Weld Quality Inspection Research Articles

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments.

Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection.

Research on Weld Seam Marking Based on Machine Vision Technology

The rapid development of machine vision technology has made it widely used in various industrial fields. The detection of welding quality can also be achieved through this technology. The core of welding quality inspection is to realize the marking of weld features. To solve the problem of weld feature marking, the GUI weld marking interface is built based on MATLAB. After the process of image acquisition, image preprocessing, edge detection and feature extraction, the weld marking experiment of is finally completed. Based on data analysis of the experiment, it can be concluded that Canny edge detection operator has the best marking effect and the most stable detection for the research object used.

Digital twin-based tig welding quality prediction using electrode tip angle degradation influencing Industry 5.0 in manufacturing sector

Automation in tungsten inert gas (TIG) welding is important to achieve high production rates and quality in manufacturing industries. To improve the welding process and quality inspection methodologies, the intelligent welding robot and vision-based inspection system have been researched and deployed in many engineering fields. Hence to enhance the performance and production, a digital twin-based welding system with the prediction of weld quality based on the consideration of electrode tip angle degradation. The proposed system will capture real-time electrode tip angle and weld pool temperature using a forward looking infrared (FLIR) camera along with welding current and speed correlated with tensile strength as the output parameter. To validate the analysis, support vector machine (SVM) and random forest (RF) algorithms were implemented in which the RF model performs well on the prediction of welding quality by mapping with tensile strength. RF model confirms maximum accuracy of 90% with 0.29 seconds computation time to perform prediction on the next execution of welding operation. It is inferred that if the tip angle degradation increases consecutively welding current decreases drastically impacting the weld quality from good to poor. To forecast the need for immediate or scheduled maintenance to reduce the tip angle degradation, a linear regression algorithm is implemented to enable the inspection engineer to perform maintenance without delay in production.

Weld beads and defects automatic identification, localization, and size calculation based on a lightweight fully convolutional neural network

Weld quality inspection is a key aspect of modern manufacturing. Automatic identification, location, and dimensioning of welds and defects are necessary for complex industrial environments. Weld and defects dimensional data are important indicators for high precision assessment of weld quality, while precise positioning of welds and defects can provide important location data for subsequent processing. In this study, a novel weld and defects detection, location, and size calculation method is proposed based on deep fully convolutional neural networks. Firstly, 43 high-precision high-power laser weld plate scanned images are automatically cropped to 800 pixels × 800 pixels by the proposed sliding window sampling method for training, testing, and validation, while an image data enhancement method is proposed to quadruple the amount of data to improve the model performance. Next, the OU-net image semantic segmentation model is established to accurately identify different detection targets, which can achieve MIoU and MPA values of 78.15% and 85.18% on the validation set after sufficient training, respectively. Then, the proposed cropped segmentation image fusion recovery method is used to obtain the complete weld segmentation image for a complete display of the different inspection targets on the weld plate. Finally, a detection targets localization and size calculation method is applied to accurately obtain the position data and size data of different targets. The validation results show that the proposed detection strategy can locate and measure every weld seam, collapse, overlap, spatter, and hump on the weld plate.

Deep Learning-Empowered Digital Twin Using Acoustic Signal for Welding Quality Inspection

Weld site inspection is a research area of interest in the manufacturing industry. In this study, a digital twin system for welding robots to examine various weld flaws that might happen during welding using the acoustics of the weld site is presented. Additionally, a wavelet filtering technique is implemented to remove the acoustic signal originating from machine noise. Then, an SeCNN-LSTM model is applied to recognize and categorize weld acoustic signals according to the traits of strong acoustic signal time sequences. The model verification accuracy was found to be 91%. In addition, using numerous indicators, the model was compared with seven other models, namely, CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. A deep learning model, and acoustic signal filtering and preprocessing techniques are integrated into the proposed digital twin system. The goal of this work was to propose a systematic on-site weld flaw detection approach encompassing data processing, system modeling, and identification methods. In addition, our proposed method could serve as a resource for pertinent research.

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

Weld shape and size generally determine the quality of gas metal arc welding. Auto parts manufacturers prescribe the size and shape of the weld because they can indicate the mechanical properties of the weld. It is impossible to evaluate the quality of all welds through destruction inspection. Therefore, research on welding quality inspection using laser vision sensors as a non-destructive inspection method is underway. Although the external profile of the weld can be measured using a laser vision sensor, studies to predict the weld strength are insufficient. In this study, an artificial neural network (ANN) model was developed to predict the welding strength of the lap-fillet weld of an aluminum alloy. Input date for weld size was obtained in two ways. In the first method, a bead profile was acquired using a laser vision sensor, whereas the size of the weld was obtained through the acquired bead profile. In the second method, the size of the weld was obtained directly from cross-section analysis. The output data on the strength of the weld was obtained through a tensile shear test. Two models for predicting the tensile shear strength based on ANN were developed. By predicting the tensile strength of both models, the average error rate was within 10%, but the prediction accuracy using the laser vision sensor was better than that of the cross-sectional method.

