The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

被引：0
|
作者：
Headley, Connor V. [1 ]
Herrera del Valle, Roberto J. [1 ]
Ma, Ji [1 ]
Balachandran, Prasanna [1 ,2 ]
Ponnambalam, Vijayabarathi [3 ]
LeBlanc, Saniya [3 ]
Kirsch, Dylan [4 ,5 ]
Martin, Joshua B. [4 ]
机构：
[1] Department of Materials Science and Engineering, University of Virginia, Charlottesville,VA,22903, United States
[2] Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville,VA,22903, United States
[3] Department of Mechanical and Aerospace Engineering, George Washington University, Washington D.C.,20052, United States
[4] Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg,MD,20899, United States
[5] Department of Materials Science & Engineering, University of Maryland, College Park,MD,20742, United States
来源：
Journal of Manufacturing Processes | 2024年 / 116卷
关键词：
Compendex;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Iterative methods
引用
收藏
页码：165 / 175
相关论文
共 50 条
  • [21] A stethoscope-guided interpretable deep learning framework for powder flow diagnosis in cold spray additive manufacturing
    Lee, Jiho
    Akin, Semih
    Sim, Yuseop
    Lee, Hojun
    Kim, Eunseob
    Nam, Jungsoo
    Song, Kyeongeun
    Jun, Martin B.G.
    Manufacturing Letters, 2024, 41 : 1515 - 1525
  • [22] Machine learning aided malware detection for secure and smart manufacturing: a comprehensive analysis of the state of the art
    Rani, Sangeeta
    Tripathi, Khushboo
    Kumar, Ajay
    International Journal on Interactive Design and Manufacturing, 2023,
  • [23] Properly and automatically naming java methods: A machine learning based approach
    Yu, Shusi
    Zhang, Ruichang
    Guan, Jihong
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7713 LNAI : 235 - 246
  • [24] Forecasting Crude Oil Price Using SARIMAX Machine Learning Approach
    Tahseen Mohammad, Farah
    Krupasindhu Panigrahi, Shrikant
    2023 International Conference on Sustainable Islamic Business and Finance, SIBF 2023, 2023, : 131 - 135
  • [25] Aircraft Categorization Approach Using Machine Learning to Analyze Aircraft Behavior
    Vincent-Boulay, Nicolas
    Marsden, Catharine
    Journal of Air Transportation, 2024, 32 (04): : 218 - 229
  • [26] Comprehensive review of depression detection techniques based on machine learning approach
    Pinto, Smitha Joyce
    Parente, Mimmo
    Soft Computing, 2024, 28 (17-18) : 10701 - 10725
  • [27] Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
    Vasylenko, Andrij
    Antypov, Dmytro
    Gusev, Vladimir V.
    Gaultois, Michael W.
    Dyer, Matthew S.
    Rosseinsky, Matthew J.
    NPJ COMPUTATIONAL MATERIALS, 2023, 9 (01)
  • [28] Deep learning based sinogram interpolation applied to X-ray CT measurements of polymer additive manufacturing parts
    Bellens, Simon
    Janssens, Michel
    Vandewalle, Patrick
    Dewulf, Wim
    e-Journal of Nondestructive Testing, 2022, 27 (03):
  • [29] A machine learning-based framework for automatic identification of process and product fingerprints for smart manufacturing systems
    Kundu, Pradeep
    Luo, Xichun
    Qin, Yi
    Cai, Yukui
    Liu, Zhanqiang
    Journal of Manufacturing Processes, 2022, 73 : 128 - 138
  • [30] A Review of Using CNN Approach for Lung Cancer Detection Through Machine Learning
    Thaseen, Mohseena
    Umamaheswaran, S.K.
    Naik, Darshana A
    Aware, Mrunal Swapnil
    Pundhir, Prachi
    Pant, Bhasker
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, 2022, : 1236 - 1239
12345