Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning

Abstract

Computer image analysis is a well-known method in material science, mechanical engineering and other branches of science and engineering. Application of the machine learning method in image processing delivers promising results, especially in images with a high level of noise and low contrast. Employing automatic classification, predictive models, simulation models deliver a huge benefit in research. The former model of science-based mainly on experiments slowly passing away. It is still an important part of research but, using advanced computer methods save time and allow significantly reduces the cost of studies. It begins new branch of material science and new research challenges, interdisciplinary filed of materials informatics, incorporating scientists exploring, mathematics, informatics and material science.

Enhancing Materials Science through Computer Image Analysis and IQA Approaches

“…These algorithms create a mathematical model based on collected data, called a "training dataset", which produces predictive or decision-making results without directly programming each step of data processing (as typical for convection algorithms). Comparing the application of ML with classical methods, the working model relates a several steps: collection of training data, characteristic feature extraction, development of model and test data [14].…”