Thermal modeling and uncertainty quantification of tool for automated garment assembly

被引:0
|
作者
Castrillon, Nicolas [1 ]
Rock, Avery [1 ]
Zohdi, Tarek I. [1 ]
机构
[1] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA
关键词
Uncertainty quantification; Finite element method; Thermal modeling; Automation; Garments; SENSITIVITY-ANALYSIS; OPTIMIZATION; DESIGN;
D O I
10.1007/s00466-022-02215-5
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this work, a thermal Finite Element model is developed to simulate the performance of a blade-like tool for robotic work cells performing automated garment production using a novel thermoplastic stiffening layer. Uncertainty quantification and sensitivity analysis are applied to determine the most important design properties and optimize key performance metrics for swift and reliable garment assembly. Attention is focused on the geometric and thermal design properties that minimize sensitivity to environmental conditions while maximizing expected productivity. An example design is shown for illustrative purposes. This work may inform future design innovation for similar heating tools and reduce the need for physical experiments and long calibration times on the factory floor.
引用
收藏
页码:879 / 889
页数:11
相关论文
共 50 条
  • [31] UNCERTAINTY QUANTIFICATION IN DAMAGE MODELING OF HETEROGENEOUS MATERIALS
    Bogdanor, Michael J.
    Mahadevan, Sankaran
    Oskay, Caglar
    INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING, 2013, 11 (03) : 289 - 307
  • [32] SPECIAL ISSUE ON UNCERTAINTY QUANTIFICATION AND STOCHASTIC MODELING
    Beck, Andre T.
    Trindade, Marcelo A.
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2013, 3 (06) : VII - VIII
  • [33] Design of experiments: a statistical tool for PIV uncertainty quantification
    Adatrao, Sagar
    van der Velden, Simone
    van der Meulen, Mark-Jan
    Bordes, Marc Cruellas
    Sciacchitano, Andrea
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (01)
  • [34] Configurable tool for automated exocytotic events quantification.
    Lee, S-J. J.
    Kenyon, Z.
    Wadadekar, T.
    Watanabe, H.
    Numano, R.
    Tsuboi, T.
    MOLECULAR BIOLOGY OF THE CELL, 2012, 23
  • [35] A Planning Tool for the Automated Quantification and Visualization of Blue Space
    Hellmanns J.
    Schiewe J.
    Kistemann T.
    Höser C.
    KN - Journal of Cartography and Geographic Information, 2019, 69 (1) : 63 - 71
  • [36] Automated planning to minimise uncertainty of machine tool calibration
    Parkinson, S.
    Longstaff, A. P.
    Fletcher, S.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2014, 30 : 63 - 72
  • [37] Automated uncertainty quantification analysis using a system model and data
    Nannapaneni, Saideep
    Mahadevan, Sankaran
    Lechevalier, David
    Narayanan, Anantha
    Rachuri, Sudarsan
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2015, : 1408 - 1417
  • [38] Photoelectric factor prediction using automated learning and uncertainty quantification
    Alsamadony, Khalid
    Ibrahim, Ahmed Farid
    Elkatatny, Salaheldin
    Abdulraheem, Abdulazeez
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (30): : 22595 - 22604
  • [39] Photoelectric factor prediction using automated learning and uncertainty quantification
    Khalid Alsamadony
    Ahmed Farid Ibrahim
    Salaheldin Elkatatny
    Abdulazeez Abdulraheem
    Neural Computing and Applications, 2023, 35 : 22595 - 22604
  • [40] GEOMETRIC MODELING SYSTEM FOR AUTOMATED MECHANICAL ASSEMBLY
    WESLEY, MA
    LOZANOPEREZ, T
    LIEBERMAN, LI
    LAVIN, MA
    GROSSMAN, DD
    IBM JOURNAL OF RESEARCH AND DEVELOPMENT, 1980, 24 (01) : 64 - 74