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Traitement de maquettes numériques pour la préparation de modèles de simulation en conception de produits à l'aide de techniques d'intelligence artificielle

机译：使用人工智能技术处理数字模型以在产品设计中准备仿真模型

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Controlling the well-known triptych costs, quality and time during the different phases of the Product Development Process (PDP) is an everlasting challenge for the industry. Among the numerous issues that are to be addressed, the development of new methods and tools to adapt to the various needs the models used all along the PDP is certainly one of the most challenging and promising improvement area. This is particularly true for the adaptation of CAD (Computer-Aided Design) models to CAE (Computer-Aided Engineering) applications. Today, even if methods and tools exist, such a preparation phase still requires a deep knowledge and a huge amount of time when considering Digital Mock-Up (DMU) composed of several hundreds of thousands of parts. Thus, being able to estimate a priori the impact of DMU preparation process on the simulation results would help identifying the best process right from the beginning, and this will ensure a better control of processes and preparation costs. This thesis addresses such a difficult problem and uses Artificial Intelligence (AI) techniques to learn and accurately predict behaviors from carefully selected examples. The main idea is to identify rules from these examples used as inputs of learning algorithms. Once those rules obtained, they can be used as estimators to be applied a priori on new cases for which the impact of a preparation process can be estimated without having to perform it. To reach this objective, a method to build a representative database of examples has been developed, the right input and output variables have been identified, then the learning model and its associated control parameters have been tuned. The performance of a preparation process is assessed by criteria like preparation costs, analysis costs and the errors induced by the simplifications on the analysis results. The first challenge of the proposed approach is to extract and select most relevant input variables from the original and 3D prepared models, which are completed with data characterizing the preparation processes. Another challenge is to configure learning models able to assess with good accuracy the quality of a process, despite a limited number of examples of preparation processes and data available (the only data known to a new case are the data that characterize the original CAD models and simulation case). In the end, the estimator of the process’ performance will help analysts in the selection of CAD model preparation operations. This does not exempt the analysts to make the numerical simulation. However, this will get faster a simplified model of best quality. The rules linking the output variables to the input ones are obtained using AI techniques such as well-known neural networks and decision trees. The proposed approach is illustrated and validated on industrial examples in the context of CFD simulations.
机译：在产品开发过程（PDP）的不同阶段中，控制众所周知的三联画成本，质量和时间是业界永恒的挑战。在要解决的众多问题中，开发新方法和工具以适应PDP整个模型所使用的各种需求无疑是最具挑战性和最有希望的改进领域之一。对于CAD（计算机辅助设计）模型适应CAE（计算机辅助工程）应用的情况尤其如此。如今，即使存在方法和工具，在考虑由数十万个零件组成的数字模型（DMU）时，这种准备阶段仍需要深入的知识和大量的时间。因此，能够事先估计DMU制备过程对模拟结果的影响，将有助于从一开始就确定最佳过程，这将确保更好地控制过程和制备成本。本文解决了这样一个难题，并使用人工智能（AI）技术从精心选择的示例中学习并准确预测了行为。主要思想是从这些示例中识别出用作学习算法输入的规则。一旦获得了这些规则，就可以将它们用作估计器，以对新案件进行先验评估，而无需执行新案件就可以估计准备过程的影响。为了达到这个目的，开发了一种建立示例数据库的方法，确定了正确的输入和输出变量，然后对学习模型及其相关的控制参数进行了调整。制备过程的性能通过诸如制备成本，分析成本以及简化分析结果所引起的错误之类的标准进行评估。提出的方法的第一个挑战是从原始模型和3D准备的模型中提取并选择最相关的输入变量，这些变量通过表征准备过程的数据来完成。另一个挑战是配置学习模型，尽管准备过程和可用数据的示例数量有限，但配置学习模型能够准确评估过程的质量（新情况下唯一已知的数据是表征原始CAD模型和模拟案例）。最后，过程绩效的估算器将帮助分析人员选择CAD模型准备操作。这不能免除分析人员进行数值模拟的麻烦。但是，这将更快地获得最佳质量的简化模型。使用AI技术（例如众所周知的神经网络和决策树）获得将输出变量链接到输入变量的规则。所提出的方法在CFD模拟的背景下在工业实例上得到了说明和验证。

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    DANGLADE Florence;

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  • 年度 2015
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  • 原文格式 PDF
  • 正文语种 fr
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