scholarly journals Semi-Supervised Training for Positioning of Welding Seams

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7309
Author(s):  
Wenbin Zhang ◽  
Jochen Lang
Keyword(s):  
Supervised Learning ◽  
Deep Neural Networks ◽  
Image Resolution ◽  
Learning Approaches ◽  
Welding Robot ◽  
Robotic Welding ◽  
Welding Seam ◽  
Classification Tasks ◽  
Point Detection ◽  
Imaging Conditions

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

scholarly journals Storage Fit Learning with Unlabeled Data

2017 ◽  
Author(s):  
Bo-Jian Hou ◽  
Lijun Zhang ◽  
Zhi-Hua Zhou
Keyword(s):  
Supervised Learning ◽  
Unlabeled Data ◽  
Low Rank ◽  
Learning Approaches ◽  
Similarity Matrix ◽  
Matrix Approximation ◽  
Rank Matrix ◽  
Classification Tasks ◽  
Real Time Applications ◽  
Low Rank Matrix

By using abundant unlabeled data, semi-supervised learning approaches have been found very useful in various tasks. Existing approaches, however, neglect the fact that the storage available for the learning process is different under different situations, and thus, the learning approaches should be flexible subject to the storage budget limit. In this paper, we focus on graph-based semi-supervised learning and propose two storage fit learning approaches which can adjust their behaviors to different storage budgets. Specifically, we utilize techniques of low-rank matrix approximation to find a low-rank approximator of the similarity matrix so as to reduce the space complexity. The first approach is based on stochastic optimization, which is an iterative approach that converges to the optimal low-rank approximator globally. The second approach is based on Nystrom method, which can find a good low-rank approximator efficiently and is suitable for real-time applications. Experiments on classification tasks show that the proposed methods can fit dynamically different storage budgets and obtain good performances in different scenarios.

scholarly journals A Novel Seam Tracking Technique with a Four-Step Method and Experimental Investigation of Robotic Welding Oriented to Complex Welding Seam

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3067
Author(s):  
Gong Zhang ◽  
Yuhang Zhang ◽  
Shuaihua Tuo ◽  
Zhicheng Hou ◽  
Wenlin Yang ◽  
...  
Keyword(s):  
Seam Tracking ◽  
Laser Sensor ◽  
Feature Points ◽  
Welding Robot ◽  
Robotic Welding ◽  
Step Method ◽  
Welding Seam ◽  
Precise Positioning ◽  
Tracking Technique ◽  
Joint Width

The seam tracking operation is essential for extracting welding seam characteristics which can instruct the motion of a welding robot along the welding seam path. The chief tasks for seam tracking would be divided into three partitions. First, starting and ending points detection, then, weld edge detection, followed by joint width measurement, and, lastly, welding path position determination with respect to welding robot co-ordinate frame. A novel seam tracking technique with a four-step method is introduced. A laser sensor is used to scan grooves to obtain profile data, and the data are processed by a filtering algorithm to smooth the noise. The second derivative algorithm is proposed to initially position the feature points, and then linear fitting is performed to achieve precise positioning. The groove data are transformed into the robot’s welding path through sensor pose calibration, which could realize real-time seam tracking. Experimental demonstration was carried out to verify the tracking effect of both straight and curved welding seams. Results show that the average deviations in the X direction are about 0.628 mm and 0.736 mm during the initial positioning of feature points. After precise positioning, the average deviations are reduced to 0.387 mm and 0.429 mm. These promising results show that the tracking errors are decreased by up to 38.38% and 41.71%, respectively. Moreover, the average deviations in both X and Z direction of both straight and curved welding seams are no more than 0.5 mm, after precise positioning. Therefore, the proposed seam tracking method with four steps is feasible and effective, and provides a reference for future seam tracking research.

scholarly journals Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 329
Author(s):  
Venkataramana Veeramsetty ◽  
Bhavana Reddy Edudodla ◽  
Surender Reddy Salkuti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Harmonic Distortion ◽  
Sinusoidal Signal ◽  
Zero Crossing ◽  
Complete Study ◽  
Consistent Performance ◽  
Point Detection ◽  
Crossing Point

Zero-crossing point detection is necessary to establish a consistent performance in various power system applications, such as grid synchronization, power conversion and switch-gear protection. In this paper, zero-crossing points of a sinusoidal signal are detected using deep neural networks. In order to train and evaluate the deep neural network model, new datasets for sinusoidal signals having noise levels from 5% to 50% and harmonic distortion from 10% to 50% are developed. This complete study is implemented in Google Colab using deep learning framework Keras. Results shows that the proposed deep learning model is able to detect zero-crossing points in a distorted sinusoidal signal with good accuracy.

scholarly journals Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

2019 ◽  
Vol 28 (6) ◽  
pp. 1177-1183
Author(s):  
Pengyuan Zhang ◽  
Hangting Chen ◽  
Haichuan Bai ◽  
Qingsheng Yuan
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Scene Classification ◽  
Scattering Spectra ◽  
Classification Tasks
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