Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

© 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.Peer reviewe

Single- and dual-carrier microwave noise abatement in the deep space network

The NASA/JPL Deep Space Network (DSN) microwave ground antenna systems are presented which simultaneously uplink very high power S-band signals while receiving very low level S- and X-band downlinks. Tertiary mechanisms associated with elements give rise to self-interference in the forms of broadband noise burst and coherent intermodulation products. A long-term program to reduce or eliminate both forms of interference is described in detail. Two DSN antennas were subjected to extensive interference testing and practical cleanup program; the initial performance, modification details, and final performance achieved at several planned stages are discussed. Test equipment and field procedures found useful in locating interference sources are discussed. Practices deemed necessary for interference-free operations in the DSN are described. Much of the specific information given is expected to be easily generalized for application in a variety of similar installations. Recommendations for future investigations and individual element design are given

Columbus pressurized module verification

The baseline verification approach of the COLUMBUS Pressurized Module was defined during the A and B1 project phases. Peculiarities of the verification program are the testing requirements derived from the permanent manned presence in space. The model philosophy and the test program have been developed in line with the overall verification concept. Such critical areas as meteoroid protections, heat pipe radiators and module seals are identified and tested. Verification problem areas are identified and recommendations for the next development are proposed

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

잔차 합성곱 신경망을 통한 산업용 로봇 기어박스의 동작 적응형 퓨샷 고장 감지 방법

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Nowadays, industrial robots are indispensable equipment for automated manufacturing processes because they can perform repetitive tasks with consistent precision and accuracy. However, when faults occur in the industrial robot, it can lead to the unexpected shutdown of the production line, which brings significant economic losses, so the fault detection is important. The gearbox, one of the main drivetrain components of an industrial robot, is often subjected to high torque loads, and faults occur frequently. When faults occur in the gearbox, the amplitude and frequency of the torque signal are modulated, which leads to changes in the characteristics of the torque signal. Although several previous studies have proposed fault detection methods for industrial robots using torque signals, it is still a challenge to extract fault-related features under various environmental and operating conditions and to detect faults in the complex motions used in industrial sites To overcome such difficulties, in this paper, we propose a novel motion-adaptive few-shot (MAFS) fault detection method of industrial robot gearboxes using torque ripples via a one-dimensional (1D) residual-convolutional neural network (Res-CNN) and binary-supervised domain adaptation (BSDA). The overall procedure of the proposed method is as follows. First, applying the moving average filtering to the torque signal to extract the data trend, and the torque ripples of the high-frequency band are obtained as a residual value between the original signal and the filtered signal. Second, classifying the state of pre-processed torque ripples under various operating and environmental conditions. It is shown that Res-CNN network 1) distinguishes small differences between normal and fault torque ripples effectively, and 2) focuses on important regions of the input data by the attention effect. Third, after constructing the Siamese network with a pre-trained network in the source domain, which consisted of simple motions, detecting the faults on the target domain, which consisted of complex motions through BSDA. As a result, 1) the similarities of the jointly shared physical mechanisms of torque ripples between simple and complex motions are learned, and 2) faults of the gearbox are adaptively detected while the industrial robot executes complex motions. The proposed method showed the most superior accuracy over other deep learning-based methods in few-shot conditions where only one cycle of each normal and fault data of complex motions is available. In addition, the transferable regions on the torque ripples after domain adaptation was highlighted using 1D guided grad-CAM. The effectiveness of the proposed method was validated with experimental data of multi-axial welding motions in constant and transient speed, which are commonly executed in real-industrial fields such as the automobile manufacturing line. Furthermore, it is expected that the proposed method is applicable to other types of motions, such as inspection, painting, assembly, and so on. The source code is available on my GitHub page of https://github.com/oyt9306/MAFS.Chapter 1. Introduction 1 1.1 Research Motivation 1 1.2 Scope of Research 4 1.3 Thesis Layout 5 Chapter 2. Research Backgrounds 6 2.1 Interpretations of Torque Ripples 6 2.1.1. Causes of torque ripples 6 2.1.1. Modulations on torque ripples due to gearbox faults 8 2.2 Architectures of Res-CNN 11 2.2.1 Convolutional Operation 11 2.2.2 Pooling Operation 12 2.2.3 Activation 13 2.2.4 Batch Normalization 13 2.2.5 Residual Learning 15 2.3 Domain Adaptation (DA) 17 2.3.1 Few-shot domain adaptation 18 Chapter 3. Motion-Adaptive Few-Shot (MAFS) Fault Detection Method 20 3.1 Pre-processing 23 3.2 Network Pre-training 28 3.3 Binary-Supervised Domain Adaptation (BSDA) 31 Chapter 4. Experimental Validations 37 4.1 Experimental Settings 37 4.2 Pre-trained Network Generation 40 4.3 Motion-Adaptation with Few-Shot Learning 43 Chapter 5. Conclusion and Future Work 52 5.1 Conclusion 52 5.2 Contribution 52 5.3 Future Work 54 Bibliography 55 Appendix A. 1D Guided Grad-CAM 60 국문 초록 62Maste

