Abstract
Recent advances in machine learning and image recognition tools/methods are being used to address fundamental challenges in materials engineering, such as the automated extraction of statistical information from dual phase titanium alloy microstructure images to support rapid engineering decision making. Initially, this work was performed by extracting dense layer outputs from a pretrained convolutional neural network (CNN), running the high dimensional image vectors through a principal component analysis, and fitting a logistic regression model for image classification. Kfold cross validation results reported a mean validation accuracy of 83% over 19 different material pedigrees. Furthermore, it was shown that fine-tuning the pre-trained network was able to improve image classification accuracy by nearly 10% over the baseline. These image classification models were then used to determine and justify statistically equivalent representative volume elements (SERVE). Lastly, a convolutional neural network was trained and validated to make quantitative predictions from a synthetic and real, two-phase image datasets. This paper explores the application of convolutional neural networks for microstructure analysis in the context of aerospace engineering and material quality.
Highlights
Design and control of material microstructure is important in many industries, in par cular aerospace, where cri cal rota ng hardware operate under extreme condi ons for extended amounts of me
The goal of this work is to demonstrate the capacity of convolutional neural networks for microstructure image classification and property prediction in Ti-6Al-4V, a workhorse alloy in the aerospace industry
Pre-trained and fine-tuned convolutional neural network (CNN) models were utilized for image classification, RVE assessment, and continuous property prediction
Summary
Design and control of material microstructure is important in many industries, in par cular aerospace, where cri cal rota ng hardware operate under extreme condi ons for extended amounts of me. The increasing size and rate of incoming data necessitates the development and use of automated, high-throughput tools and analysis methods. Another alarming, but o en overlooked challenge is the issue of repeatability in quan ta ve analysis of microstructure data. To improve material design and engineering decision making under uncertainty, there is a strong need for objec ve, scalable methods for sta s cal representa ons of microstructure
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