Simultaneous material microstructure classification and discovery via hidden Markov modeling of acoustic emission signals

Xinyu Zhao, Ashif Iquebal, Huifeng Sun, Hao Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, we aim 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, we developed a Hidden Markov Model to consider the temporal dependency of the high-resolution AE data. Furthermore, we compute the posterior classification probability and the negative likelihood score for microstructure classification and discovery. Subsequently, we present a diagnostic procedure to identify the dominant AE frequencies that allow us to track the microstructural characteristics. Finally, we apply the proposed approach 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 further investigation to discover new material phases/properties.

Original languageEnglish (US)
Title of host publicationManufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791884263
DOIs
StatePublished - 2020
EventASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020 - Virtual, Online
Duration: Sep 3 2020 → …

Publication series

NameASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020
Volume2

Conference

ConferenceASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020
CityVirtual, Online
Period9/3/20 → …

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Materials Science (miscellaneous)
  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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