Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images

Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.Copyright © 2022 by the authors.Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety.The research leading to these results has received funding from the UK’s innovation agency, Innovate UK, under grant agreement No. 103991. The research has been undertaken as a part of the project Amphibious robot for inspection and predictive maintenance of offshore wind assets (iFROG). The iFROG project is a collaboration between the following organizations: Innovative Technology and Science Ltd., Brunel University London, TWI Ltd., and ORE Catapult Development Services Ltd

An Artificial Intelligence Neural Network Predictive Model for Anomaly Detection and Monitoring of Wind Turbines Using SCADA Data

Copyright © 2022 The Author(s). The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction

Effect of the addition of nitrogen through shielding gas on tig welds made homogenously and heterogeneously on 300 series austenitic stainless steels

Copyright: © 2021 by the authors. Tungsten inert gas (TIG) welding of austenitic stainless steels is a critical process used in industries. Several properties of the welds must be controlled depending on the application. These properties, which include the geometrical, mechanical and microstructural features, can be modified through an appropriate composition of shielding gas. Researchers have studied the effects of the addition of nitrogen through the shielding gas; however, due to limited amount of experimental data, many of the interaction effects are not yet reported. In this study, welds were made homogeneously as well as heterogeneously with various concentrations of nitrogen added through the shielding gas. The gas compositions used were 99.99%Ar (pure), 2.5% N2 + Ar, 5% N2 + Ar and 10% N2 + Ar. Additionally, the welding process parameters were varied to understand different interaction effects between the shielding gas chemistry and the process variables such as filler wire feed rate, welding current, etc. Strong interactions were observed in the case of heterogeneous welds between the gas composition and the filler wire feed rate, with the penetration depth increasing by nearly 30% with the addition of 10% nitrogen in the shielding gas. The interactions were found to influence the bead geometry, which, in turn, had an effect on the mechanical properties as well as the fatigue life of the welds. A nearly 15% increase in the tensile strength of the samples was observed when using 10% nitrogen in the shielding gas, which also translated to a similar increase in the fatigue life

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Copyright: © 2021 by the authors. With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels.European Union’s HORIZON 2020 research and innovation progra

Destructive Testing of Open Rotor Propeller Blades for Extreme Operation Conditions

Due to the complexity of composite material, accurate manufacturing is very complicated and carry over a large number of uncertainties, the process of optimization and In this paper as part of design technology for rotor blades, the process of mechanical testing against severe working conditions using Acoustic Emission (AE) is discussed. As part of this process, the dynamic behaviour is analysed with validation and model updating of the structure and material properties, followed by structural tests under loads specific to the operational conditions. The results presented show how the appropriate AE technique has been implemented in order to obtain relevant data and focus on specific areas of interest for the design development, for correlation between modelling and experimental testing, the structure being tested to the ultimate load

INVESTIGATION ON THE USE OF POWER ULTRASONIC TO IMPROVE THE LASER WELDING OF ALUMINIUM ALLOYS

There is a rising interest on the autonomous laser welding of Aluminium alloys due to the quality of the weld, productivity and the simplicity of implementation. Unlike high grade alloys (i.e. Al 1100 which has excellent weldability), laser welding of low grade Alloys (i.e. Al 6063 which has poor weldability) has a higher demand due to material strength and cost benefits. However, laser welding of Alloys such as Al 6063 are challenging due to the material composition which has a poor weldability. Current study investigates the possibility of using high power ultrasonic during the laser welding process, to reduce voids during solidification and optimize the laser welding process. A finite element-based numerical study was undertaken to evaluate the propagation of ultrasonic waves and their interaction with the incremental weld seam. The plate sample (before joining) used in this study is a 300 x 150 x 3 mm (height, width and thickness respectively). A parametric study was conducted to obtain the resonant frequency of the sample plate and the optimum power level in order to tune the power ultrasonic system. A 3-D laser Doppler vibrometry experiment was conducted to validate the finite element results. There is a good agreement between numerical and experimental results. Based on the results, 40 kHz 60 W transducers need to be used for ultrasonication in order to improve the laser welding of Al 6063 using power ultrasonic. Furthermore, transducer topology was also investigated in order to optimize the system performance

Structural health monitoring of above-ground storage tank floors by ultrasonic guidedwave excitation on the tank wall

