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Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO)

Author

Listed:
  • Jia Hao

    (Beijing Institute of Technology)

  • Mengying Zhou

    (Beijing Institute of Technology)

  • Guoxin Wang

    (Beijing Institute of Technology)

  • Liangyue Jia

    (Beijing Institute of Technology)

  • Yan Yan

    (Beijing Institute of Technology)

Abstract

Surrogate models have been widely studied for optimization tasks in the domain of engineering design. However, the expensive and time-consuming simulation cycles needed for complex products always result in limited simulation data, which brings a challenge for building high accuracy surrogate models because of the incomplete information contained in the limited simulation data. Therefore, a method that builds a surrogate model and conducts design optimization by integrating limited simulation data and engineering knowledge through Bayesian optimization (BO-DK4DO) is presented. In this method, the shape engineering knowledge is considered and used as derivative information which is integrated with the limited simulation data with a Gaussian process (GP). Then the GP is updated sequentially by sampling new simulation data and the optimal design solutions are found by maximizing the GP. The aim of BO-DK4DO is to significantly reduce the required number of computer simulations for finding optimal design solutions. The BO-DK4DO is verified by using benchmark functions and an engineering design problem: hot rod rolling. In all scenarios, the BO-DK4DO shows faster convergence rate than the general Bayesian optimization without integrating engineering knowledge, which means the required amount of data is decreased.

Suggested Citation

  • Jia Hao & Mengying Zhou & Guoxin Wang & Liangyue Jia & Yan Yan, 2020. "Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2049-2067, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01551-8
    DOI: 10.1007/s10845-020-01551-8
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    References listed on IDEAS

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    1. Saurabh Pratap & Yash Daultani & M. K. Tiwari & Biswajit Mahanty, 2018. "Rule based optimization for a bulk handling port operations," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 287-311, February.
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    3. M’hammed Sahnoun & Belgacem Bettayeb & Samuel-Jean Bassetto & Michel Tollenaere, 2016. "Simulation-based optimization of sampling plans to reduce inspections while mastering the risk exposure in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1335-1349, December.
    4. James M. Calvin & Yvonne Chen & Antanas Žilinskas, 2012. "An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 628-636, November.
    5. Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
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    Cited by:

    1. Patrick Link & Miltiadis Poursanidis & Jochen Schmid & Rebekka Zache & Martin Kurnatowski & Uwe Teicher & Steffen Ihlenfeldt, 2022. "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2129-2142, October.
    2. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.

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