Advanced Optical Coherence Tomography for Real-Time Detection of Defects in Aluminum Alloy Laser Welding

In order to measure the quality of aluminum alloy laser welding workpiece online, an optical coherence tomography on-line detection system was established. Porosity is one of the most common defects in laser welding of aluminum alloy. The porosity produced during welding will seriously affect the welding quality. Firstly, a test device of laser welding quality detection system is built based on optical coherence tomography algorithm. Then, the theoretical model of the optical coherence tomography detection system is built, and the key parameters affecting the detection device are qualitatively analyzed. Then, deep convolutional neural network algorithm is used to process the image. Finally, the testing equipment is used to test the sample, and the testing results are analyzed. The experimental results show that this method can detect the weld quality of laser welding, and the detection accuracy is 20 μm

Seam tracking and gap bridging during robotic laser beam welding via grayscale imaging and wobbling

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration

High-resolution 3D weld toe stress analysis and ACPD method for weld toe fatigue crack initiation

Weld toe fatigue crack initiation is highly dependent on the local weld toe stress-concentrating geometry including any inherent flaws. These flaws are responsible for premature fatigue crack initiation (FCI) and must be minimised to maximise the fatigue life of a welded joint. In this work, a data-rich methodology has been developed to capture the true weld toe geometry and resulting local weld toe stress-field and relate this to the FCI life of a steel arc-welded joint. To obtain FCI lives, interrupted fatigue test was performed on the welded joint monitored by a novel multi-probe array of alternating current potential drop (ACPD) probes across the weld toe. This setup enabled the FCI sites to be located and the FCI life to be determined and gave an indication of early fatigue crack propagation rates. To understand fully the local weld toe stress-field, high-resolution (5 mu m) 3D linear-elastic finite element (FE) models were generated from X-ray micro-computed tomography (mu-CT) of each weld toe after fatigue testing. From these models, approximately 202 stress concentration factors (SCFs) were computed for every 1 mm of weld toe. These two novel methodologies successfully link to provide an assessment of the weld quality and this is correlated with the fatigue performance

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

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time

Weld pool and keyhole dynamic analysis based on visual system and neural network during laser keyhole welding

In keyhole fiber laser welding processes, the weld pool behavior and keyhole dynamics are essential to determining welding quality. To observe and control the welding process, the accurate extraction of the weld pool boundary as well as the width is required. In addition, because of the cause-and-effect relationship between the welding defects and stability of the keyhole, which is primarily determined by keyhole geometry during the welding process, the stability of keyhole needs to be considered as well.^ The first part of this thesis presents a weld pool edge detection technique based on an off axial green illumination laser and a coaxial image capturing system that consists of a CMOS camera and optic filters. According to the difference of image quality, a complete developed edge detection algorithm is proposed based on the local maximum gradient of grayness searching approach and linear interpolation. The extracted weld pool geometry and the width are validated by the actual welding width measurement and predictions by a numerical multi-phase model.^ As for the keyhole dynamics, three essential attributes to describe the simplified three-dimensional keyhole shape include keyhole size, penetration depth and keyhole inclination angle. However, when using traditional measurement techniques, it is very challenging to take in-process measurements of penetration depth and inclination angle, even if the keyhole size can be detected by using a visual monitoring system. To realize the on-line estimation of keyhole dynamics and welding defects, a data-based radial basis function neural network state observer is adopted for estimating penetration depth and inclination angle in the transient state when welding parameters change suddenly. First, a static neural network is trained in advance to establish a correlation between the welding parameters and unobservable keyhole geometry. The dynamic state observer is trained based on the transient welding conditions predicted by a numerical model and then used to estimate the time-varying keyhole geometery. Meanwhile, the coaxial monitoring system is used to observe the keyhole shape from the top side in real time, which not only provides input to the neural network but also indicates the potential welding porosities. The predicted results are validated by experimental data performed by welding with stainless steel 304 and magnesium alloy AZ31B

Nondestructive evaluation and in-situ monitoring for metal additive manufacturing

Powder-based additive manufacturing (AM) technologies are seeing increased use, particularly because they give greatly enhanced design flexibility and can be used to form components that cannot be formed using subtractive manufacturing. There are fundamental differences in the morphology of additively manufactured materials, when compared with, for example castings or forgings. In all cases it is necessary to ensure that parts meet required quality standards and that “allowable” anomalies can be detected and characterized. It is necessary to understanding the various types of manufacturing defects and their potential effects on the quality and performance of AM, and this is a topic of much study. In addition, it is necessary to investigate quality from powder throughout the manufacturing process from powder to the finished part. In doing so it is essential to have metrology tools for mechanical property evaluation and for appropriate anomaly detection, quality control, and monitoring. Knowledge of how and when the various types of defects appear will increase the potential for early detection of significant flaws in additively manufactured parts and offers the potential opportunity for in-process intervention and to hence decrease the time and cost of repair or rework. Because the AM process involves incremental deposition of material, it gives unique opportunities to investigate the material quality as it is deposited. Due to the AM processes sensitivity to different factors such as laser power and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of additive manufacturing (AM) is crucial to assure the quality, integrity, and safety of AM parts. To meet this need there are a variety of sensing methods and signals which can be measured. Among the available measurement modalities, acoustic-based methods have the advantage of potentially providing real-time, continuous in-service monitoring of manufacturing processes at relatively low cost. In this research, the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capabilities of existing characterization methodologies for powder-based AM parts are discussed and methods for in-situ non-destructive evaluation are reviewed. A proof-of-concept demonstration for acoustic measurements used for monitoring both machine and material state is demonstrated. The analyses have been performed on temporal and spectral features extracted from the acoustic signals. These features are commonly related to defect formation, and acoustic noise that is generated and can potentially characterize the process. A novel application of signal processing tools is used for identification of temporal and spectral features in the acoustic signals. A new approach for a K-means statistical classification algorithm is used for classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures demonstrate potential for in-situ monitoring and quality control of the additive manufacturing process and parts. A numerical model of the temperature field and the ultrasonic wave displacement field induced by an incident pulsed laser on additively manufactured stainless steel 17 4 PH is established which is based on thermoelastic theory. The numerical results indicate that the thermoelastic source and the ultrasonic wave features are strongly affected by the characteristics of the laser source and the thermal and mechanical properties of the material. The magnitude and temporal-spatial distributions of the pulsed laser source energy are very important factors which determine not only the wave generation mechanisms, but also the amplitude and characteristics of the resulting elastic wave signals

Novel Approaches for Nondestructive Testing and Evaluation

Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively