Multi-layer thermal history prediction framework for directed energy deposition based on extended physics-informed neural networks (XPINN)

IF 11.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Bohan Peng, Ajit Panesar
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引用次数: 0

Abstract

This paper presents an eXtended physics-informed neural networks (XPINN)-based framework for predicting the temperature history during a multi-layer Directed Energy Deposition (DED) process. The proposed XPINN-based framework, advancing from its PINN-based counterpart, demonstrates significant accuracy improvement, around 50% reduction in RMSE and maximum absolute error, and extended capability of temperature history prediction with domain decomposition for more complex configurations such as interpass time, void, and interruption of scan that are prevalent in real-life DED designs. It is validated via a series of 2D benchmark tests against numerical simulations with an increasing degree of complexity. The effect of different domain decompositions is compared and discussed. Strategies that improve the training outcome are also proposed and analysed. With the enhanced capability of working on more complex configurations while retaining the characteristic availability of derivative information, the proposed framework brings process-ware design optimisation based on scientific machine learning (SciML) techniques one step closer to the application to real-life additive manufacturing applications.
基于扩展物理信息神经网络(XPINN)的定向能沉积多层热历史预测框架
本文提出了一种基于扩展物理信息神经网络(XPINN)的框架,用于多层定向能沉积(DED)过程的温度历史预测。提出的基于xpup的框架,在基于pup的框架的基础上,显示出显著的精度提高,RMSE和最大绝对误差降低了约50%,并且扩展了温度历史预测的域分解能力,适用于更复杂的配置,如在实际的DED设计中普遍存在的interpass time, void和扫描中断。通过一系列2D基准测试,对复杂程度不断增加的数值模拟进行了验证。比较和讨论了不同领域分解的效果。提出并分析了提高培训效果的策略。通过增强处理更复杂配置的能力,同时保留衍生信息的特征可用性,所提出的框架使基于科学机器学习(SciML)技术的过程软件设计优化更接近于实际增材制造应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Additive manufacturing
Additive manufacturing Materials Science-General Materials Science
CiteScore
19.80
自引率
12.70%
发文量
648
审稿时长
35 days
期刊介绍: Additive Manufacturing stands as a peer-reviewed journal dedicated to delivering high-quality research papers and reviews in the field of additive manufacturing, serving both academia and industry leaders. The journal's objective is to recognize the innovative essence of additive manufacturing and its diverse applications, providing a comprehensive overview of current developments and future prospects. The transformative potential of additive manufacturing technologies in product design and manufacturing is poised to disrupt traditional approaches. In response to this paradigm shift, a distinctive and comprehensive publication outlet was essential. Additive Manufacturing fulfills this need, offering a platform for engineers, materials scientists, and practitioners across academia and various industries to document and share innovations in these evolving technologies.
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