An automated computational framework to construct printability maps for additively manufactured metal alloys

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-11-06 DOI:10.1038/s41524-024-01436-x
Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave
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Abstract

In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map classifies regions in the processing space where an alloy can be printed with or without porosity defects. However, the creation of these printability maps is resource-intensive. Previous efforts to generate printability maps have required single-track experiments on pre-alloyed powder, limiting the utilization of these printability maps for the high-throughput design of printable alloys. We address these challenges in the case of Laser Powder Bed Fusion AM (L-PBF-AM) by introducing a fully computational, predictive approach to create printability maps for arbitrary alloys. Our framework uses physics-based thermal models and a variety of defect formation criteria. We benchmark the predictive ability of the proposed framework against literature data for the following commonly printed alloys: 316 Stainless Steel, Inconel 718, Ti-6Al-4V, AF96, and Ni-5Nb. Furthermore, we deploy the framework on NiTi-based Shape Memory Alloys (SMAs) as a case study. We scrutinize the accuracy of various sets of defect criteria and use these accuracy measurements to create an uncertainty-aware probabilistic framework capable of predicting the printability maps of arbitrary alloys. This framework has the potential to guide alloy designers to potentially easy-to-print alloys, enabling the co-design of high-performing printable alloys.

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构建增材制造金属合金可印刷性图的自动化计算框架
在金属增材制造(AM)过程中,加工参数会影响宏观缺陷(熔合不足、键孔、球化)的形成概率,进而危及最终产品的完整性。印刷适性图可对加工空间中的区域进行分类,在这些区域中,合金可印刷出有或无气孔缺陷的产品。然而,创建这些印刷适性图需要大量资源。以前生成印刷适性图的工作需要在预合金粉末上进行单轨实验,从而限制了利用这些印刷适性图进行可印刷合金的高通量设计。我们在激光粉末床熔融 AM(L-PBF-AM)中引入了一种完全计算的预测方法,为任意合金创建可印刷性地图,从而解决了这些难题。我们的框架采用基于物理的热模型和各种缺陷形成标准。我们以文献数据为基准,对以下常见印刷合金的预测能力进行了评估:316不锈钢、Inconel 718、Ti-6Al-4V、AF96和Ni-5Nb。此外,我们还在镍钛基形状记忆合金(SMA)上部署了该框架作为案例研究。我们仔细研究了各种缺陷标准集的准确性,并利用这些准确性测量结果创建了一个不确定性感知概率框架,该框架能够预测任意合金的印刷适性图。该框架有望引导合金设计人员选择潜在的易打印合金,从而实现高性能可打印合金的协同设计。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
期刊最新文献
Deep learning generative model for crystal structure prediction High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy An automated computational framework to construct printability maps for additively manufactured metal alloys Opportunities for retrieval and tool augmented large language models in scientific facilities Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints
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