## 2 Previous Works
### Post-Process Welding Quality Control  
The quality of the weld beads can be verified after the welding process is finished. In this approach, information about the surface of weld beads is extracted by utilizing devices such as profile sensors and cameras. Using machine-learning techniques, a model is designed and trained to detect irregularities and defects on the weld surface by creating a labeled dataset from the acquired raw data. [PERSON] et al., 2019 [7] presented a passive vision-based system to detect irregularities in the weld bead texture. The Principal Component Analysis (PCA) technique was deployed to acquire the main characteristics of each class while reducing the dimension of data. Then, a Support Vector Machine (SVM) model was trained based on the class characteristics to assess the weld surface quality. [PERSON] et al., 2020 [8] utilized a structured light sensor to measure the geometry of the weld bead. The laser stripe was projected onto the weld bead surface, and a camera captured the laser stripe through a narrow-band filter to decrease the environmental noises. Through image preprocessing and baseline extraction, a laser strip profile was extracted from the raw image data. Furthermore, the weld profile was classified as the filling weld or the capping weld using a threshold-based method. By extractingfeatures points from the stripe profile, the weld bead size could be measured. Additionally, the bead shape data was compared with acceptable thresholds to detect geometrical defects. [PERSON] & [PERSON], 2017 [9] developed a passive machine vision-based framework to detect the undercut on shell-tube welding. By applying preprocessing algorithms, the quality of the image was enhanced, and the edges in the picture were extracted. Median [10] filter and Otsu segmentation [11] were utilized to segment the metal tube, and morphological operations were employed to improve the segmentation integrity. Finally, the undercut was detected by counting white pixels in each angle inside the tube. [PERSON] & [PERSON], 2016 [12] presented an automated passive quality control system that used structured light to measure bead size and detect corresponding defects such as undercut and plate displacement. The laser centerline was determined in the image by applying preprocessing techniques such as Median filter, thresholding, and morphological operations. Then, feature points were extracted by fitting lines to the laser stripe reflecting from parent metal using the RANSAC technique. Afterward, a sliding window was applied to determine the extremums of the laser profile. Once feature points were calculated, the quality of welding could be controlled by comparing them with thresholds. [PERSON] et al., 2019 [13] proposed a vision-based defect-detection system to inspect the welding quality of thin-walled metal canisters. They developed a modified Gaussian mixture model to extract defect feature areas from gray images. An area thresholding technique was applied to filter out false detections, which mainly consisted of small areas. Finally, using the feature image as the input, three main types of defects were classified by utilizing brightness thresholding and curve detection algorithms. [PERSON] et al., 2019 [14] developed a deep-learning framework to monitor the quality of friction stir welds. They utilized the one-stage object detector YOLOv2 [15] to detect and localize the weld seam and compared several convolutional neural networks such as DenseNet-121 [16] to classify the possible defects. They created 112 stir-weld samples that were used in the dataset to train and test the framework. The proposed framework input were RGB and topology images. [PERSON] et al., 2020 [17] designed a vision-based system that used the convolutional neural network to detect the misalignment of the metal parts before welding. In addition, the developed system could identify geometrical nonconformities of weld beads after the welding process was completed using laser triangulation and image processing techniques.