Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images
This work addresses the computationally expensive characterization of heterogeneous microstructures in materials science, though it appears incremental as it combines existing methods like CNNs, CEDNs, YOLOv5, and optimization techniques.
The authors developed an optimized machine learning framework to autonomously characterize pores, particles, grains, and grain boundaries in microstructural images of additively manufactured metals, achieving a substantial reduction in training time and GPU usage while maintaining high accuracy and significantly improving analysis time compared to conventional methods.
Additively manufactured metals exhibit heterogeneous microstructure which dictates their material and failure properties. Experimental microstructural characterization techniques generate a large amount of data that requires expensive computationally resources. In this work, an optimized machine learning (ML) framework is proposed to autonomously and efficiently characterize pores, particles, grains and grain boundaries (GBs) from a given microstructure image. First, using a classifier Convolutional Neural Network (CNN), defects such as pores, powder particles, or GBs were recognized from a given microstructure. Depending on the type of defect, two different processes were used. For powder particles or pores, binary segmentations were generated using an optimized Convolutional Encoder-Decoder Network (CEDN). The binary segmentations were used to used obtain particle and pore size and bounding boxes using an object detection ML network (YOLOv5). For GBs, another optimized CEDN was developed to generate RGB segmentation images, which were used to obtain grain size distribution using two regression CNNS. To optimize the RGB CEDN, the Deep Emulator Network SEarch (DENSE) method which employs the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) was implemented. The optimized RGB segmentation network showed a substantial reduction in training time and GPU usage compared to the unoptimized network, while maintaining high accuracy. Lastly, the proposed framework showed a significant improvement in analysis time when compared to conventional methods.