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

A Hybrid Algorithm Based on GRNN and Grasshopper Optimization Algorithm for Welding Nugget Diameter Prediction

Abstract Resistance spot welding (RSW) is applied extensively by automotive manufacturers for assembling the structural and body components of vehicles. The current method of welding quality inspection is off-line inspection after welding, which cannot provide real-time feedback on welding quality and cannot meet the rhythm of modern production. Therefore, the online non-destructive testing technology of welding quality is worth studying. In this study, an RSW quality prediction model is developed using the improved grasshopper optimization algorithm combined with the generalized regression neural network (GRNN) algorithm, in which the actual process parameters including welding current, welding voltage, energy, power, and pulse width are used as inputs to predict the nugget diameter. During the network training process, the optimization algorithm is used for finding the optimum smoothing factor σ of GRNN, chaotic mapping, and non-uniform mutation are added to the traditional grasshopper optimization algorithm to enhance the optimization ability of the algorithm. Through bootstrap sampling, a comparison experiment about the prediction effect of the proposed quality prediction model with earlier methods is carried out, and the analysis of the experimental results leads to a conclusion that the accuracy of the proposed welding quality prediction model is higher.

Detection of butt weld of laser-MIG hybrid welding of thin-walled profile for high-speed train

PurposeThis study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.Design/methodology/approachIn order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.FindingsIt is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.Originality/valueThe results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

Image recognition of limited and imbalanced samples based on transfer learning methods for defects in welds

Welding quality inspection is critical to the quality control of the welding structure. Traditional manual detection requires experienced workers and the method is time-consuming. Currently, deep learning has made great progress in the field of image recognition. However, in terms of industrial defect detection, the contradiction between huge computational parameters and limited imbalanced samples still exists, which makes the deep learning method unable to play its role to the maximum extent. In this case, a combination of deep learning methods and transfer learning methods is considered to improve the performance of the model on limited and imbalanced data sets. The VGG16 model, which is pre-trained on a large number of source data sets, is fine-tuned on our target data sets. In this paper, a new DTN-VGG16 structure is proposed, in which a Global Average Pooling (GAP) layer, Batch Normalization (BN) layer, and Soft-max classifier are added on top of the frozen pre-training model, which can effectively reduce the number of parameters and accelerate the convergence speed of the model. In addition, focal loss function is used to replace the commonly used multi-class cross entropy loss function. By adjusting the parameters of the loss function, the training process has better convergence. Experimental results show that DTN-VGG16 has better performance than other traditional machine learning methods and deep learning models. And the proposed model has good robustness and generalization performance when valid on the real data of aerospace welding seams which is difficult to learn.

Optical coherence tomography for laser welding applications

In order to realize the automatic quality measurement of laser welding parts, an automatic on-line quality measurement system is established. The optical coherence tomography algorithm used in the system is studied. Firstly, the test equipment of laser welding quality inspection system is built based on optical coherence tomography algorithm. Then, the typical control algorithm is analyzed by taking the scanning galvanometer control as an example, that is, high-precision scanning and high-precision image acquisition in X and Y directions are realized. Then, the image is processed by dual-tree complex wavelet transform. Finally, the test sample is tested with the test equipment and the test results are analyzed. Experimental results show that this method can detect the quality of laser welding seam, and the detection accuracy is 5 µm. It provides industrial solutions for the online inspection of the laser welding to enable consistent and high quality welds.

Development of a digital twin for spot welding quality inspection in an industry 4.0 testbed of a Brazilian University

Development of a digital twin for spot welding quality inspection in an industry 4.0 testbed of a Brazilian University

Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net

Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved.

Welding Defects and Welding Quality Inspection

Welding Defects and Welding Quality Inspection

Design of a Robot for Pipeline Weld Quality Inspection

In the quality assessment of welding engineering, quality of the weld can reflect the welder’s technical level, and also serves as the basis for preliminary judgment of the quality of the weld. Therefore, the appearance quality inspection of the weld has an important position in the welding quality assessment. This paper designs a pipeline weld quality inspection robot based on visual inspection. Inspectors control the motor, lights and camera pan-title of the pipeline robot through WiFi to realize the accurate positioning of the robot and the shooting and transmission of real-time images. The movement distance of the robot is recorded by the encoder to ensure the accurate positioning of the robot; the motor speed is adjusted to ensure the level of the pipeline robot body; the height and rotation angle of the pan-tilt are adjusted to ensure that the camera shoots the weld image radially at the center of the pipeline. The experiment shows that the transmission of robot image is smooth, and actions can be completed according to instructions.

