Research On Thermal Hydroforming Process Of Aluminium Alloys

Hydroforming technology can manufacture complex hollow components with a variety of sections.Combined with structural and material light weight designs,the hydroforming technology of aluminum alloy is an effective method to manufacture light-weight structures.So the technology is widely used in aerospace and automotive industry .But the deformability of aluminum alloy at room-temperature is poor.By heating,we can significantly improve the formability of aluminum alloy.Therefore , it is necessary to investigate the hydroforming process of aluminum alloy at thermal state.In this paper firstly, the forming limit diagrams(FLDS) of aluminum alloy at various temperatures were determined analytically based on the Marciniak-Kuczynski concept of localized necking to be the criteria to predict the cracking during the tube thermal hydroforming simulationSecondly, we investigated the free bulging process of aluminum alloy at various temperatures, and analyzed the influence of temperature to the formability of tube hydroforming. The research results provide that:with the temperature increasing, the limit of tube bulging ratio gradually increases, which keeps consistent with the total elongation.It suggests the formability of tube hydroforming is improved, when the forming temperature increases.The following work started with the thermal hydroforming process of T-shape tube. First of all,we identified the quality evaluation indicators of the T-shape tube hydroforming, and then analyzed the influence of parameters (pressure, axial feed, etc.) on the forming process and the forming quality by numerical simulation. Finally, we proposed process controling strategy of T-shape of theraml tube hydroforming.The loading paths have a significant effect on the forming quality of hydroformed T-shaped tube. Based on the simulation, the orthogonal experimental design and response surface design were combined together to build the second order response surface models between the forming parameters and quality indicators. Furthermore, these models were optimized using multi-objective genetic algorithm NSGA-â…¡, and an even distributed Pareto solutions set was obtained. Then, defining a satisfactory degree function, the satisfactory solution was obtained. Finally, in order to verify the rationality of optimization result, the simulation verification was carried out, and it shows that simulation result is in accord with optimal one.