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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2158-3226 |