Citation: Adeniji, D.; Oligee, K.;
Schoop, J. A Novel Approach for
Real-Time Quality Monitoring in
Machining of Aerospace Alloy
through Acoustic Emission Signal
Transformation for DNN. J. Manuf.
Mater. Process. 2022, 6, 18.
https://doi.org/10.3390/
jmmp6010018
Academic Editor: Steven Y. Liang
Received: 21 December 2021
Accepted: 18 January 2022
Published: 25 January 2022
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Manufacturing and
Materials Processing
Journal of
Article
A Novel Approach for Real-Time Quality Monitoring in
Machining of Aerospace Alloy through Acoustic Emission
Signal Transformation for DNN
David Adeniji
1,2
, Kyle Oligee
1
and Julius Schoop
1,2,
*
1
Department of Mechanical Engineering, University of Kentucky, Lexington, KY 40506, USA;
doad224@uky.edu (D.A.); klol223@g.uky.edu (K.O.)
2
Institute for Sustainable Manufacturing, University of Kentucky, Lexington, KY 40506, USA
* Correspondence: julius.schoop@uky.edu; Tel.: +1-859-323-8308; Fax: +1-859-257-1071
Abstract:
Gamma titanium aluminide (
γ
-TiAl) is considered a high-performance, low-density re-
placement for nickel-based superalloys in the aerospace industry due to its high specific strength,
which is retained at temperatures above 800
◦
C. However, low damage tolerance, i.e., brittle material
behavior with a propensity to rapid crack propagation, has limited the application of
γ
-TiAl. Any
cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of
γ
-TiAl
components, making the workpiece surface’s quality from finish machining a critical component to
product quality and performance. To address this issue and enable more widespread use of
γ
-TiAl,
this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring tech-
nique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN).
Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature
extraction and classification, which face challenges such as the extraction of good/relevant features
and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a
convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional
image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying
the AE signature based on pedigreed experimental data and finally predicting the process-induced
surface quality. The results of the present work show good classification accuracy of 80.83% using
scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing
the significant potential for real-time quality monitoring in manufacturing processes.
Keywords: aerospace; manufacturing; titanium aluminide; surface integrity; NDE
1. Introduction
Intermetallic titanium aluminide alloys such as TiAl, Ti
3
Al, Al
3
Ti, and Ti
2
AlNb are
currently gaining ground in the aerospace, biomedical, and automotive industry, due
to their low density, high strength, and suitability for high-temperature applications.
Over the years, TiAl has been grouped under three categories, namely alpha-2 (
α
2
-Ti
3
Al),
gamma (
γ
-TiAl), and alpha-2/gamma (
α
2
/
γ
) phases. Among these, gamma titanium
aluminide (
γ
-TiAl) features unique physical and mechanical properties: high melting
point, low density, high strength, resistance to oxidation, and corrosion. Compared to
conventional titanium, steel, and nickel-based (super)alloys, the low density offered by
γ
-TiAl provides improved specific strength in high-temperature performance. To date,
there have been three commercially developed generations of TiAl alloys [
1
]. In the first
and second generations of Ti (42–48%)Al, elements such as Cr, V, and Mn were added
to produce ternary alloys, which were further heat-treated to improve the ductility. The
addition of elements such as Ta, Mo, and W enhances the oxidation and creep properties
at high temperatures. The third and fourth generations of TiAl alloys have high Mo and
Nb content. The fourth generation of TiAl are often referred to as TNM alloys and possess
J. Manuf. Mater. Process. 2022, 6, 18. https://doi.org/10.3390/jmmp6010018 https://www.mdpi.com/journal/jmmp
