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Controllable Drawbead U-shaped piece of high-strength sheet springback analysis and experimental study

Author: YiZongHua
Tutor: ZhouJie
School: Chongqing University
Course: Materials Processing Engineering
Keywords: Controllable Drawbead Rebound High-strength sheet Neural Network
CLC: TG386
Type: Master's thesis
Year: 2010
Downloads: 50
Quote: 0
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Abstract


Since the state of the automotive industry to take more stringent measures for energy saving , lightweight vehicles and safety performance has become a research focus of the car manufacturers . Achieve lightweight vehicles and improve their security measures available from many angles . Wherein the vehicle body as much as possible the use of high strength steel sheets are widely used in the automotive industry is one of the effective methods . Use of high strength steel can reduce vehicle weight while improving vehicle safety performance. But relatively ordinary high tensile strength body panels , forming process is poor, difficult to control the rebound , at this stage in the complex structure of the body parts on the application there are still difficult. This paper proposes a new method of forming process - controlled Drawbead techniques to solve high- strength steel in the forming and springback forming process control and performance issues . This article focuses on controllable Drawbead the use of technology for high-strength plate U-shaped piece springback situation . First, the use of CAE software for high- strength steel U-shaped piece in step controllable Drawbead rebound under study to analyze and test results show that the simulation and experimental verification results meet the better ; then pull controllable deep tendon changes in real time the path under springback simulation , constraints and other parameters in the same circumstances the same selected section of the maximum springback value as an evaluation index by the sample data obtained by simulation with the reduction ratio springback in order to establish controllable motion path parameter drawbead X- output parameter Y ( S and thinning rate springback K), BP neural network prediction model , and verify its reliability ; on this basis , using genetic algorithm to find the optimal path to weigh the amount of springback and the reduction ratio between , so that parts only have a smaller rebound but also has good strength . While developing the appropriate study for the test mold test device and designed to achieve real-time motion control Drawbead simulation path control system for the subsequent test test platform to build , hoping for the future use of further research and engineering to provide basic data and experience .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal pressure processing > Cold stamping ( sheet metal processing) > Cold stamping process
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