Data-augmented modeling in laser powder bed fusion: A Bayesian approach

IF 10.3 1区 工程技术 Q1 ENGINEERING, MANUFACTURING Additive manufacturing Pub Date : 2024-09-25 DOI:10.1016/j.addma.2024.104545
Peter Morcos , Brent Vela , Cafer Acemi , Alaa Elwany , Ibrahim Karaman , Raymundo Arróyave
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Abstract

The laser powder bed fusion (LPBF) technique has become increasingly prominent in metal additive manufacturing. However, tuning parameters for printing defect-free parts requires labor-intensive experimental work and computationally expensive simulations. Moreover, to calibrate LPBF models against the experimental data, typically MCMC methods or similar methods are used which is also time-consuming. These procedures are viable when calibrating LPBF models against data for individual chemistries but are not efficient for alloy design. A rapid method to calibrate LPBF models is needed to design for printable alloys. We address this challenge by integrating a low-fidelity analytical thermal model, a machine learning model, and proxy experimental data to create an accurate and rapidly-trained model that leverages the principles of Bayesian updating. As a case study in ‘printability extrapolation’, a dataset of 195 single-tracks on 16 unique chemistries was used to probe the method’s ability to predict melt-pool dimensions on ‘unseen’ chemistries. As a case study in ‘printability interpolation’ the framework was deployed on two compositions that were studied rigorously in the literature for their printability, namely, the ultra-high strength martensitic steel alloy AF9628 and the nickel super alloy 718. The interpolative/extrapolative abilities of the proposed method were compared to a set of 4 control models under data sparse conditions.
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激光粉末床融合中的数据增强建模:贝叶斯方法
激光粉末床熔融(LPBF)技术在金属增材制造领域的应用日益突出。然而,要调整参数以打印出无缺陷的零件,需要进行劳动密集型的实验工作和计算成本高昂的模拟。此外,要根据实验数据校准 LPBF 模型,通常需要使用 MCMC 方法或类似方法,这也很耗时。在根据单个化学成分的数据校准 LPBF 模型时,这些程序是可行的,但对于合金设计来说,这些程序并不高效。设计可打印合金需要一种校准 LPBF 模型的快速方法。为了应对这一挑战,我们将低保真分析热模型、机器学习模型和代理实验数据整合在一起,利用贝叶斯更新原理创建了一个精确、快速训练的模型。作为 "可印刷性外推法 "的案例研究,我们使用了一个包含 16 种独特化学成分的 195 个单轨数据集,以探究该方法预测 "未见 "化学成分熔池尺寸的能力。作为 "可印刷性内插法 "的案例研究,该框架被应用于文献中对其可印刷性进行过严格研究的两种成分,即超高强度马氏体钢合金 AF9628 和镍超级合金 718。在数据稀少的条件下,将所提出方法的内插/外推能力与一组 4 个控制模型进行了比较。
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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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