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.

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.

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DOI: 10.5151/siintec2023-306412

Referências bibliográficas

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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 23577592, DOI 10.5151/siintec2023-306412