Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

Deep learning technology for weld defects classification based on transfer learning and activation features

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model.This work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE)

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Visual Sensing and Defect Detection of Gas Tungsten Arc Welding

Weld imperfections or defects such as incomplete penetration and lack of fusion are critical issues that affect the integration of welding components. The molten weld pool geometry is the major source of information related to the formation of these defects. In this dissertation, a new visual sensing system has been designed and set up to obtain weld pool images during GTAW. The weld pool dynamical behavior can be monitored using both active and passive vision method with the interference of arc light in the image significantly reduced through the narrow band pass filter and laser based auxiliary light source.Computer vision algorithms based on passive vision images were developed to measure the 3D weld pool surface geometry in real time. Specifically, a new method based on the reversed electrode image (REI) was developed to calculate weld pool surface height in real time. Meanwhile, the 2D weld pool boundary was extracted with landmarks detection algorithms. The method was verified with bead-on-plate and butt-joint welding experiments.Supervised machine learning was used to develop the capability to predict, in real-time, the incomplete penetration on thin SS304 plate with the key features extracted from weld pool images. An integrated self-adaptive close loop control system consisting the non-contact visual sensor, machine learning based defect predictor, and welding power source was developed for real-time welding penetration control for bead on plate welding. Moreover, the data driven methods were first applied to detect incomplete penetration and LOF in multi-pass U groove welding. New features extracted from reversed electrode image played the most important role to predict these defects. Finally, real time welding experiments were conducted to verify the feasibility of the developed models

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learning

Laser beam butt welding of thin sheets of high-alloy steel can be really challenging due to the formation of joint gaps, affecting weld seam quality. Industrial approaches rely on massive clamping systems to limit joint gap formation. However, those systems have to be adapted for each individually component geometry, making them very cost-intensive and leading to a limited flexibility. In contrast, jigless welding can be a high flexible alternative to substitute conventionally used clamping systems. Based on the collaboration of different actuators, motions systems or robots, the approach allows an almost free workpiece positioning. As a result, jigless welding gives the possibility for influencing the formation of the joint gap by realizing an active position control. However, the realization of an active position control requires an early and reliable error prediction to counteract the formation of joint gaps during laser beam welding. This paper proposes different approaches to predict the formation of joint gaps and gap induced weld discontinuities in terms of lack of fusion based on optical and tactile sensor data. Our approach achieves 97.4 % accuracy for video-based weld discontinuity detection and a mean absolute error of 0.02 mm to predict the formation of joint gaps based on tactile length measurements by using inductive probes

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset