Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

Abstract

We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies.

Characteristics of the Evolution of Carbide Morphology in the Haynes® 230® Alloy as a Result of High Temperature Annealing

“…Recently developed methods for image recognition and classification based on machine learning allow the stage of parameterization of visual characteristics to be avoided. A set of morphological patterns identified for each image can be described in the natural language and used for microstructure feature extraction by CNN procedures (Ma et al 2020;Azimi et al 2018;Chun et al 2020).…”

“…Recently, many procedures of identification and classifications of microstructure images based on the artificial intelligence and machine learning concepts have been developed (DeCost and Holm 2015;Ma et al 2020;Azimi et al 2018;Chun et al 2020). The published results are mainly focused on extracting the features of the microstructure model.…”

Predicting material microstructure evolution via data-driven machine learning

“…The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including material property prediction, 1 mining (micro)structure-property and (micro)structure-processing relationships, 2 , 3 , 4 and characterization of material microstructures. 5 , 6 Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation).…”

Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks

“… 32 , 33 , 34 , 35 They have seen diverse applications ranging from the discovery of new materials 36 , 37 , 38 , 39 , 40 to the predictions of materials’ properties, 41 , 42 , 43 , 44 , 45 the development of accurate and efficient potentials for atomistic simulations, 46 , 47 , 48 , 49 microscopic and spectroscopic data analysis and processing, 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 and effective inference of a material’s properties from a limited experimental dataset. 63 , 64 A large number of these works are devoted to material microstructure, with encouraging results, including microstructure classification and quantification, 50 , 51 , 52 , 53 , 54 , 65 , 66 , 67 image segmentation, 55 , 56 predictions of microstructure-property relations, 57 , 68 , 69 , 70 mapping processing-microstructure relations, 71 , 72 , 73 , 74 microstructure optimization, 75 , 76 , 77 and equilibrium configuration prediction. 78 Datasets in these works are mainly in the form of static microstructure images.…”