Process monitoring and internet of things feasibility for submerged arc welding: State of art

Good Quality and higher productivity play important role in manufacturing industries. Delivering improved quality product at reduced price in stiff-cut throat competitive market condition needs optimization of process. Welding is one of basic manufacturing process adopted by many industries for the production work. Weld quality inspection is carried out after welding is performed and product are rejected if defects are identified resulting in to repetition of whole cycle. To study and develop credible diagnosis system for welding On-line quality control in industry is customary of strategies. Sensors located in various region of welding play a significant role in online monitoring for process diagnostics. Experimental investigations will help to map weld quality in early stage with real time monitoring weld temperature, voltage, acoustic etc. with help of sensors placed around welding region. Arc column interface data can be captured using sensor and transferred via controllers displayed on mobile or workstation for live monitoring. In present paper authors have put an attempt to find need of submerged arc welding (SAW) arc column behaviour, its varied effect on weld quality, characterization of defect with sensor signals, methods used for monitoring and techniques to make intelligent welding expert system using Internet of Thing (IoT). Smart manufacturing is demanding change in the path of digitalization revolution. Adoption of Internet of Things (IoT) in the welding imparts assistances to overcome constraint based on adaptive control strategy leading to smart welding. Modern manufacturing environment is increasing reliant on high performance monitoring systems.

Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition

Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition

Automatic Detection of Welding Defects Using Faster R-CNN

In the shipbuilding industry, the non-destructive testing for welding quality inspection is mainly used for the permanent storage of the testing results and the radio-graphic testing which can visually inspect the interior of the welded part. Experts are required to properly detect the test results and it takes a lot of time and cost to manually Interpret the radio-graphic testing image of the structure over 500 blocks. The algorithms that automatically interpret the existing radio-graphic testing images to extract features through image pre-processing and classify the defects using neural networks, and only partial automation is performed. In order to implement the feature extraction and classification in one algorithm and to implement the overall automation, this paper proposes a method of automatically detecting welding defect using Faster R-CNN which is a deep learning basis. We analyzed the data to learn algorithms and compared the performance improvements using data augmentation method to artificially increase the limited data. In order to appropriately extract the features of the radio-graphic testing image, two internal feature extractors of Faster R-CNN were selected, compared, and performance evaluation was performed.

Nondestructive Testing Method for Welding Quality in Key Parts of Ocean-going Ships

Tang, F.D. and Yu, Y.F., 2020. Nondestructive testing method for welding quality in key parts of oceangoing ships. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 91–94. Coconut Creek (Florida), ISSN 0749-0208.Welding technology is widely used in the manufacture and production of structural parts of ocean-going ships. The quality of welding is crucial to the safety of ocean-going ships. This paper attempts to study the NDT methods for welding quality in the key parts of ocean-going ships. To this end, it first summarizes several typical defects that are common in key parts of ocean-going ships, and selects the butt welds on the deck as the research objects. Then, the conventional ultrasonic testing (UT), phased array ultrasonic testing (PAUT), and radiographic testing (RT) were used to conduct the NDT testing on the test plates. Finally, a comparative analysis was conducted for the test results of these three methods. The research results show that like the UT and RT methods, the PAUT can detect all defects, and the qualitative results are consistent; except for incompletely fused defects, its other testing results are better than the UT and RT, verifying the feasibility of the PAUT. The research findings enrich the practical research results of the PAUT in ship welding quality inspection, and provide an effective reference for the promotion of this testing method in marine ship welding quality.

A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery

With the wide applications of power battery in the automobile industries, the safety of power battery is becoming an increasingly prominent problem. At present, a safety vent welded on the battery plate could prevent unpredictable explosions. To perform quality control, the inspection of laser welding defects on safety vent is a critical issue. In this paper, based on the theory of convolutional neural network (CNN) and the technique of transfer learning, a pre-trained SqueezeNet model with small model size and low computation complexity was proposed. We totally collected 34537 images from the production line, and built a 2-classifications dataset and a 7-classifications dataset respectively. It proves that our proposed model achieved better accuracy than the other six contrastive CNN models in these two classification tasks. Specifically, it obtained an accuracy of 99.57 % in the 2-classifications task and an accuracy of 95.58 % in the 7-classifications task. Besides, the model features lightweight and high-speed with only 1.2 MB model size and 4.9 ms average test time; so, it is more suitable for welding quality inspection of safety vent in an industrial production line. Additionally, we ported our pre-trained SqueezeNet and the other four contrastive models to Raspberry Pi embedded system to evaluate their response time. Our proposed model has achieved well response time of less than 330 ms. Our experiments indicate that the proper CNN model can help to conduct the laser welding quality inspection tasks that are usually performed by humans.