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The Analysis On Springback Of Hss U-shaped Part And Its Experimental Research Based On Controllable Drawbead

Posted on:2011-03-26Degree:MasterType:Thesis
Country:ChinaCandidate:Z H YiFull Text:PDF
GTID:2121360308458191Subject:Materials Processing Engineering
Abstract/Summary:PDF Full Text Request
Since the state of the automotive industry to take increasingly stringent energy-saving measures to reduce emissions, lightweight and security of the automobile has become the focus of the study of various car manufacturers. Measures of achieving automotive lightweight and improving their safety can be from a lot of perspective. Among them, the use of high-strength steel as much as possible on the car body has been proven effective methods, and widely used in the automotive industry.The adoption of high-strength steel can reduce vehicle weight while improving the safety performance of vehicles. However, compare to the ordinary body plate, it has the relatively high tensile strength, poor formability, and its resilience is difficult to control. At this stage, there are still difficult to apply to the complex body parts. Regarding this, we try to propose a new method of forming process-controllable drawbead technology to address the issues above.This paper focuses on discussing the quality of resilience of high-strength sheet (HSS) parts in the case of controllable drawbead technology. First, using CAE software to simulate different trajectories and fixed drawbead height of HSS U-shaped parts under the controllable drawbead technology,chosing the maximum resilience in the same cross-section of simulation model as evaluating indicator in the case of same constraints. Select the same under similar circumstances on the largest cross-section springback as evaluation indicators, then GA-BP Artificial Neural Network of drawbead match trajectories—springback value was established to search globally optimal solution,and its reliability was verified. At the same time, developing the corresponding experimental devices and control system that can realize simulation trajectories of controllable drawbead, and then obtaining relevant data through experiments. Finally, analyzing and comparing the experimental results to the simulation, looking forward to providing basic data and experience for the follow-up experiments and future projects.
Keywords/Search Tags:controllable drawbead, springback, high-strength steel, neural networks
PDF Full Text Request
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