A 3D Non-Linear FE Model and Optimization of Cavity Die Sheet Hydroforming Process

Autor: Arun Achuthankutty, Ajith Ramesh, Ratna Kishore Velamati
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Metals, Vol 14, Iss 4, p 478 (2024)
Druh dokumentu: article
ISSN: 2075-4701
DOI: 10.3390/met14040478
Popis: Cryo-rolled aluminum alloys have a much higher strength-to-weight ratio than cold-rolled alloys, which makes them invaluable in the aerospace and automotive industries. However, this strength gain is frequently accompanied by a formability loss. When uniformly applied to the blank surface, hydroforming provides a solution by generating geometries with constant thickness, making it possible to produce complex structures with “near-net dimensions”, which are difficult to achieve with conventional approaches. This study delves into the cavity die sheet hydroforming (CDSHF) process for high-strength cryo-rolled AA5083 aluminum alloy, focusing on two primary research questions. Firstly, we explored the utilization of a nonlinear 3D finite-element (FE) model to understand its impact on the dimensional accuracy of hydroformed components within the CDSHF process. Specifically, we investigated how decreasing fluid pressure and increasing the holding time of peak fluid pressure can be quantitatively assessed. Secondly, we delved into the optimization of process parameters—fluid pressure (FP), blank holding force (BHF), coefficient of friction (CoF), and flange radius (FR)—to achieve dimensional accuracy in hydroformed square cups through the CDSHF process. Our findings reveal that our efforts, such as reducing peak fluid pressure to 22 MPa, implementing a 30 s holding period, and utilizing an unloading path, enhanced component quality. We demonstrated this with a 35 mm deep square cup exhibiting a 16.1 mm corner radius and reduced material thinning to 5.5%. Leveraging a sophisticated nonlinear 3D FE model coupled with response surface methodology (RSM) and multi-objective optimization techniques, we systematically identified the optimal process configurations, accounting for parameter interactions. Our results underscore the quantitative efficacy of these optimization strategies, as the optimized RSM model closely aligns with finite-element (FE) simulation results, predicting a thinning percentage of 5.27 and a corner radius of 18.64 mm. Overall, our study provides valuable insights into enhancing dimensional accuracy and process optimization in CDSHF, with far-reaching implications for advancing metal-forming technologies.
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