Abstract: There is an increasing interest in using ultrasonic guided waves to assess the structural degradation of above-ground storage tank floors. This is a non-invasive and economically viable means of assessing structural degradation. Above-ground storage tank floors are ageing assets which need to be inspected periodically to avoid structural failure. At present, normal-stress type transducers are bonded to the tank annular chime to generate a force field in the thickness direction of the floor and excite fundamental symmetric and asymmetric Lamb modes. However, the majority of above-ground storage tanks in use have no annular chime due to a simplified design and/or have a degraded chime due to corrosion. This means that transducers cannot be mounted on the chime to assess structural health according to the present technology, and the market share of structural health monitoring of above-ground storage tank floors using ultrasonic guided wave is thus limited. Therefore, the present study investigates the potential of using the tank wall to bond the transducer instead of the tank annular chime. Both normal and shear type transducers were investigated numerically, and results were validated using a 4.1 m diameter above-ground storage tank. The study results show shear mode type transducers bonded to the tank wall can be used to assess the structural health of the above-ground tank floors using an ultrasonic guided wave. It is also shown that for the cases studied there is a 7.4 dB signal-to-noise ratio improvement at 45 kHz for the guided wave excitation on the tank wall using shear mode transducers

A numerical study on the excitation of guided waves in rectangular plates using multiple point sources

Ultrasonic guided waves are widely used to inspect and monitor the structural integrity of plates and plate-like structures, such as ship hulls and large storage-tank floors. Recently, ultrasonic guided waves have also been used to remove ice and fouling from ship hulls, wind-turbine blades and aeroplane wings. In these applications, the strength of the sound source must be high for scanning a large area, or to break the bond between ice, fouling and plate substrate. More than one transducer may be used to achieve maximum sound power output. However, multiple sources can interact with each other, and form a sound field in the structure with local constructive and destructive regions. Destructive regions are weak regions and shall be avoided. When multiple transducers are used it is important that they are arranged in a particular way so that the desired wave modes can be excited to cover the whole structure. The objective of this paper is to provide a theoretical basis for generating particular wave mode patterns in finite-width rectangular plates whose length is assumed to be infinitely long with respect to its width and thickness. The wave modes have displacements in both width and thickness directions, and are thus different from the classical Lamb-type wave modes. A two-dimensional semi-analytical finite element (SAFE) method was used to study dispersion characteristics and mode shapes in the plate up to ultrasonic frequencies. The modal analysis provided information on the generation of modes suitable for a particular application. The number of point sources and direction of loading for the excitation of a few representative modes was investigated. Based on the SAFE analysis, a standard finite element modelling package, Abaqus, was used to excite the designed modes in a three-dimensional plate. The generated wave patterns in Abaqus were then compared with mode shapes predicted in the SAFE model. Good agreement was observed between the intended modes calculated in SAFE and the actual, excited modes in Abaqus

Monitoring of industrial machine using a novel blind feature extraction approach

Copyright: © 2021 by the authors. Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.UK’s innovation agency, Innovate UK under grant agreement number 104505

Characterization of the Use of Low Frequency Ultrasonic Guided Waves to Detect Fouling Deposition in Pipelines

Abstract: The accumulation of fouling within a structure is a well-known and costly problem across many industries. The build-up is dependent on the environmental conditions surrounding the fouled structure. Many attempts have been made to detect fouling accumulation in critical engineering structures and to optimize the application of power ultrasonic fouling removal procedures, i.e., flow monitoring, ultrasonic guided waves and thermal imaging. In recent years, the use of ultrasonic guided waves has been identified as a promising technology to detect fouling deposition/growth. This technology also has the capability to assess structural health; an added value to the industry. The use of ultrasonic guided waves for structural health monitoring is established but fouling detection using ultrasonic guided waves is still in its infancy. The present study focuses on the characterization of fouling detection using ultrasonic guided waves. A 6.2-m long 6-inch schedule 40 carbon steel pipe has been used to study the effect of (Calcite) fouling on ultrasonic guided wave propagation within the structure. Parameters considered include frequency selection, number of cycles and dispersion at incremental fouling thickness. According to the studied conditions, a 0.5 dB/m drop in signal amplitude occurs for a fouling deposition of 1 mm. The findings demonstrate the potential to detect fouling build-up in lengthy pipes and to quantify its thickness by the reduction in amplitude found from further numerical investigation. This variable can be exploited to optimize the power ultrasonic fouling removal procedure