학술논문

Research on springback characteristic and prediction accuracy control of advanced high strength steel
Document Type
Conference
Source
International Forum on Strategic Technology 2010 Strategic Technology (IFOST), 2010 International Forum on. :352-355 Oct, 2010
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Analytical models
Numerical models
Optimization
Floors
Predictive models
advanced high strength steel
springback
prediction accuracy
numerical simulation error
Language
Abstract
With the progress of automobiles lightening, there has been an increasing demand for high strength steel in automobile manufacturing. Under such situation that the springback is lager and the prediction accuracy is lower, deep researches on the springback characteristic, prediction accuracy and simulation error control of advanced high strength steel have been carried out by means of numerical simulation and experiment. The influences of material parameters and process variables on the bending springback of typical U-shaped parts are analyzed. Springback simulation error function (SSEF) has been established to characterize the springback prediction accuracy. Effects of material hardening model, element size, the number of integration points in the direction of plate thickness and virtual punch velocity on springback prediction accuracy in finite element numerical simulation are discussed. The cause of numerical simulation error is analyzed. Combined with Reverse Engineering techniques, springback simulation accuracy has been improved by the multidimensional error control solution of simulation parameters adjustment, keywords modification and simulation process optimization.