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens

Friction Stir Welding Manufacturing Advancement by On-Line High Temperature Phased Array Ultrasonic Testing and Correlation of Process Parameters to Joint Quality

Welding, a manufacturing process for joining, is widely employed in aerospace, aeronautical, maritime, nuclear, and automotive industries. Optimizing these techniques are paramount to continue the development of technologically advanced structures and vehicles. In this work, the manufacturing technique of friction stir welding (FSW) with aluminum alloy (AA) 2219-T87 is investigated to improve understanding of the process and advance manufacturing efficiency. AAs are widely employed in aerospace applications due to their notable strength and ductility. The extension of good strength and ductility to cryogenic temperatures make AAs suitable for rocket oxidizer and fuel tankage. AA-2219, a descendent of the original duralumin used to make Zeppelin frames, is currently in wide use in the aerospace industry. FSW, a solid-state process, joins the surfaces of a seam by stirring the surfaces together with a pin while the metal is held in place by a shoulder. The strength and ductility of friction stir (FS) welds depends upon the weld parameters, chiefly spindle rotational speed, feedrate, and plunge force (pinch force for self-reacting welds). Between conditions that produce defects, it appears in this study as well as those studies of which we are aware that FS welds show little variation in strength; however, outside this process parameter “window” the weld strength drops markedly. Manufacturers operate within this process parameter window, and the parameter establishment phase of welding operations constitutes the establishment of this process parameter window. The work herein aims to improve the manufacturing process of FSW by creating a new process parameter window selection methodology, creation of a weld quality prediction model, developing an analytical defect suppression model, and constructing a high temperature on-line phased array ultrasonic testing system for quality inspection

A Study on Adhesive Strength of Co-Cured CFRP-Metal Multi-Material Joints and Joint Failure Detection Using Electrical Resistance Measurement

