Evaluation of the Elastic Modulus and Plateau Stress of a 2D Porous Aluminum Alloy Based on a Convolutional Neural Network

• Published in: Metals (Basel ) Vol. 13; no. 2; p. 284
• Main Authors: Sun, Jianhang, Xu, Yepeng, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Alloying elements; Alloys; Aluminum; Aluminum alloys; Aluminum base alloys; Analysis; Artificial neural networks; convolutional neural network; Deep learning; Density; elastic modulus; Elastic properties; Energy; Energy absorption; Finite element method; forward prediction; Goodness of fit; Heat conductivity; Inverse design; Machine learning; Mechanical properties; Metals; Modulus of elasticity; Neural networks; Permeability; plateau stress; Pore size; Porosity; Porous materials; porous metal; Porous metals; Regularization; Search methods; Simulation; Stiffness; Stress-strain curves; Training; China
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met13020284

Porous metals are a new ultra-light material with high specific stiffness, specific strength, and good energy absorption properties. The elastic modulus and plateau stress of porous metals are essential parameters. There have been many studies on the effects of the matrix material, porosity, and pore size on the elastic modulus and plateau stress of porous metals, but few studies can be found on the impact of pore arrangement. The pore arrangement of porous metals cannot be quantitatively described, and the design space of a porous metal structure under the same porosity is vast. With the powerful learning and prediction ability of neural networks, the influence of pore arrangement can be better understood. In this paper, a convolutional neural network was used to explore the impact of pore arrangement on both the elastic modulus and plateau stress of a porous aluminum alloy. Firstly, a finite element method was used to simulate the compression of a porous aluminum alloy to obtain a training sample library. Secondly, a convolutional neural network was built to positively predict the elastic modulus and plateau stress of the porous aluminum alloy. Partial samples were used to select the best training model from five convolutional neural network candidates. Dropout, Batch Normalization, and L2 regularization methods were used to alleviate the over-fitting phenomenon in training. All data in the database were then trained and predicted, and the predicted goodness of fit of the elastic modulus and plateau stress were 0.8785 and 0.5922, respectively. A search method based on the convolutional neural network was then used to iteratively search the database. Under the condition of using a small amount of data, the pore structure with the best elastic modulus and plateau stress in the database could be determined, and the inverse design of a structure with high elastic modulus and plateau stress could be realized.