Robust Contact Computation in Non-Rigid Variation Simulation

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computing and Information Science in Engineering Pub Date : 2024-05-21 DOI:10.1115/1.4065570
Roham Sadeghi Tabar, Samuel Lorin, L. Lindkvist, Kristina Wärmefjord, R. Söderberg
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Abstract

Geometric variation is an inevitable element of any fabrication process. To secure the geometric quality of the assembled products, variation simulation is performed to control compliance with the set geometric requirements. In non-rigid variation simulation, contact modeling is used to avoid the virtual penetration of the components in the adjacent areas, enhancing the simulation accuracy. For frictionless contact models, numerical errors and convergence issues due to the deformation behavior of the interacting surfaces are still limiting the computational efficiency of solving this optimization problem. The optimization problem associated with a contact model is often large-scale, and in practice, fast and robust methods for achieving convergence are required. Previous implementations of contact modeling for non-rigid variation simulation have been prominently based on the Iterative or Penalty Methods. In this paper, a quadratic programming approach has been introduced, based on the Lagrangian multiplier method, for robust contact modeling in non-rigid variation simulation, and the performance of the proposed approach has been compared to the previously applied Iterative and Interior Point Method. The methods have been compared on three industrial reference cases, and the convergence and time-efficiency of each method are compared. The results show that robust optimization of the quadratic program associated with the contact model is highly dependent on the reduced stiffness matrix condition. Furthermore, it has been shown that robust and efficient contact modeling in non-rigid variation simulation is achievable through the proposed quadratic programming method.
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非刚性变化模拟中的稳健接触计算
几何变化是任何制造过程中都不可避免的因素。为确保组装产品的几何质量,需要进行变化模拟,以控制是否符合设定的几何要求。在非刚性变化模拟中,接触建模用于避免部件在相邻区域的虚拟穿透,从而提高模拟精度。对于无摩擦接触模型,由于相互作用表面的变形行为导致的数值误差和收敛问题仍然限制着解决这一优化问题的计算效率。与接触模型相关的优化问题通常规模较大,在实践中需要快速、稳健的收敛方法。以往用于非刚性变化模拟的接触建模主要基于迭代法或惩罚法。本文介绍了一种基于拉格朗日乘法的二次编程方法,用于非刚性变化仿真中的稳健接触建模,并将所提方法的性能与之前应用的迭代法和内点法进行了比较。在三个工业参考案例中对这些方法进行了比较,并比较了每种方法的收敛性和时间效率。结果表明,与接触模型相关的二次方程程序的稳健优化高度依赖于减小刚度矩阵条件。此外,研究还表明,通过所提出的二次编程方法,可以在非刚性变化仿真中实现稳健高效的接触建模。
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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