Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6
Distinct alloys have different chemical and thermal characteristics, making it difficult to weld together. For the purpose of maximizing tensile strength and weld hardness of the joint, the gas metal arc (GMA) welding process for the dissimilar aluminium alloys AA5083-O and AA6061-T6 was modelled an...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | International Journal of Lightweight Materials and Manufacture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2588840423000392 |
| _version_ | 1797349434022952960 |
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| author | Rajesh P. Verma K.N. Pandey Gaurav Mittal |
| author_facet | Rajesh P. Verma K.N. Pandey Gaurav Mittal |
| author_sort | Rajesh P. Verma |
| collection | DOAJ |
| description | Distinct alloys have different chemical and thermal characteristics, making it difficult to weld together. For the purpose of maximizing tensile strength and weld hardness of the joint, the gas metal arc (GMA) welding process for the dissimilar aluminium alloys AA5083-O and AA6061-T6 was modelled and optimized in the current study. A genetic-neural approach was attempted, in which optimal artificial neural network (ANN) was applied to model the process and genetic algorithm (GA) approach was extended to optimize the parameters. The proposed genetic-neural (GA-ANN) approach was also compared to the traditional response surface methodology (RSM). In predicting the reactions of a GMA welded joint made of two different alloys, AA5083-O and AA6061-T6, the suggested optimum ANN model was shown to be more accurate (error 6%). The genetic-neural optimization technique has less inaccuracy (approximately 5% error) than the RSM optimization approach, however the more computational time was required to select GA-ANN parameters. |
| first_indexed | 2024-03-08T12:30:11Z |
| format | Article |
| id | doaj.art-63fad51d563f4d76b6405cc5a676d8fc |
| institution | Directory Open Access Journal |
| issn | 2588-8404 |
| language | English |
| last_indexed | 2024-03-08T12:30:11Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Lightweight Materials and Manufacture |
| spelling | doaj.art-63fad51d563f4d76b6405cc5a676d8fc2024-01-22T04:16:08ZengKeAi Communications Co., Ltd.International Journal of Lightweight Materials and Manufacture2588-84042024-01-0171214220Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6Rajesh P. Verma0K.N. Pandey1Gaurav Mittal2Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India; Corresponding author.Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, IndiaDepartment of Mechanical Engineering, UPES Dehradun, IndiaDistinct alloys have different chemical and thermal characteristics, making it difficult to weld together. For the purpose of maximizing tensile strength and weld hardness of the joint, the gas metal arc (GMA) welding process for the dissimilar aluminium alloys AA5083-O and AA6061-T6 was modelled and optimized in the current study. A genetic-neural approach was attempted, in which optimal artificial neural network (ANN) was applied to model the process and genetic algorithm (GA) approach was extended to optimize the parameters. The proposed genetic-neural (GA-ANN) approach was also compared to the traditional response surface methodology (RSM). In predicting the reactions of a GMA welded joint made of two different alloys, AA5083-O and AA6061-T6, the suggested optimum ANN model was shown to be more accurate (error 6%). The genetic-neural optimization technique has less inaccuracy (approximately 5% error) than the RSM optimization approach, however the more computational time was required to select GA-ANN parameters.http://www.sciencedirect.com/science/article/pii/S2588840423000392Dissimilar aluminium alloysAA5083-OAA6061-T6GMA weldingANNGA |
| spellingShingle | Rajesh P. Verma K.N. Pandey Gaurav Mittal Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 International Journal of Lightweight Materials and Manufacture Dissimilar aluminium alloys AA5083-O AA6061-T6 GMA welding ANN GA |
| title | Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 |
| title_full | Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 |
| title_fullStr | Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 |
| title_full_unstemmed | Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 |
| title_short | Genetic-neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of AA5083-O/AA6061-T6 |
| title_sort | genetic neural optimization approach for gas metal arc welding of dissimilar aluminium alloys of aa5083 o aa6061 t6 |
| topic | Dissimilar aluminium alloys AA5083-O AA6061-T6 GMA welding ANN GA |
| url | http://www.sciencedirect.com/science/article/pii/S2588840423000392 |
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