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| Abstract: |
| To gain a more comprehensive understanding and evaluate foam aluminum"s performance, researchers have introduced various characterization indicators. However, the current understanding of the significance of these indicators in analyzing foam aluminum"s performance is limited. This study employs the Generalized Regression Neural Network (GRNN) method to establish a model that links foam aluminum"s microstructure characterization data with its mechanical properties. Through the GRNN model, researchers extracted four of the most crucial features and their corresponding weight values from the 13 pore characteristics of foam aluminum. Subsequently, a new characterization formula, called "Wang equivalent porosity" (WEP), was developed by using residual weights assigned to the feature weights, and four parameter coefficients were obtained. This formula aims to represent the relationship between foam aluminum"s microstructural features and its mechanical performance. Furthermore, the researchers conducted model verification using compression data from 11 sets of foam aluminum. The validation results showed that among these 11 foam aluminum datasets, the Gibson-Ashby formula yielded anomalous results in two cases, whereas WEP exhibited exceptional stability without any anomalies. In comparison to the Gibson-Ashby formula, WEP demonstrated an 18.18% improvement in evaluation accuracy. |
| Key words: aluminum foam, characterization index, importance analysis, feature learning |
| DOI：10.11916/j.issn.1005-9113.2023100 |
| Clc Number:G146.21，TG496 |
| Fund: |