Department of Mechanical EngineeringCarbon-Fiber-Reinforced Plastics (CFRPs) are composite materials, consisting of carbon fibers and polymeric matrices. Depending on the types of carbon fiber and polymer used, CFRP can have a variety of properties. Generally, CFRP show high specific strength and stiffness, so it is regarded as a substitute material for existing structural materials, such as metals. In addition, CFRP can have high temperature or corrosion resistance based on the type of matrix used. For this reason, despite the high price of carbon fiber, it is widely applied to the aerospace industry and has gradually expanded into the automotive industry in recent years. Despite their advantages in terms of weigh-saving, it is not possible to replace all the metal parts, especially ultra-high-strength steels, etc., with CFRPs due to their limitations in intrinsic properties. This has led to the ???multi-material design??? concept, in which hetero-junctions between composites and metals have become an important issue. Typical methods for multi-material joining include mechanical joining and adhesive bonding. Mechanical joining, e.g., riveting, mechanical fastening, etc., leads to high stress concentration due to the pre-drilled holes, and it has to bear additional weight of inserts, such as bolts and rivets. Adhesive bonding, on the other hand, requires time of adhesive curing in addition to matrix curing, which has detrimental effects on manufacturing time and costs. To overcome these drawbacks, the co-curing method, in which the infused resin serves as the adhesive and therefore, the additional adhesive curing time can be omitted, has been considered as an alternative cost-effective adhesive joining method. Although multi-material joining using the co-curing method results in a lower adhesive strength than adhesive-bonded joints, this method can reduce the curing time since adhesive and CFRP curing proceed simultaneously and makes possible real-time health monitoring of the joints using electrical resistance measurement because carbon fiber directly contacts the metal surface, both of which are electrically conductive. In this study, we showed that structural health monitoring using electrical resistance measurement at the junction between metals and CFRPs joined by co-curing is feasible, and its effectiveness was studied as compared to the case where a conductive epoxy was used as the adhesive. Also, we measured the adhesive strength and determined the possibility of failure detection when a steel bushing, which is one of metal inserts, was joined by co-curing with CFRP. In addition, the interfacial strength between metal and polymer resin was enhanced by atmospheric plasma surface treatment since aluminum-CFRP co-cured joints initially had poor lap shear strength. CFRP was fabricated by plain-woven carbon fibers and unsaturated polyester resin, and stainless steel and aluminum sheets were used as the metals for multi-material joining. To detect the failure at the junction between CFRP and metal, co-curing was adopted rather than an epoxy adhesive containing dispersed carbon nanotubes (CNTs). In the co-curing process, conductive carbon fiber and metals directly contacted each other, so electric current can flow through both materials. As the initial load increased, the resistance gradually decreased, and then increased drastically due to de-bonding at the co-cured joints. Electrical resistance was increased when the contact area between carbon fiber and metal surface were decreased, so it can monitor the failure detection at the multi-material joints. Single-lap shear test was performed for each joint, and four-wire Kelvin resistance measurement was adopted to measure the change in resistance during the test. To apply this research, we manufactured steel bushing-inserted CFRPs joined by co-curing method. Push-out tests were performed to measure the adhesive strength between the inserts and CFRPs. Next, we demonstrated the proof-of-concept of health monitoring at the co-cured joints between steel bushings and CFRPs using electrical resistance measurements. In the case of aluminum-CFRP co-cured joints, the adhesive strength was about 30% compared to the other joints, so we applied atmospheric plasma to the metal surfaces such as steel, aluminum and steel bushings. Upon plasma treatment, the adhesive strength of aluminum-CFRPs co-cured joint was increased by 300%. After plasma treatment, the number of hydrogen bonds increased between the unsaturated polyester and the metal surfaces as the metal surfaces were getting more hydrophilic. Wettability was increased due to the increase of -OH functional groups on the metal surfaces, which led to the enhancement of the interfacial adhesive strength between polyester and metal surfaces bonded through the co-curing process. As the adhesive strength increased with plasma treatment, it was shown that the gradient of the resistance rate decreases prior to the complete destruction at the joints. For this reason, it is important to identify the optimized point to secure failure strength and predict joint failure. Based on the experimental results, it is feasible to monitor failures in multi-material joints between CFRPs and conductive metals real-time by measuring the change in resistance. This ensures the safety of various CFRP-metal multi-material structures, including aircraft, automotive parts, civil structures, sporting goods, electronic modules, and biomedical devices.ope

Underpinning UK High-Value Manufacturing: Development of a Robotic Re-manufacturing System

Impact and its measure of outcome is a given performance indicator within academia. Impact metrics and the associated understanding play a large part of how academic research is judged and ultimately funded. Natural progression of successful scientific research into industry is now an essential tool for academia. This paper describes what began over ten years ago as a concept to automate a bespoke welding system, highlighting its evolution from the research laboratories of The University of Sheffield to become a platform technology for aerospace remanufacturing developed though industry-academia collaboration. The design process, funding mechanisms, research and development trials and interaction between robotic technology and experienced welding engineers has made possible the construction of a robotic aerospace turbofan jet engine blade re-manufacturing system. This is a joint collaborative research and development project carried out by VBC Instrument Engineering Limited (UK) and The University of Sheffield (UK) who are funded by the UK governments’ innovation agency, Innovate-UK with the Aerospace Technology Institute, the Science and Facilities Technology Council (STFC) and the Engineering and Physical Sciences Research Council (EPSRC)

Optimising non-destructive examination of newbuilding ship hull structures by developing a data-centric risk and reliability framework based on fracture mechanics

This thesis was previously held under moratorium from 18/11/19 to 18/11/21Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure.Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure