Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202
Deep drawing is a critical manufacturing process in the automobile, aerospace, and packaging industries, widely employed for producing cup-shaped components. This paper provides a comprehensive evaluation of the deep drawing process for cylindrical cups formed from Al1100 and SS202, focusing on the...
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| Format: | Article |
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AIP Publishing LLC
2024-11-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0235139 |
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| author | Amit Kaimkuriya S. Balaguru |
| author_facet | Amit Kaimkuriya S. Balaguru |
| author_sort | Amit Kaimkuriya |
| collection | DOAJ |
| description | Deep drawing is a critical manufacturing process in the automobile, aerospace, and packaging industries, widely employed for producing cup-shaped components. This paper provides a comprehensive evaluation of the deep drawing process for cylindrical cups formed from Al1100 and SS202, focusing on the influence of material type, blank diameter (50, 55, 60, and 70 mm), and lubrication conditions. A hybrid approach, combining experimental investigations, Finite Element Analysis (FEA), and the Whale Optimization Algorithm (WOA), was utilized to determine optimal process parameters, including load, compressive strength, and elongation. Experimental results indicated that FEA accurately predicted elongation (20 mm) across all blank diameters but overestimated maximum loads and compressive strengths, particularly for SS202. Lubrication significantly reduced loads and defects while enhancing elongation, although these improvements were not fully captured by FEA simulations. WOA outperformed FEA in predictive accuracy, achieving error margins as low as 1.87% for minimum load and 2.31% for compressive strength. The optimization process identified a 50 mm blank diameter as the most efficient for both the materials, enhancing material utilization and process efficiency. Integrating WOA with FEA yielded valuable insights into defect mitigation, particularly in reducing wrinkling and fractures, thereby improving product quality. This study demonstrates the effectiveness of combining advanced optimization algorithms with simulation tools, promoting sustainable manufacturing by enhancing efficiency and material utilization in deep drawing processes. |
| format | Article |
| id | doaj-art-c01b5bd37ddd4e4db31e3791ae0730c1 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-c01b5bd37ddd4e4db31e3791ae0730c12025-08-20T02:30:46ZengAIP Publishing LLCAIP Advances2158-32262024-11-011411115230115230-1410.1063/5.0235139Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202Amit Kaimkuriya0S. Balaguru1School of Mechanical Engineering, VIT Bhopal University, Sehore, Madhya Pradesh 466114, IndiaSchool of Mechanical Engineering, VIT Bhopal University, Sehore, Madhya Pradesh 466114, IndiaDeep drawing is a critical manufacturing process in the automobile, aerospace, and packaging industries, widely employed for producing cup-shaped components. This paper provides a comprehensive evaluation of the deep drawing process for cylindrical cups formed from Al1100 and SS202, focusing on the influence of material type, blank diameter (50, 55, 60, and 70 mm), and lubrication conditions. A hybrid approach, combining experimental investigations, Finite Element Analysis (FEA), and the Whale Optimization Algorithm (WOA), was utilized to determine optimal process parameters, including load, compressive strength, and elongation. Experimental results indicated that FEA accurately predicted elongation (20 mm) across all blank diameters but overestimated maximum loads and compressive strengths, particularly for SS202. Lubrication significantly reduced loads and defects while enhancing elongation, although these improvements were not fully captured by FEA simulations. WOA outperformed FEA in predictive accuracy, achieving error margins as low as 1.87% for minimum load and 2.31% for compressive strength. The optimization process identified a 50 mm blank diameter as the most efficient for both the materials, enhancing material utilization and process efficiency. Integrating WOA with FEA yielded valuable insights into defect mitigation, particularly in reducing wrinkling and fractures, thereby improving product quality. This study demonstrates the effectiveness of combining advanced optimization algorithms with simulation tools, promoting sustainable manufacturing by enhancing efficiency and material utilization in deep drawing processes.http://dx.doi.org/10.1063/5.0235139 |
| spellingShingle | Amit Kaimkuriya S. Balaguru Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 AIP Advances |
| title | Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 |
| title_full | Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 |
| title_fullStr | Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 |
| title_full_unstemmed | Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 |
| title_short | Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 |
| title_sort | experimental and computational optimization of sheet metal forming parameters for cylindrical cups of al1100 and ss202 |
| url | http://dx.doi.org/10.1063/5.0235139 |
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