Deep-Learning Based Microstructure Reconstruction and Generation

Characterizing the microstructural behavior of materials is crucial for understanding their properties and performance. Traditional imaging methods, such as optical microscopy and electron microscopy, are effective but costly and time-consuming. Computational approaches can reduce costs and time while expanding the accessibility of microstructural analysis through the generation of new microstructure images. Traditional computational approaches, namely descriptor-based approaches, are slow but effective in low-data scenarios. Modern approaches use machine learning (ML), which is faster but often requires a lot of data to approach the performance of descriptor-based methods. This research leverages a special data-efficient Generative Adversarial Network (GAN) architecture to artificially generate microstructures of strain-sensing nanomaterial networks. The nanomaterial networks consist of carbon nanotubes (CNTs) that exhibit strain sensing properties. By training the GAN on experimentally obtained microscopic images, the model can replicate complex microstructures. This ML-based approach significantly reduces the time and cost of microstructural characterization, providing an efficient method to build large databases for analyzing the electrical properties of nanomaterial networks.