Identification of Microstructures in 3-D–Printed Ti-6Al-4V Using Acoustic Emission Cepstrum

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.

Smart defect identification for manufacturing applications

“…Confocal microscopy was first described by Minski in 1957 [11,12], and has become a more and more powerful tool for surface characterization. This technique works similar to the conventional microscope where illumination from a source is focused onto a small point instead of the whole specimen surface and scanned under computer control.…”

“…However, this form of quality inspection is both time inefficient and results in traces of human error [10]. In addition, some defects are only found on the microscopic level such as micro-cavities and microstructural anomalies [11,12]. For example, experimental results conducted at the University of Zilina conclude that such micro-cavity defects have a significant impact on the fatigue endurance of materials such as ductile cast iron [13].…”