Accurate prediction of topology of composite plates via machine learning and propagation of elastic waves

Composites are artificial or natural materials that exhibit extraordinary material properties over homogenous materials made of one of their constituents. Detecting the microstructure of unknown solid composites is still challenging for engineers due to the complexity and nonlinear dynamic response of the materials. The emerging of machine learning (ML) in solving complex problems has enabled scientists to find solutions for problems that were impossible before. In this article, we proposed a methodology using ML and propagation of elastic waves to accurately determine the topology of binary composite plates. Results of this study indicate that a short period of flexural elastic waves propagated through composites can efficiently collect the information of the microstructures of materials and multiple independent RF models can be leveraged to predict configurations of composites accurately by learning from the topologies of plates and their corresponding output elastic waves. The average prediction accuracy of the ML model on 8×8 composite plates can reach 96% using 100,000 samples which only occupy 1/2×1014 of all the possible combinations. This approach is the first attempt on this topic and could be modified for applications such as composite characterization, geology, and archaeology.