# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 5 Conclusions  
In this study, an algorithm called denoising autoencoder (DAE) is developed to extract the flaw signal from the mixed A-scan signal obtained by ultrasonic testing (UT). Background signals have always been an obstacle to the evaluation of UT signals, especially for unskilled inspectors. Although many attempts have been made to extract flaw signals or denoise background signals using various techniques, these attempts have remained largely unsuccessful. Most attempts to combine UT with artificial neural networks (ANN) have focused on detecting flaws rather than extracting flaw signals from A-scan signals obtained by UT. Although autoencoder has been used in previous research to denoise 2-D image datasets or 1-D signal datasets, it was not particularly focused on the extraction of flaw signals. Therefore, in this study, we propose a newly developed derivative of the DAE that can be used to extract the target flaw signal from mixed A-scan signals. For this research, a total of 45 weldment standard blocks, in which 5 types of flaws were embedded, were used. The 5 types of flaws used in this study are cracks, lack of fusion, slag inclusions, porosity, and incomplete penetration. The total number of A-scan signals used in the training of the DAE is 2463. For the training of the DAE, we zero-padded the acquired A-scan signals and manually removed the background signals. The zero-padded data are used as validation data in the ordinary DAE process so that they can be compared to the output and update the weights, thereby reducing the loss. After training, most of the background signals from the original mixed A-scan signals disappeared, and the flaw signals were properly extracted from the mixed A-scan signals. The MSE loss of the model started at 0.0019 but ended at 0.000508, which shows that the DAE used in this study worked well. A point-by-point comparison method was used to evaluate the performance of the network. For training data, there was an average difference of 0.02764, and for testing data, there was an average difference of 0.03282. The result of the point-by-point comparison also shows that the network worked well. However, there remained some problems to be solved. First, the amplitude of the extracted flaw signals was reduced. Second, some parts of the mixed signal were not denoised well. Lastly, some flaw signals of the signals with low amplitude were not extracted as expected. The causes of these errors came from the shortage of data used in the training. The DAE was trained using A-scan data acquired from the peak amplitude, which caused a shortage of low amplitude data and became the reason for the error. Another reason could be the pattern of signals. The characteristic of the dataset could mislead the network to denoise every smaller signal. Lastly, the dataset was not regularized. The flaw signals with low amplitude could be regularized, and by regularizing signals with the amplitude of flaw signals, the DAE would be able to learn to treat the flaw signals with low amplitude. The overall result of this study suggests that the proposed DAE is practical for extracting flaw signals from A-scan signals obtained by UT. Although some revisions should be made for the actual usage of the proposed model in the field, they can be completed in a short period of time. Through this model, experts who practice UT will be able to work more effectively and efficiently. The proposed DAE in this study would be a great solution for experts in the nondestructive evaluation (NDE) field, and the implementation of the proposed DAE in the NDE field is expected soon. **Author Contributions:** Conceptualization, S.-J.S. and S.-E.L.; methodology, H.-J.K. and S.-E.L.; software, H.-J.K., S.-E.L., and J.P.; validation, S.-J.S., S.-E.L., J.P., and H.-J.K.; formal analysis, S.-E.L.; investigation, S.-E.L.; resources, S.-E.L. and J.P.; data curation, S.-E.L.; writing--original draft preparation, S.-E.L.; writing--review and editing, S.-E.L., J.P., and H.-J.K.; visualization, S.-E.L.; supervision, S.-J.S.