo Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld Advances in Materials Science and Engineering |
| title | Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld |
| title_full | Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld |
| title_fullStr | Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld |
| title_full_unstemmed | Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld |
| title_short | Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld |
| title_sort | neuro fuzzy model for the prediction and classification of the fused zone levels of imperfections in ti6al4v alloy butt weld |
| url | http://dx.doi.org/10.1155/2013/952690 |
| work_keys_str_mv | AT giuseppecasalino neurofuzzymodelforthepredictionandclassificationofthefusedzonelevelsofimperfectionsinti6al4valloybuttweld AT sabinaluisacampanelli neurofuzzymodelforthepredictionandclassificationofthefusedzonelevelsofimperfectionsinti6al4valloybuttweld AT fabriziomemolacapeceminutolo neurofuzzymodelforthepredictionandclassificationofthefusedzonelevelsofimperfectionsinti6al4valloybuttweld |