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Directed Gaussian process metamodeling with improved firefly algorithm (iFA) for composite manufacturing uncertainty propagation analysis

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Abstract

A computationally effective and physically accurate metamodeling approach is demonstrated to analyze, under uncertainties, the spring-in angle deformation for composite manufacturing processes. Various uncertainties are inevitably present in this manufacturing process due to the heterogeneous thermo-mechanical properties of the composite materials. Analysis of uncertainty propagation using the direct Monte Carlo approach is computationally prohibitive, which calls for the employment of machine learning techniques and surrogate models or metamodels such as Gaussian processes (GP). While these approaches are promising, tuning model parameters and optimizing the hyperparameters are essential to predictive modeling performance. So far, most existing approaches rely on empirical experience through trial and error. Randomly selecting these hyperparameters results in excessive computational cost and poor convergence results. A nature-inspired methodology has been developed to guide the GP in selecting and optimizing the hyperparameters for the uncertainty propagation analysis of composite manufacturing processes. An improved firefly algorithm (iFA) takes account of the environmental factor. It disregards the contribution of a constant attractiveness factor, which in turn accelerates the convergence rate at the early stages of the generation and boosts the immunity of the proposed algorithm. The proposed methodology enabled selection of the proper combination of the factors for the GP and showed its merits over other state-of-the-art deterministic/metaheuristic algorithms, which is further confirmed by various nonparametric, multiple comparison tests.

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Funding

This research is supported by the AFRL Materials and Manufacturing Directorate (AFRL/RXMS) under contract FA8650-18-C-5700.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA

    Amit Kumar Ball, Dong Xu & Jiong Tang

  2. Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, USA

    Kai Zhou

  3. School of Aeronautics and Astronautics Engineering, Purdue University, West Lafeyette, IN, USA

    Dianyun Zhang

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A. Ball, K. Zhou, D. Xu, D. Zhang, and J. Tang worked together to generate the conception of the work. A. Ball carried out algorithm development, data analysis and interpretation, and drafted the paper. K. Zhou and D. Xu supported the algorithm development and data analysis. D. Zhang provided guidance on process modeling and results interpretation. J. Tang provided advisement to A. Ball and also provided critical revision of the paper.

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Ball, A.K., Zhou, K., Xu, D. et al. Directed Gaussian process metamodeling with improved firefly algorithm (iFA) for composite manufacturing uncertainty propagation analysis. Int J Adv Manuf Technol 126, 49–66 (2023). https://doi.org/10.1007/s00170-023-10994-1

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