Comparative Study on Different CNN Architectures Developed on Microstructural Classification in Al-Si Alloys

Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don’t always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes.