To gain a more comprehensive understanding and evaluate foam aluminum’s performance.researchers have introduced various characterization indicators. However, the current understanding otthe significance of these indicators in analyzing foam aluminum’s performance is limited. This studyemploys the Generalized Regression Neural Network ( GRNN) method to establish a model that linksfoam aluminum’s microstructure characterization data with its mechanical properties. Through the CRNNmodel , researchers extracted four of the most crucial features and their corresponding weight values fromthe 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 featureweights, and four parameter coefficients were obtained. This formula aims to represent the relationshipbetween foam aluminum’s microstructural features and its mechanical performance, Furthermore, theresearchers conducted model verification using compression data from 11 sets of foam aluminum. Thevalidation results showed that among these 11 foam aluminum datasets. the Gibson-Ashby formulayielded anomalous results in two cases, whereas WEP exhibited exceptional stability without anyanomalies. In comparison to the Gibson-Ashby formula, WEP demonstrated an 18.18% improvement inevaluation accuracy.

Foam aluminum , composed of aluminum metalor its alloys , is characterized by its porous nature numerous voids, and a honeycomb-like structure. Developing precise and comprehensive performance evaluation methods is urgently required to enhance theutilization of foam aluminum.

The Gibson-Ashby formulal and the Hanssen formulat , introduced by Gibson and Ashby , hold significance as traditional equations for describing the mechanical characteristics materials of porous Nevertheless ,foam aluminum displays diverse microstructures and is affected by various structural factors, such as porosity ,pore size , and pore distribution. The majority of these conventional constitutive relationships are macroscopic phenomenological models that do not account for theinfluence of pore structures. Furthermore, foam aluminum may have distinct performance requirementsin different application scenarios.Consequently, evaluation methods must be tailored to meet specific application requirements. Additionally , the foam aluminum field lacks standardized testing methods and performance parameters , leading to challenges inand validating findings across different comparingstudies. In summary ,traditional methods fail toestablish a correlation between the microstructural attributes of foam aluminum and its mechanical characteristics or to precisely model and forecast foam aluminum’s mechanical properties within parameter space. Relying solely on a macroscopic perspective for evaluating foam aluminum’s mechanical properties no longer meets the contemporary performance designdemands. The establishment of a mapping relationship between foam aluminum’s structural parameters and its mechanical characteristics is a pressing and pivotal matter.

 In this study , a novel characterization parameter called equivalent porosity was proposed torepresent the relationship between the microstructural features and performance of foam aluminum. This parameter was derived from the weighted distribution of features such as porosity , average pore size, andcircularity of foam aluminum.Through experimental verification on foam aluminum samples , it has been demonstrated that the equivalent porosity , as a new characterization parameter, provides a more accurate reflection of foam aluminum’s performance.

In this study , we adopt an interdisciplinary approach by combining materials science with machine learning. Machine learning techniques , suchas feature selection, feature weight analysis , and residual weight allocation, are integrated to establish aformula for equivalent porosity. We have demonstrated the accuracy of equivalent porosity as a new characterization parameter through the validationof five sample sets , providing a precise reflection of foam aluminum’s performance. We provide a moreprecise method for evaluating and analyzing the material’s mechanical properties , offering valuable references for decision-making and analysis. The research closely aligns with the rapid advancements in big data and artificial intelligence technologies by merging data-driven materials research with the study of foam aluminum. We introduce novel patterns and methods, offering a fresh perspective on the characterization and analysis of foam aluminum’s performance.