A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

• Published in: Journal of Manufacturing and Materials Processing 2022-02, Vol.6 (1), p.18
• Main Authors: Adeniji, David, Oligee, Kyle, Schoop, Julius
• Format: Article
• Language: English
• Subjects: Acoustic emission testing; Acoustics; Aeronautics; aerospace; Aerospace industry; Artificial neural networks; Brittle materials; Composite materials; Crack propagation; Critical components; Damage tolerance; Deformation; Ductility; Emission analysis; Fatigue cracks; Feature extraction; Gas turbine engines; Heat; Image classification; Intermetallic compounds; Machining; Manufacturing; MATERIALS SCIENCE; Monitoring; NDE; Neural networks; Nickel base alloys; Noise; Nondestructive testing; Oxidation; Pattern recognition; Precipitation hardening; Propagation; Real time; Sensors; Signal analysis; Signal processing; Superalloys; surface integrity; Surface properties; Temperature; Titanium alloys; titanium aluminide; Titanium aluminides; Titanium base alloys; Wavelet transforms
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/jmmp6010018

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement 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 technique 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.