Uncertainty Quantification and Optimal Robust Design for Machining Operations

被引:1
|
作者
Wan, Jinming [1 ]
Che, Yiming [1 ]
Wang, Zimo [1 ]
Cheng, Changqing [1 ]
机构
[1] SUNY Binghamton, Dept Syst Sci & Ind Engn, 4400 Vestal Pkwy, Binghamton, NY 13902 USA
基金
美国国家科学基金会;
关键词
uncertainty quantification; robust optimal design; stochastic kriging; Gaussian process; conditional value-at-risk; computational foundations for engineering optimization; data-driven engineering; machine learning; STABILITY LOBE DIAGRAMS; CHATTER STABILITY; OPTIMIZATION; PREDICTION; MODELS; WORKPIECES; VARIANCE; WAFER;
D O I
10.1115/1.4055039
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this study, we carry out robust optimal design for the machining operations, one key process in wafer polishing in chip manufacturing, aiming to avoid the peculiar regenerative chatter and maximize the material removal rate (MRR) considering the inherent material and process uncertainty. More specifically, we characterize the cutting tool dynamics using a delay differential equation (DDE) and enlist the temporal finite element method (TFEM) to derive its approximate solution and stability index given process settings or design variables. To further quantify the inherent uncertainty, replications of TFEM under different realizations of random uncontrollable variables are performed, which however incurs extra computational burden. To eschew the deployment of such a crude Monte Carlo (MC) approach at each design setting, we integrate the stochastic TFEM with a stochastic surrogate model, stochastic kriging, in an active learning framework to sequentially approximate the stability boundary. The numerical result suggests that the nominal stability boundary attained from this method is on par with that from the crude MC, but only demands a fraction of the computational overhead. To further ensure the robustness of process stability, we adopt another surrogate, the Gaussian process, to predict the variance of the stability index at unexplored design points and identify the robust stability boundary per the conditional value at risk (CVaR) criterion. Therefrom, an optimal design in the robust stable region that maximizes the MRR can be identified.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Quantification of uncertainty in machining operations based on probabilistic and robust approaches
    Hajdu, David
    Insperger, Tamas
    Stepan, Gabor
    [J]. 8TH CIRP CONFERENCE ON HIGH PERFORMANCE CUTTING (HPC 2018), 2018, 77 : 82 - 85
  • [2] Optimal and robust design of docking blocks with uncertainty
    Cheng, YS
    Au, FTK
    Tham, LG
    Zeng, GW
    [J]. ENGINEERING STRUCTURES, 2004, 26 (04) : 499 - 510
  • [3] Decoupling uncertainty quantification from robust design optimization
    Tanmoy Chatterjee
    Rajib Chowdhury
    Palaniappan Ramu
    [J]. Structural and Multidisciplinary Optimization, 2019, 59 : 1969 - 1990
  • [4] Uncertainty quantification guided robust design for nanoparticles' morphology
    He, Y.
    Razi, M.
    Forestiere, C.
    Dal Negro, L.
    Kirby, R. M.
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2018, 336 : 578 - 593
  • [5] An Evidence Theory Based Uncertainty Quantification for Robust Design
    Wei, Fayuan
    Hu, Junming
    Ge, Renwei
    [J]. INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL, 2012, 15 (12B): : 5811 - 5818
  • [6] Decoupling uncertainty quantification from robust design optimization
    Chatterjee, Tanmoy
    Chowdhury, Rajib
    Ramu, Palaniappan
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2019, 59 (06) : 1969 - 1990
  • [7] Optimal design for kernel interpolation: Applications to uncertainty quantification
    Narayan, Akil
    Yan, Liang
    Zhou, Tao
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 430
  • [8] A COMPUTATIONAL TECHNIQUE FOR UNCERTAINTY QUANTIFICATION AND ROBUST DESIGN IN CARDIOVASCULAR SYSTEMS
    Sankaran, Sethuraman
    Feinstein, Jeffrey A.
    Marsden, Alison L.
    [J]. PROCEEDINGS OF THE ASME SUMMER BIOENGINEERING CONFERENCE - 2009, PT A AND B, 2009, : 17 - 18
  • [9] UNCERTAINTY QUANTIFICATION FOR ROBUST CONTROL DESIGN OF SMART MATERIAL SYSTEMS
    McMahan, Jerry A.
    Smith, Ralph C.
    [J]. PROCEEDINGS OF THE ASME CONFERENCE ON SMART MATERIALS, ADAPTIVE STRUCTURES, AND INTELLIGENT SYSTEMS - 2013, VOL 1, 2014,
  • [10] Optimal Uncertainty Quantification
    Owhadi, H.
    Scovel, C.
    Sullivan, T. J.
    McKerns, M.
    Ortiz, M.
    [J]. SIAM REVIEW, 2013, 55 (02) : 271 - 345