Start Date
19-5-2023 3:00 PM
End Date
19-5-2023 3:15 PM
Document Type
Short Paper
Keywords
Structural Health Monitoring, Additive Manufacturing, Convolutional Neural Network, Long Short-Term Memory
Description
An active Structural Health Monitoring (SHM) method called Surface Response to Excitation (SuRE), is used in this study to detect and quantify the damages created by a milling operation on additively manufactured metal plates. This method entails bonding two piezoelectric disks to the test specimens, one to excite it with surface waves from one end of the plate, and the other to sense the dynamic response to excitation at the other end. A sweep sine wave with a duration of 1 ms, ranging from 50-120 kHz is used as the excitation signal. Five stainless steel plates of identical size (195×54×2.5 mm) were created using a Markforged metal 3D printer. The data for four different conditions were recorded, which are, when the parts were undamaged and when they were face milled at 3 different lengths. The data was then used to train One-Dimensional and Two-Dimensional Convolutional Neural Networks (CNN) and also a Long Short-Term Memory (LSTM) neural network. The continuous wavelet transform (CWT) was used to convert the collected sensor data from the time domain to time-frequency representation images to be classified by the 2D CNN. The 1D CNN, 2D CNN and the LSTM classified the damage length with an overall accuracy of 98.9%, 100%, and 97.8% respectfully.
DOI
https://doi.org/10.5038/ZPYX4570
Included in
Acoustics, Dynamics, and Controls Commons, Computer-Aided Engineering and Design Commons, Electro-Mechanical Systems Commons
Damage identification in 3D printed metal parts using deep learning
An active Structural Health Monitoring (SHM) method called Surface Response to Excitation (SuRE), is used in this study to detect and quantify the damages created by a milling operation on additively manufactured metal plates. This method entails bonding two piezoelectric disks to the test specimens, one to excite it with surface waves from one end of the plate, and the other to sense the dynamic response to excitation at the other end. A sweep sine wave with a duration of 1 ms, ranging from 50-120 kHz is used as the excitation signal. Five stainless steel plates of identical size (195×54×2.5 mm) were created using a Markforged metal 3D printer. The data for four different conditions were recorded, which are, when the parts were undamaged and when they were face milled at 3 different lengths. The data was then used to train One-Dimensional and Two-Dimensional Convolutional Neural Networks (CNN) and also a Long Short-Term Memory (LSTM) neural network. The continuous wavelet transform (CWT) was used to convert the collected sensor data from the time domain to time-frequency representation images to be classified by the 2D CNN. The 1D CNN, 2D CNN and the LSTM classified the damage length with an overall accuracy of 98.9%, 100%, and 97.8% respectfully.