基于声发射的LPBF不稳定熔池状态的不确定性驱动可信识别范式

IF 11.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Jiafeng Tang , Kunpeng Tan , Junlong Tang , Zhibin Zhao , Xingwu Zhang , Xuefeng Chen
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引用次数: 0

摘要

激光粉末床融合技术以其高精度和高灵活性,在航空航天、生物医药等领域的关键部件生产中占有重要地位。然而,在制造过程中确保质量的一致性仍然是一个令人头痛的挑战。在线监测熔池状态并实施相应的闭环反馈控制是提高熔池质量稳定性的有效途径。特别是,在线监测和基于深度学习(DL)的方法的结合正在获得显着的牵引力。不幸的是,深度学习模型的“黑箱”性质降低了其预测的可靠性。此外,熔融池复杂的多物理场耦合特性常常导致监测数据的瞬态波动,表现出层间和层内的非均匀性,这加深了DL方法和闭环控制的可信度危机。在这项工作中,我们提出了一种可靠的范式来识别LPBF, MSRIM(熔池状态可靠识别模型)的层间和层内熔池不稳定状态。它输出预测和不确定性,使控制系统能够根据置信度动态调整策略。具体而言,我们分析和研究了不同情景下熔池波动引起的处理数据的异质性,以及这种异质性带来的不确定性。然后,对不同来源的不确定因素进行量化和分解,为在线质量控制提供可靠的依据。此外,我们开发了定制的LPBF熔池全处理声发射(AE)监测系统,并创建了基于AE的数据集,包括36组参数和三种熔池状态,以验证我们的工作。大量的实验表明,我们的模式可以实现令人满意和可靠的熔池状态识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertainty-driven trustworthy identification paradigm for unstable melt pool state based on acoustic emission in LPBF
Thanks to the high precision and flexibility, laser powder bed fusion (LPBF) has hugged in producing key components for the fields of aerospace and biomedicine. However, ensuring the consistent of quality during the manufacturing process remains a headache challenge. Online monitoring the state of melt pool and implementing related closed-loop feedback control is a promising solution to improve quality stability. Especially, the combination of online monitoring and deep learning (DL)-based methods is gaining significant traction. Unfortunately, the ‘black-box’ nature of DL models reduces their reliability of prediction. Additionally, the complex multiphysics-coupled nature of the melt pool often causes transient fluctuations that manifest the inter-layer and intra-layer heterogeneity in monitoring data, which deepens the credibility crisis of DL methods and closed-loop control. In this work, we propose a reliable paradigm for identifying the unstable state of melt pool over inter-layer and intra-layer in LPBF, MSRIM (Melt pool State Reliable Identification Model). It outputs both predictions and their uncertainties, enabling control systems to dynamically adjust strategies based on confidence levels. Concretely, we analyze and investigate the heterogeneity of processing data caused by fluctuations of melt pool under different scenarios, along with the uncertainties introduced by such heterogeneity. Then, we quantify and decompose the uncertainties from different sources, and provides a reliable foundation for online control of quality. Furthermore, we develop a custom LPBF melt pool full-processing acoustic emission (AE) monitoring system and created an AE-based dataset including 36 groups of parameters with three melt pool states for verifying our work. Extensive experiments demonstrate that our paradigm achieves the satisfactory and reliable melt pool state identification.
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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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