| Related citation: | Wenhao Da,Lucai Wang,Yanli Wang,Xiaohong You,Wenzhan Huang,Fang Wang.Proposal of Equivalent Porosity Indicator for Foam Aluminum Based on GRNN[J].Journal of Harbin Institute Of Technology(New Series),2024,31(5):16-31.DOI:10.11916/j.issn.1005-9113.2023100. |
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| Author Name | Affiliation | | Wenhao Da | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China | | Lucai Wang | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China | | Yanli Wang | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China | | Xiaohong You | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China | | Wenzhan Huang | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China | | Fang Wang | School of Material Science and Technology, Taiyuan University of Science andTechnology, Taiyuan 030024, China |
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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: |
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| Descriptions in Chinese: |
| 广义回归神经网络的泡沫铝当量孔隙率公式的提出 达文豪，王录才，王艳丽，游晓红，黄闻战，王芳 （太原科技大学 材料科学与工程学院，太原 030024） 中文说明：为更全面了解和评估泡沫铝的性能，引入了各种特征指标。然而，目前对这些指标在分析泡沫铝性能方面的理解有限。本研究采用广义回归神经网络（GRNN）方法建立一个模型，将泡沫铝的微观结构特征数据与其力学性能联系起来。通过GRNN模型，从泡沫铝的13个孔特征中提取了4个最关键的特征及其相应的权重值。随后，通过对特征权重分配的残余权重，开发一种新的特征表示公式，名为“王氏等效孔隙度”（WEP），并获得了4个参数系数。该公式旨在表示泡沫铝微观结构特征与其力学性能之间的关系。此外，本文使用来自11组泡沫铝的压缩数据进行模型验证。验证结果显示，在11组泡沫铝数据中，Gibson-Ashby公式在两种情况下产生异常结果，而WEP表现出异常稳定性。与Gibson-Ashby公式相比，WEP的评价准确性提高了18.18%。 关键词：泡沫铝, 特征选择,重要性分析, 特征学习 |
