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

Detection and classification of internal flaws in laser powder bed fusion: application of in-situ monitoring for quality control of Hastelloy X builds

Additive manufacturing technologies, in particular laser powder bed fusion (LPBF), have received much attention in recent years due to their multiple advantages over traditional manufacturing. Yet, the usage of additively manufactured products is still quite limited, mainly due to two factors: the low repeatability, which is particularly relevant for applications where high performance is required from the materials, and the typically low productivity, particularly relevant for products with a substantial production volume.The main factor that affects repeatability and compromises the performance of the materials is the presence of flaws. Hence, to assess the quality of a product and to predict its performance, it is crucial to recognize which flaws are present and ensure their detectability. Moreover, if the flaws can be detected during the manufacturing process, corrective actions can be taken. In this thesis, internal flaws were deliberately created in LPBF manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate flaw formation have been identified while preserving flaw formation mechanisms; therefore, this thesis is split into two parts, according to the approach employed to create flaws.Flaws are generated systematically if inadequate process parameters are employed. By varying the processing conditions, different types, amounts and sizes of flaws are created. By monitoring the manufacturing process with long-exposure near-infrared imaging and applying supervised machine learning, it was possible to distinguish process conditions that generate the different flaw categories with accuracy, precision and recall of at least 96%.Flaws are created stochastically as a result of the redeposition of process by-products on the build area. It was found that substantial amounts of flaws can be provoked through this route when increasing the nominal layer thickness in the build, thus enabling the validation of the monitoring system in their detection. After applying an image analysis algorithm to all the images output from in-situ monitoring in three builds, it was possible to identify trends in the spatial distribution of spatter redeposits. Ex-situ inspection and material characterization provided cross-check for the distribution of flaws.The low productivity of LPBF makes it less competitive in applications with moderate to high production volumes. This issue is briefly addressed in this thesis. Even though one of the main approaches to increase productivity is to tune the main process parameters, dissimilar strategies were identified in the literature towards this goal. Thus, parametrization of build rates was done and applied to the processing conditions deemed to provide material with acceptable quality, based on the quantity and types of flaws present. The material manufactured in these conditions was characterized, and it was found that substantially different microstructures can be achieved within the process window, depending on the build rate

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

ESTABLISHING THE FOUNDATION TO ROBOTIZE COMPLEX WELDING PROCESSES THROUGH LEARNING FROM HUMAN WELDERS BASED ON DEEP LEARNING TECHNIQUES

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF). This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by robotize a complex welding process. By integrate process sensing, data visualization, deep learning-based modeling and optimization, a complex welding system is established, with the systematic solution to generalize domain-specific knowledge from experienced human welder. Such system can automatically perform complex welding processes that can only be handled by human in the past. To enhance the system\u27s tracking capabilities, we trained an image segmentation network to offer precise position information. We incorporated a recurrent neural network structure to analyze dynamic variations during welding. Addressing the challenge of human heterogeneity in data collection, we conducted experiments illustrating that even inaccurate datasets can effectively train deep learning models with zero mean error. Fine-tuning the model with a small portion of accurate data further elevates its performance

Development of a real-time ultrasonic sensing system for automated and robotic welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The implementation of robotic technology into welding processes is made difficult by the inherent process variables of part location, fit up, orientation and repeatability. Considering these aspects, to ensure weld reproducibility consistency and quality, advanced adaptive control techniques are essential. These involve not only the development of adequate sensors for seam tracking and joint recognition but also developments of overall machines with a level of artificial intelligence sufficient for automated welding. The development of such a prototype system which utilizes a manipulator arm, ultrasonic sensors and a transistorised welding power source is outlined. This system incorporates three essential aspects. It locates and tracks the welding seam ensuring correct positioning of the welding head relatively to the joint preparation. Additionally, it monitors the joint profile of the molten weld pool and modifies the relevant heat input parameters ensuring consistent penetration, joint filling and acceptable weld bead shape. Finally, it makes use of both the above information to reconstruct three-dimensional images of the weld pool silhouettes providing in-process inspection capabilities of the welded joints. Welding process control strategies have been incorporated into the system based on quantitative relationships between input parameters and weld bead shape configuration allowing real-time decisions to be made during the process of welding, without the need for operation intervention.British Technology Group (BTG

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university

Additive Manufacturing (AM) of Metallic Alloys

The introduction of metal AM processes in such industrial sectors as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight, and fewer potential failure points from joining features. This Special Issue on “Additive Manufacturing (AM) of Metallic Alloys” contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metals in AM

Development of Non-Destructive Testing by Eddy Currents for Highly Demanding Engineering Applications

Defect detection with Non-Destructive Testing (NDT) is essential in accidents prevention, requiring R&TD to generate new scientific and procedural knowledge for new products with high safety requirements. A current challenge lies in the detection of surface and sub-surface micro defects with NDT by Eddy Currents (EC). The main objective of this work was the development of applied research, technological innovation and experimental validation of EC customized systems for three highly demanding inspection scenarios: micro defects in tubular geometries; brazed joints for the automotive industry; and high-speed moving composite materials. This objective implied starting from the scientific fundamentals of NDT by EC to design and simulate EC probes and the prototypes developed were tested in industrial environment, reaching a TRL ≈ 5. Another objective, of a more scientific and disruptive nature, was to test a new technique for the creation of EC in the materials to be inspect, named Magnetic Permeability Pattern Substrate (MPPS). This technique consists on the development of substrates/films with patterns of different magnetic permeabilities rather than the use of excitation bobbin coils or filaments of complex geometry. The experimental results demonstrated that the prototypes developed for the three industrial applications studied outperformed the state of the art, allowing the detection of target defects with a very good signal-to-noise ratio: in tubular geometries defects with depth of 0.5 mm and thickness of 0.2 mm in any scanning position; in the laser brazed weld beads pores with 0.13 mm diameter and internal artificial defects 1 mm from the weld surface; in composite materials defects under 1 mm at speeds up to 4 m/s and 3 mm lift-off. The numerical simulations assisted the probe design, allowing to describe and characterize electrical and magnetic phenomena. The new MPPS concept for the introduction of EC was validated numerically and experimentally