Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

• Published in: Materials & design 2022-11, Vol.223, p.111236, Article 111236
• Main Authors: Arumugam, Dharanidharan, Kiran, Ravi
• Format: Article
• Language: English
• Subjects: ASTM A992; Convolutional autoencoder; Interpretable AI; Pixel relevance; Saliency maps
• ISSN: 0264-1275; 1873-4197
• DOI: 10.1016/j.matdes.2022.111236

[Display omitted] •Dual-phase steel microstructural images are compactly represented with a compression ratio of 32.•The trained decoder network of the configured convolutional autoencoder facilitates the secure sharing of microstructural data.•Saliency maps generated in the study highlights the important microstructural regions for compact representation.•Grey pearlite regions of dual phase microstructure are highly important for compact representation. In this study, we propose a complex-step convolutional autoencoder to identify the regions that are important in a metal microstructure for compact representation and secure sharing. Firstly, the architecture of a convolutional autoencoder is designed for the compact representation of microstructural images. The designed autoencoder achieved a high image compression ratio of 32 without loss of important information. Secondly, an in-home developed model agnostic sensitivity analysis using complex step derivative approximation is implemented on convolutional autoencoders to identify regions of the microstructure that are important for reconstruction. Finally, saliency maps that highlight the importance of pixels for reconstruction are generated for three grades of dual-phase structural steels. The saliency maps indicated secondary phase regions and grain boundaries are important for microstructure image reconstruction. The proposed approach produces more tenable saliency explanations compared to guided backpropagation and layer wise relevance propagation methods. The decoder part of the convolutional autoencoder can be used as a key that could be used to reconstruct the actual microstructure from encoded image information contributing to secure and efficient sharing of microstructure data. The proposed framework is generic and can be extended to identify important microstructural regions for other metals, composites, biomaterials, and material systems.