A new article by Econ has been published in the journal Heliyon (IF=4,0), which deals with a fitting method for composite failure models based on parameter identification and iterative experimental design. The co-authors of the article, in addition to László Kovács, Econ’s senior simulation engineer and R&D team leader, include Ádám Ipkovich, Alex Kummer, and Dr. János Abonyi from Pannon University, as well as Balázs Fodor from BMW’s development center in Munich. The paper presents an iterative, nonlinear design of experiments (DoE) based method capable of identifying the most informative mechanical test failure results for certain failure models, such as the Tsai-Wu, Tsai-Hill, Hashin, maximum stress, and Puck theories.
The authors proposed a novel method for identifying failure models and their model parameters in this technical paper. Fisher Matrix-based sensitivity analysis was performed for identifiability analysis, and then the identifiability of the model parameters has been inferred from their sensitivity. The authors used design of experiments (DoE) technique to select the most informative subsets of experiments, and finally, developed a clustering- and linear programming-based experiment proposing algorithm.
The study was published in the journal Heliyon (IF=4,0) in April, under the title ” Iterative experimental design and identifiability analysis of composite material failure models”.
Abstract
The present work proposes an iterative, nonlinear design of experiments (DoE) approach that finds the most informative experimental data to identify the parameters of the Tsai-Wu, Tsai-Hill, Hoffman, Hashin, max stress, and Puck failure models. Depending on the data, the models perform differently, therefore, the parameter identification is validated by the Euclidean distance of the measured points to the closest ones on the nominal surface. The resulting errors provide a base for the ranking of the models, which helps to select the best fitting. Lastly, an iterative design of the experiments is implemented to select the optimal set of experiments from which the parameters can be identified from the least data by minimizing the fitting error. In this way, the number of experiments required for the identification of a model of a composite material can be significantly reduced. The authors demonstrate how the proposed method selected the most optimal experiments out of generated data. The results indicate that if the dataset contains enough information, the method is robust and accurate.
The full professional article can be downloaded in PDF format here.
The article and the related study were realized as part of the joint R&D project between Econ Engineering and Pannon University, supported by NKFIH .
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