Simultaneous Material Microstructure Classification and Discovery using Acoustic Emission Signals
Description
Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, the aim is to analyze the AE signals gathered during a scanning probe lithography process to classify the known microstructure types and discover unknown surface microstructures/anomalies. To achieve this, a Hidden Markov Model is developed to consider the temporal dependency of the high-resolution AE data. Furthermore, the posterior classification probability and the negative likelihood score for microstructure classification and discovery are computed. Subsequently, a diagnostic procedure to identify the dominant AE frequencies that were used to track the microstructural characteristics is presented. In addition, machine learning methods such as KNN, Naive Bayes, and Logistic Regression classifiers are applied. Finally, the proposed approach applied to identify the surface microstructures of additively manufactured Ti-6Al-4V and show that it not only achieved a high classification accuracy (e.g., more than 90\%) but also correctly identified the microstructural anomalies that may be subjected to further investigation to discover new material phases/properties.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2020
Agent
- Author (aut): Sun, Huifeng
- Thesis advisor (ths): Yan, Hao
- Thesis advisor (ths): Fricks, John
- Committee member: Cheng, Dan
- Publisher (pbl): Arizona State University