Training Tricks for Steel Microstructure Segmentation with Deep Learning

Abstract

Data augmentation and other training techniques have improved the performance of deep learning segmentation methods for steel materials. However, these methods often depend on the dataset and do not provide general principles for segmenting different microstructural morphologies. In this work, we collected 64 granular carbide images (2048 × 1536 pixels) and 26 blocky ferrite images (2560 × 1756 pixels). We used five carbide images and two ferrite images and derived from them the test set to investigate the influence of frequently used training techniques on model segmentation accuracy. We propose a novel method for quickly building models that achieve the highest segmentation accuracy for a given dataset through combining multiple training techniques that enhance the segmentation quality. This method leads to a 1–2.5% increase in mIoU values. We applied the optimal models to the quantization of carbides. The results show that the optimal models achieve the smallest errors of 5.39 nm for the mean radius and 29 for the total number of carbides on the test set. The segmentation results are also more reasonable than those of traditional segmentation methods.

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

“…As is well known, in recent years, machine learning technology has developed rapidly and has been widely applied in the field of materials research, including alloy design [32,33], microstructure recognition [34], performance prediction [35], and process optimization [36], as well as the applications mentioned earlier in material hot deformation behavior and constitutive modeling. Wang et al [37] used a machine learning algorithm based on singular value decomposition and deep neural networks to build metamodels for constitutive models, which not only assists in parameter fitting but also facilitates the understanding and analysis of constitutive models.…”