CDS&E: Computation-Informed Learning of Melt Pool Dynamics for Real-Time Prognosis

CDS

基本信息

  • 批准号:
    2152908
  • 负责人:
  • 金额:
    $ 50.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-07-01 至 2025-06-30
  • 项目状态:
    未结题

项目摘要

Metal additive manufacturing (AM) offers a great opportunity for making complex parts. However, the collective impact of complex part geometry, nonuniform heat dissipation, and diverse laser scanning often cause overheating of the melt pool during the printing process. The overheating problem leads to various quality issues. Therefore, the understanding and fast prediction of melt pool behaviors are necessary for printing high-quality parts. Data science models (e.g., deep learning, or DL) may use diverse types of melt pool data for efficient prediction of overheating. But the data science models lack transparency, are computationally expensive, and need massive training data. On the other hand, computational models may understand the complex melt pool behaviors, but require continuous updates of model parameters and are not suitable for fast prediction. This award provides an integrated approach by using the strength of both models for fast prediction of melt pool overheating. The outcome of this project will not only contribute to the fundamental knowledge of deep learning but also enable the broad acceptance of the project's testbed as a public tool for the AM community. The results will help many industry sectors including aerospace, healthcare, tools, and mold, automotive, and others. The project’s interdisciplinary nature also helps train the future digital manufacturing workforce by broadening the participation of women and underrepresented minority groups in data science-driven research and education.This research bridges the knowledge gap in fundamental understanding and real-time prognosis of melt pool dynamics by developing a new computation-informed deep learning (Co-DL) approach. The research team will: (1) develop a computational fluid dynamics (CFD) model of selective laser melting (SLM) to generate complementary data which cannot be measured otherwise; (2) create cyberinfrastructure to enable multimodal data curation, contextualization, integration, and interoperability, extracting knowledge from data analytics, and interfacing Co-DL testbed; (3) develop a Co-DL modeling method to integrate physical laws of melt pool dynamics and augmented data from the CFD model into DL training and learning algorithm; (4) create a set of DL acceleration and semi-supervised learning approaches with small data; and (5) create a real-time online Co-DL testbed for the metal AM community. The resulting method will solve a major limitation of pure data-driven DL models for lacking explainability, significantly reduce the time-latency of the Co-DL model training and inference, and create cyberinfrastructure to enable data curation, contextualization, integration, interoperability, and interfacing with the Co-DL testbed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
金属增材制造(AM)为制造复杂零件提供了很好的机会。然而,复杂的零件几何形状、不均匀的散热和多样化的激光扫描的共同影响往往会导致打印过程中熔池过热。过热问题导致各种质量问题。因此,了解和快速预测熔池的行为是必要的打印高品质的零件。数据科学模型(例如,深度学习或DL)可以使用各种类型的熔池数据来有效预测过热。但数据科学模型缺乏透明度,计算成本高,需要大量的训练数据。另一方面,计算模型可以理解复杂的熔池行为,但需要不断更新模型参数,不适合快速预测。该奖项提供了一个综合的方法,通过使用两个模型的强度快速预测熔池过热。该项目的成果不仅将有助于深度学习的基础知识,还将使该项目的测试平台作为AM社区的公共工具得到广泛接受。其结果将有助于许多行业,包括航空航天,医疗保健,工具和模具,汽车等。该项目的跨学科性质还有助于通过扩大妇女和代表性不足的少数群体在数据科学驱动的研究和教育中的参与来培养未来的数字化制造劳动力。该研究通过开发新的计算知情深度学习(Co-DL)方法,弥合了对熔池动态的基本理解和实时预测方面的知识差距。研究小组将:(1)开发选择性激光熔化(SLM)的计算流体动力学(CFD)模型,以生成无法以其他方式测量的补充数据;(2)创建网络基础设施,以实现多模式数据管理,情境化,集成和互操作性,从数据分析中提取知识,并连接Co-DL测试平台;(3)开发了一种Co-DL建模方法,将熔池动力学的物理规律和CFD模型的扩充数据集成到DL训练和学习算法中:(4)创建了一套小数据DL加速和半监督学习方法;以及(5)为金属AM社区创建实时在线Co-DL测试平台。由此产生的方法将解决纯数据驱动的DL模型缺乏可解释性的主要限制,显着减少Co-DL模型训练和推理的时间延迟,并创建网络基础设施以实现数据策展,情境化,集成,互操作性,与联合国合作,DL试验台。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查进行评估,被认为值得支持的搜索.

