通过训练具有随机合成数据的物理信息神经网络来加速增材制造中的热模拟

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jiangce Chen, Justin Pierce, Glen Williams, Timothy W. Simpson, N. Meisel, Sneha Prabha Narra, Christopher McComb
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引用次数: 0

摘要

在基于融合的增材制造(AM)工艺中,增材制造部件的温度历史对确定工艺-结构-性能关系起着至关重要的作用。因此,从零件设计和工艺规划的温度历史预测到制造过程中的现场温度监测和控制,各种增材制造任务都需要快速的热模拟方法。然而,由于涉及的空间和时间尺度较大,传统的数值模拟方法无法满足这些应用的严格要求。虽然数据驱动的代理模型因其快速计算能力而受到关注,但这些模型的性能依赖于训练数据的大小和质量,而创建这些数据的成本通常非常高。物理信息神经网络(pinn)通过在训练过程中施加物理原理来减轻对大型数据集的需求。本工作研究了在激光粉末床熔合(L-PBF)制造过程中,使用PINN来预测零件的时变温度分布。值得注意的是,本研究中使用的PINN使模型能够仅在随机合成的数据上进行训练。这种训练数据的获取成本低廉,而且数据集中的随机性提高了训练模型的泛化能力。结果表明,PINN模型比在标记数据上训练的同类人工神经网络具有更高的准确率。此外,在这项工作中训练的PINN模型在预测激光路径扫描策略的温度方面保持了很高的准确性,而这些策略在训练数据中是看不到的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ACCELERATING THERMAL SIMULATIONS IN ADDITIVE MANUFACTURING BY TRAINING PHYSICS-INFORMED NEURAL NETWORKS WITH RANDOMLY-SYNTHESIZED DATA
The temperature history of an additively-manufactured part plays a critical role in determining process-structure-property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in-situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of these applications due to the large space and time scales involved. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with Laser Powder Bed Fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly-synthesized data. This training data is both inexpensive to obtain and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.
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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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