Welding process selection for repairing nodular cast iron engine block by integrated fuzzy data envelopment analysis and TOPSIS approaches |
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| Affiliation: | 1. CENIMAT/i3N, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Portugal;2. Centre for Advanced Materials Joining, University of Waterloo, Canada;3. UNIDEMI, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Portugal;4. School of Mechatronics Engineering, University of Electronic Science and Technology of China, China;5. Laboratoire d''Etude des Microstructures et de Mécanique des Matériaux LEM3 (UMR CNRS 7239), 4 rue Augustin Fresnel, 57078 Metz, France;1. Institut f. Materialphysik, Universität Göttingen, Friedrich-Hund-Platz 1, D-37077 Göttingen, Germany;2. GZG Abt. Kristallographie, Universität Göttingen, Goldschmidtstr. 1, D-37077 Göttingen, Germany;3. Department of Mechanical Engineering, Helmut Schmidt University of the Federal Armed Forces, D-22039 Hamburg, Germany;1. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150080, PR China;2. Department of Mechanical Engineering, University of Colorado Denver, Denver, CO, 80217, USA |
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| Abstract: | The selection of welding process is one of the most significant decision making problems and it requires a wide range of information in accordance with the type of product. Hence, the automation of knowledge through a knowledge-based system will greatly enhance the decision-making process. A combined fuzzy data envelopment analysis (DEA) and TOPSIS is proposed to investigate the relative welding process selection factors and it can compare and evaluate different welding processes. The proposed approach compares each decision making unit (DMU) with the worst and the ideal virtual DMU and it ranks them via the relative closeness index. The proposed approach is used for ranking eleven welding processes which are commonly used for repairing nodular cast iron engine block in four cases and it is shown that the approach is sensitive to changes in dataset. |
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