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Digital twins for electro-physical, chemical, and photonic processes
  • DOI:
    10.1016/j.cirp.2023.05.007
  • 发表时间:
    2023-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yuebin Guo;A. Klink;Paulo Bartolo;W. Guo
  • 通讯作者:
    Yuebin Guo;A. Klink;Paulo Bartolo;W. Guo
Physics-informed deep learning of gas flow-melt pool multi-physical dynamics during powder bed fusion
粉末床熔融过程中气流-熔池多物理动力学的物理信息深度学习
  • DOI:
    10.1016/j.cirp.2023.04.005
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sharma, Rahul;Raissi, Maziar;Guo, Yuebin
  • 通讯作者:
    Guo, Yuebin
The Case for Learned Provenance Graph Storage Systems
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hailun Ding;Juan Zhai;Dong Deng;Shiqing Ma
  • 通讯作者:
    Hailun Ding;Juan Zhai;Dong Deng;Shiqing Ma
On the probabilistic prediction for extreme geometrical defects induced by laser-based powder bed fusion
A statistics of the extremes-based method to predict the upper bound of geometrical defects in powder bed fusion
基于极值的粉床熔合几何缺陷上限预测方法的统计
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    P. Kousoulas, Y.B. Guo
  • 通讯作者:
    P. Kousoulas, Y.B. Guo
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Yuebin Guo其他文献

Explainable AI for layer-wise emission prediction in laser fusion
  • DOI:
    10.1016/j.cirp.2023.03.009
  • 发表时间:
    2023-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Weihong “Grace” Guo;Vidita Gawade;Bi Zhang;Yuebin Guo
  • 通讯作者:
    Yuebin Guo
Mining Infrequent Itemsets Based on Extended MMS Model
基于扩展MMS模型的非频繁项集挖掘
Predictive model to decouple the contributions of friction and plastic deformation to machined surface temperatures and residual stress patterns in finish dry cutting
  • DOI:
    10.1007/s11465-010-0097-7
  • 发表时间:
    2010-06-03
  • 期刊:
  • 影响因子:
    4.000
  • 作者:
    Subhash Anurag;Yuebin Guo
  • 通讯作者:
    Yuebin Guo

Yuebin Guo的其他文献

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{{ truncateString('Yuebin Guo', 18)}}的其他基金

Collaborative Research: Fusion of Siloed Data for Multistage Manufacturing Systems: Integrative Product Quality and Machine Health Management
协作研究:多级制造系统的孤立数据融合:集成产品质量和机器健康管理
  • 批准号:
    2323083
  • 财政年份:
    2024
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
FMRG: Cyber: Manufacturing USA: NextG-Enabled Manufacturing of the Future (NextGEM)
FMRG:网络:美国制造:支持 NextG 的未来制造 (NextGEM)
  • 批准号:
    2328260
  • 财政年份:
    2024
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Conference: Early-Career Researcher Travel Support for the 30th CIRP Life Cycle Engineering Conference May 15-17, 2023
会议:2023 年 5 月 15 日至 17 日第 30 届 CIRP 生命周期工程会议的早期职业研究员旅行支持
  • 批准号:
    2322400
  • 财政年份:
    2023
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Collaborative Research: Specific Energy-Based Prognosis for Machining Surface Integrity through Integration of Process Physics and Machine Learning
合作研究:通过过程物理和机器学习的集成,基于特定能量的加工表面完整性预测
  • 批准号:
    2040358
  • 财政年份:
    2021
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Electrical Discharge Machining of Biomedical Nitinol Alloys and the Resulting Fundamental Relationship of Microstructure-Property-Function
生物医用镍钛诺合金的放电加工及其微观结构-性能-功能的基本关系
  • 批准号:
    1234696
  • 财政年份:
    2012
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Hybrid Dry Cutting - Finish Burnishing of Novel Biodegradable Magnesium-Calcium Implants for Superior Corrosion Performance
混合干切削 - 新型可生物降解镁钙植入物的精加工抛光,具有卓越的腐蚀性能
  • 批准号:
    1000706
  • 财政年份:
    2010
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
GOALI: Six-Sigma Based Robust Process Design Under Tool Deterioration for Giga Fatigue Life of Precision Machined Components in Hard Turning
GOALI:基于 6-Sigma 的稳健工艺设计,在刀具磨损情况下实现硬车削中精密加工部件的千兆疲劳寿命
  • 批准号:
    0825780
  • 财政年份:
    2008
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Fabrication, Property and Function of the Nanostructured Surface Barrier for Hydrogen Storage
储氢纳米结构表面势垒的制备、性能和功能
  • 批准号:
    0700468
  • 财政年份:
    2007
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
Collaborative Research: Massive Parallel Laser Direct-Write of Sub-micron Dent Array for Quantum Leap of Fatigue Performance
合作研究:大规模并行激光直写亚微米凹痕阵列,实现疲劳性能的量子飞跃
  • 批准号:
    0555269
  • 财政年份:
    2006
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant
CAREER: A Fundamental Study on Hard Turning - Prediction and Synthesis of Surface Integrity and Component Life
职业生涯:硬车削的基础研究 - 表面完整性和零件寿命的预测和综合
  • 批准号:
    0447452
  • 财政年份:
    2005
  • 资助金额:
    $ 50.97万
  • 项目类别:
    Standard Grant

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