Deep Transfer Learning for Ni-Based Superalloys Microstructure Recognition on γ′ Phase

Abstract

Ni-based superalloys are widely used to manufacture the critical hot-end components of aviation jet engines and various industrial gas turbines. The analysis of Ni-based superalloys microstructures is an important research task during the design and development of superalloys. The material microstructure information can only be understood by experts in the long history. Image segmentation and recognition are developing techniques for accelerating the microstructure analysis automatically. Although deep learning techniques have achieved satisfactory performance, they usually suffer from generalization, i.e., performing worse on a new dataset. In this paper, a deep transfer learning method which just needs a small number of labeled images is proposed to perform the microstructure recognition on γ′ phase. To evaluate the effectiveness, we homely prepare two Ni-based superalloys at temperatures 900 °C and 1000 °C, and manually annotate two datasets named as W-900 and W-1000. Experimental results demonstrate that the proposed method only needs 3 and 5 labeled images to achieve state-of-the-art segmentation accuracy during the transfer from W-900 to W-1000 and the transfer from W-1000 to W-900, while enjoying the advantage of fast convergence. In addition, a simple and effective software for the Ni-based superalloys microstructure recognition on γ′ phase is developed to improve the efficiency of materials experts, which will greatly facilitate the design of new Ni-base superalloys and even other multicomponent alloys.

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

“…Bostanabad et al [148] gave an overview of computational microstructure reconstruction and characterization. The majority of microstructure-related works in the field of machine learning have been focused on finding a relationship between the morphologies and properties of materials [85,149,150], the reconstruction [151,152] or detection [153] of microstructures, and microstructure classification [154].…”

“…A DTL strategy was applied to overcome the limitation of a small dataset in training the ANN predictor of the optical absorption spectrum using the Materials Agnostic Platform for Informatics and Exploration (Magpie) [187] descriptor of material composition. Li et al [152] proposed a DTL for identifying the γ phase on Ni-based superalloys datasets. They also developed software for recognizing the γ phase.…”

Correlation among Composition, Microstructure and Hardness of 7xxx Aluminum Alloy Using Original Statistical Spatial-Mapping Method

“…This process requires significant time and labor inputs; thus, the statistical results are acquired only from limited features and regions [22][23][24]. In view of the above shortcomings, in this study, image acquisition instruments were combined with computer vision methods, and a high throughput method of quantitative identification and statistical analysis of the aluminum alloy microstructure was devised based on deep learning [25,26]. After verification, this method was found to be accurate and comprehensive for the statistics of massive organizational data; please refer to our published articles, for the specific research process and results [27].…”