Identification of microstructures in 3-D-printed Ti-6Al-4V using acoustic emission cepstrum

Tapan Ganatma Nakkina, Ashif Sikandar Iquebal, Rama Krishna Sai S. Gorthi, Satish Bukkapatnam

Research output: Contribution to journalArticlepeer-review

Abstract

Recent advances in smart hybrid machine tools allow the manufacturing of components with materials discovered on demand from certain common material precursors. Imperative to on-demand material discovery is the ability to probe and characterize the microstructure and salient properties of the materials as they are created. The article focuses on harnessing the complex spectral characteristics of high-resolution acoustic emission (AE) sensor signal generated during a nanoindentation-based scanning probe lithography process to classify the different surface microstructure types of additively manufactured Ti-6Al-4V components. We demonstrate that the low-frequency mel frequency cepstral coefficients (MFCCs) provide highly informative signatures of the AE processes to make inferences about the microstructures. We also show that unlike the well-known time-frequency features of AE, including those gathered via spectrograms, the MFCC compactly capture the variation of the energies of different frequency bands and enable classification of different microstructure types with as simple classifier as logistic regression. Via extensive nanoindentation experiments and analysis of the AE signals, we identify the specific MFCCs that are most important for discriminating between two different microstructure types of Ti-6Al-4V with accuracies estimated via extensive cross-validation close to 100 %. The proposed approach of using MFCCs offers a fast and efficient way of identifying different microstructure types of a given material system compared with conventional approaches, such as X-ray diffraction and scanning electron microscopy.

Original languageEnglish (US)
JournalSmart and Sustainable Manufacturing Systems
Volume4
Issue number2
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Acoustic emission signal
  • Classification
  • Convolutional neural network
  • Lithography
  • Logistic regression
  • Mel frequency cepstral coefficient features
  • Microstructures
  • Nanoindentation
  • Spectrogram
  • Ti-6Al-4V

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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