Multimodal data-driven machine learning for the prediction of surface topography in end milling

被引：0
|
作者：
L. Hu
H. Phan
S. Srinivasan
C. Cooper
J. Zhang
B. Yuan
R. Gao
Y. B. Guo
机构：
[1] Rutgers University-New Brunswick,Department of Mechanical and Aerospace Engineering
[2] Rutgers University-New Brunswick,New Jersey Advanced Manufacturing Institute
[3] Rutgers University-New Brunswick,Department of Electrical and Computer Engineering
[4] Case Western Reserve University,Department of Mechanical and Aerospace Engineering
来源：
Production Engineering | 2024年 / 18卷
关键词：
Machine learning; Multimodal data; Surface topography; Milling;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Prediction of surface topography in milling usually requires complex kinematics and dynamics modeling of the milling process, plus solving physical models of surface generation is a daunting task. This paper presents a multimodal data-driven machine learning (ML) method to predict milled surface topography. The proposed method predicts the height map of the surface topography by fusing process parameters and in-process acoustic information as model inputs. This method has been validated by comparing the predicted surface topography with the measured data.
引用
收藏
页码：507 / 523
页数：16
相关论文
共 50 条
  • [1] Multimodal data-driven machine learning for the prediction of surface topography in end milling
    Hu, L.
    Phan, H.
    Srinivasan, S.
    Cooper, C.
    Zhang, J.
    Yuan, B.
    Gao, R.
    Guo, Y. B.
    [J]. PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT, 2024, 18 (3-4): : 507 - 523
  • [2] Development of Data-Driven Machine Learning Models for the Prediction of Casting Surface Defects
    Chen, Shikun
    Kaufmann, Tim
    [J]. METALS, 2022, 12 (01)
  • [3] Data-driven models in machine learning for crime prediction
    Wawrzyniak, Zbigniew M.
    Jankowski, Stanislaw
    Szczechla, Eliza
    Szymanski, Zbigniew
    Pytlak, Radoslaw
    Michalak, Pawel
    Borowik, Grzegorz
    [J]. 2018 26TH INTERNATIONAL CONFERENCE ON SYSTEMS ENGINEERING (ICSENG 2018), 2018,
  • [4] A DATA-DRIVEN WORKFLOW FOR PREDICTION OF FRACTURING PARAMETERS WITH MACHINE LEARNING
    Zhu, Zhihua
    Hsu, Maoya
    Kun, Ding
    Wang, Tianyu
    He, Xiaodong
    Tian, Shouceng
    [J]. THERMAL SCIENCE, 2024, 28 (2A): : 1085 - 1090
  • [5] Prediction of casing damage: A data-driven, machine learning approach
    Zhao Y.
    Jiang H.
    Li H.
    [J]. International Journal of Circuits, Systems and Signal Processing, 2020, 14 : 1047 - 1053
  • [6] A DATA-DRIVEN WORKFLOW FOR PREDICTION OF FRACTURING PARAMETERS WITH MACHINE LEARNING
    Zhu, Zhihua
    Hsu, Maoya
    Kun, Ding
    Wang, Tianyu
    He, Xiaodong
    Tian, Shouceng
    [J]. THERMAL SCIENCE, 2024, 28 (02): : 1085 - 1090
  • [7] Apple fruit surface temperature prediction using weather data-driven machine learning models
    Goosman, Nelson D.
    Amogi, Basavaraj R.
    Khot, Lav R.
    [J]. PROCEEDINGS OF 2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, METROAGRIFOR, 2023, : 429 - 433
  • [8] A data-driven method for prediction of surface roughness with consideration of milling tool wear
    Zhang, Zhao
    Jia, Long
    Luo, Ming
    Wu, Baohai
    Zhang, Dinghua
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024, : 4271 - 4282
  • [9] Dirty engineering data-driven inverse prediction machine learning model
    Jin-Woong Lee
    Woon Bae Park
    Byung Do Lee
    Seonghwan Kim
    Nam Hoon Goo
    Kee-Sun Sohn
    [J]. Scientific Reports, 10
  • [10] Dirty engineering data-driven inverse prediction machine learning model
    Lee, Jin-Woong
    Park, Woon Bae
    Lee, Byung Do
    Kim, Seonghwan
    Goo, Nam Hoon
    Sohn, Kee-Sun
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
12345