Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered

A convolutional neural network (CNN) for defect detection of additively manufactured parts

“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the detection of good deposition quality and defects such as lack of fusion, gas porosity, and cracks in a fusion-based AM process, a CNN architecture is presented comparing the classification report and evaluation of different architectural settings and obtaining the optimized result from them. Since data set preparation, visualization, and balancing are very important aspects in deep learning to improve the performance and accuracy of neural network architectures, exploratory data analysis was performed for data visualization and the up-sampling method was implemented to balance the data set for each class. By comparing the results for different architectures, the optimal CNN network was chosen for further investigation. To tune the hyperparameters and to achieve an optimized parameter set, a design of experiments was implemented to improve the performance of the network. The performance of the network with optimized parameters was compared with the results from the previous study. The overall accuracy ( \u3e 97%) for both training and testing the CNN network presented in this work transcends the current state of the art (92%) for AM defect detection”--Abstract, page iv

Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs

Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu algoritma penyingkiran label, algoritma pengenalpastian bahagian kimpalan dan algoritma segmentasi kecacatan kimpalan. Algoritma penyingkiran label dibangunkan untuk mengenalpasti dan menyingkirkan label yang terdapat pada imej radiograf kimpalan secara automatik, sebelum algoritma pengenalpastian bahagian kimpalan dan algortima segmentasi kecacatan diaplikasikan ke atas imej radiografi. Satu algoritma pengenalpastian bahagian kimpalan juga dibangunkan dengan tujuan mengenalpasti bahagian kimpalan dalam imej radiogaf secara automatik dengan menggunakan profil keamatan yang diperoleh daripada imej radiografi. The objective of the research is to develop an automatic weld defect segmentation methodology to segment different types of defects in radiographic images of welds. The segmentation methodology consists of three main algorithms. namely label removal algorithm. weld extraction algorithm and defect segmentation algorithm. The label removal algorithm was developed to detect and remove labels that are printed on weld radiographs automatically before weld extraction algorithm and defect detection algorithm are applied. The weld extraction algorithm was developed to locate and extract welds automatically from the intensity profiles taken across the image by using graphical analysis. This algorithm was able to extract weld from a radiograph regardless of whether the intensity profile is Gaussian or otherwise. This method is an improvement compared to the previous weld extraction methods which are limited to weld image with Gaussian intensity profiles. Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method

Quantifying Mechanical Properties of Automotive Steels with Deep Learning Based Computer Vision Algorithms

This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials’ plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

Multi-stage Inspection of Laser Welding Defects using Machine Learning

As welding processes become faster and components consist of many more welds compared to previous applications, there is a need for fast but still precise quality inspection. The aim of this paper is to compare already existing approaches, namely single-sensor systems (SSS) and multi-sensor systems (MSS) with a proposed cascaded system (CS). The introduced CS is characterized by the fact that not all available data are analyzed, but only cleverly selected ones. The different approaches consisting of neural networks are compared in terms of their accuracy and computational effort. The data are recorded from scratch and include two common sensor systems for quality control, namely a photodiode (PD) and a high-speed camera (HSC). As a result, when the CS makes half of the final decisions based on a SSS with PD signals and the other half based on a SSS with HSC images, the estimated computational effort is reduced by almost 50% compared to the SSS with HSC images as input. At the same time, the accuracy decreases only by 0.25% to 95.96%. Additionally, based on the CS, a general cascaded system (GCS) for quality inspection is proposed

Welding Defect Detection with Deep Learning Architectures

Welding automation is a fundamental process in manufacturing industries. Production lines integrate welding quality controls to reduce wastes and optimize the production chain. Early detection is fundamental as defects at any stage could determine the rejection of the entire product. In the last years, following the industry 4.0 paradigm, industrial automation lines have seen the introduction of modern technologies. Although the majority of the inspection systems still rely on traditional sensing and data processing, especially in the computer vision domain, some initiatives have been taken toward the employment of machine learning architectures. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and discussing possible solutions in the specific case of quality checks in fuel injectors welding during the production stage