Collaborative Research: An Approach for Model Validation in Simulating Sheet Metal Forming Processes

基于CaoModel的仿真(MBS)为设计人员提供了在物理部件部署之前探索设计备选方案的灵活且廉价的方法。然而，考虑到预测和物理测试中所有不确定性的特定模型的置信度问题阻碍了广泛的应用，特别是在处理实际工程问题时。这项合作研究代表了两所大学和通用汽车公司共同努力，开发一种严格而实用的模型验证方法(通过不确定性传播的模型验证-MVUP)。该方法将利用系统变化的知识以及计算上有效的不确定性传播技术来提供对建模方法的有效性的随机评估。通过对板料成形过程模拟模型的验证，验证了所提出的方法。这项研究有望导致一种模型验证程序，该程序可以用最少的统计假设和可能的一个物理实验来提供对模型有效性的通用随机评估。研究成果将对板料成形过程中回弹的数值模拟产生直接的影响。我们期望它可以推广到其他工程问题，并通过考虑所有物理和数值不确定性，对我们如何看待数值模拟产生重大影响。研究成果还将有助于开发基于模型的仿真、制造过程的建模和优化以及工程设计中的不确定性分析等新课程。伊利诺伊大学芝加哥分校和西北大学的这项合作研究是在国家自然科学基金00-26，基于模型的模拟的探索性研究资助下进行的。

0084582CaoModel-Based Simulation (MBS) has provided designers with flexible and cheaper means to explore design alternatives before physical part deployment. However, question of the confidence level on a particular model considering the full range of uncertainties in prediction and in physical tests has hampered wide applications, especially when dealing with real-life engineering problems. This collaborative research represents the joint efforts from two universities and General Motors for the development of a rigorous and practical approach for model validation (Model Validation via Uncertainty Propagation -- MVUP). The approach will utilize the knowledge of system variations along with computationally efficient uncertainty propagation techniques to provide a stochastic assessment of the validity of a modeling approach. The proposed methodology will be demonstrated through validating the simulation models for sheet metal forming processes. The research is expected to lead to a model validation procedure that can provide a general-purpose stochastic assessment of model validity with the least amount of statistical assumptions and possibly one physical experiment. The research results developed will have an immediate impact on simulating springback in sheet metal forming process. It is our expectation that it can be generalized to other engineering problems and will make a significant impact on how we view numerical simulations by considering all the physical and numerical uncertainties. The research results will also contribute to the development of new courses on model-based simulation, modeling and optimization of manufacturing processes, and uncertainty analysis in engineering design. This collaborative research between University of Illinois at Chicago and Northwestern University is funded under NSF 00-26, Exploratory Research on Model-Based Simulation.***