Damage Progress Classification in AlSi10Mg SLM Specimens by Convolutional Neural Network and k-Fold Cross Validation

In this study, the damage evolution stages in testing AlSi10Mg specimens manufactured using Selective Laser Melting (SLM) process are identified using Acoustic Emission (AE) technique and Convolutional Neural Network (CNN). AE signals generated during the testing of AlSi10Mg specimens are recorded and analysed to identify their time-frequency features in three different damage evolution stages: elastic stage, plastic stage, and fracture stage. Continuous Wavelet Transform (CWT) spectrograms are used for the processing of the AE signals. The AE signals from each of these stages are then used for training a CNN based on SqueezeNet. Moreover, k-fold cross validation is implemented while training the modified SqueezeNet to improve the classification efficiency of the network. The trained network shows promising results in classifying the AE signals from different damage evolution stages.

Automated summary

In this study, the stages of damage evolution in AlSi10Mg specimens were successfully identified using Acoustic Emission (AE) technique and Convolutional Neural Network (CNN). Continuous Wavelet Transform (CWT) spectrograms were used for processing the AE signals, and a modified SqueezeNet-based CNN was trained with k-fold cross validation to improve classification efficiency. The trained network showed promising results in classifying AE signals from different damage evolution stages.