Outubro 2023 vol. 10 num. 5 - IX Simpósio Internacional de Inovação e Tecnologia
Full article - Open Access.
PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR REGRESSION OF FINITE ELEMENT ANALYSIS (FEA) SIMULATION DESIGN DATA
PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR REGRESSION OF FINITE ELEMENT ANALYSIS (FEA) SIMULATION DESIGN DATA
Dias, Victor Leão da Silva ; Guarieiro, Lilian Lefol Nani ; Nascimento, Erick Giovani Sperandio ;
Full article:
The process of developing the design for new parts and structures through FE simulations demands significant human and computational effort. By employing the multi-objective optimization method using DOE and metamodels, it is possible to achieve optimal design parameters faster and with greater precision. Thus, this study assessed the efficiency of using Machine Learning as metamodels to represent the behavior of FE models. Conventional methods were trained with and without data normalization and standardization, employing cross-validation and hyperparameter tuning. Ultimately, this analysis provides the best models for different types of design data, making their utilization viable in certain cases.
Full article:
The process of developing the design for new parts and structures through FE simulations demands significant human and computational effort. By employing the multi-objective optimization method using DOE and metamodels, it is possible to achieve optimal design parameters faster and with greater precision. Thus, this study assessed the efficiency of using Machine Learning as metamodels to represent the behavior of FE models. Conventional methods were trained with and without data normalization and standardization, employing cross-validation and hyperparameter tuning. Ultimately, this analysis provides the best models for different types of design data, making their utilization viable in certain cases.
Palavras-chave: machine learning, multi-objective optimization, finite element analysis,
Palavras-chave: machine learning, multi-objective optimization, finite element analysis,
DOI: 10.5151/siintec2023-306412
Referências bibliográficas
- [1] VARDHN, H., SZTIPANOVITS, J. Deep Learning based FEA Surrogate for SubSea Pressure Vessel. 6th International Conference on Computer, Software and Modeling (ICCSM), p. 36-39, 2022. SONG, T., ZHANG, Z., LIU, H. and HU, W., Multi-objective optimisation design and performance comparison of permanent magnet synchronous motor for EVs based on FEA. IET Electric Power Applications, 13, p. 1157-1166, 2019. VON WYSOCKI, T., RIEGER, F., TSOKAKTSIDIS, D.E., GAUTERIN, F. Generating Component Designs for an Improved NVH Performance by Using an Artificial Neural Network as an Optimization Metamodel. Designs, 5, 36, 202 DÍAZ, N. J. G. Algoritmo de Otimização Multi-Objetivo Assistida por Metamodelagem com Aplicações em Problemas de Aerodinâmica. Tese – Térmica, Fluidos e Máquinas de Fluxo, Instituto de Engenharia Mecânica, Universidade Federal de Itajubá, 2020. YOU, Y.-m. Multi-Objective Optimal Design of Permanent Magnet Synchronous Motor for Electric Vehicle Based on Deep Learning. Appl. Sci., 10, 482, 2020. ULLAH, I., YAMAMOTO, T., AL MAMLOOK, R., JAMAL, A., LIU, K. A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability. Energy & Environment, 33, p. 1583– 1612, 202 SHUI, L., CHEN, F., GARG, A. et al. Design optimization of battery pack enclosure for electric vehicle. Struct Multidisc Optim, 58, p. 331–347, 2018.
Como citar:
Dias, Victor Leão da Silva ; Guarieiro, Lilian Lefol Nani ; Nascimento, Erick Giovani Sperandio ; "PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR REGRESSION OF FINITE ELEMENT ANALYSIS (FEA) SIMULATION DESIGN DATA", p. 541-547 . In: .
São Paulo: Blucher,
2023.
ISSN 2357-7592,
DOI 10.5151/siintec2023-306412
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