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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Pierce, Stephen
University of Strathclyde
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (51/51 displayed)
- 20243-Dimensional residual neural architecture search for ultrasonic defect detectioncitations
- 2023Flexible and automated robotic multi-pass arc welding
- 2023Application of eddy currents for inspection of carbon fibre composites
- 2023Application of machine learning techniques for defect detection, localisation, and sizing in ultrasonic testing of carbon fibre reinforced polymers
- 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approachescitations
- 2023Mapping SEARCH capabilities to Spirit AeroSystems NDE and automation demand for composites
- 2023Using neural architecture search to discover a convolutional neural network to detect defects From volumetric ultrasonic testing data of composites
- 2023Phased array inspection of narrow-gap weld LOSWF defects for in-process weld inspection
- 2023Driving towards flexible and automated robotic multi-pass arc welding
- 2022Transfer learning for classification of experimental ultrasonic non-destructive testing images from synthetic data
- 2022Mechanical stress measurement using phased array ultrasonic system
- 2022Towards ultrasound-driven, in-process monitoring & control of GTA welding of multi-pass welds for defect detection & prevention
- 2022Automated bounding box annotation for NDT ultrasound defect detection
- 2022Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defects
- 2022Investigating ultrasound wave propagation through the coupling medium and non-flat surface of wire + arc additive manufactured components inspected by a PAUT roller-probe
- 2022Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing
- 2022Towards real-time ultrasound driven inspection and control of GTA welding processes for high-value manufacturing
- 2022Dual-tandem phased array inspection for imaging near-vertical defects in narrow gap welds
- 2022In-process non-destructive evaluation of wire + arc additive manufacture components using ultrasound high-temperature dry-coupled roller-probe
- 2022Automated real time eddy current array inspection of nuclear assetscitations
- 2021Feed forward control of welding process parameters through on-line ultrasonic thickness measurementcitations
- 2021A cost-function driven adaptive welding framework for multi-pass robotic weldingcitations
- 2021Non-contact in-process ultrasonic screening of thin fusion welded jointscitations
- 2020In-process calibration of a non-destructive testing system used for in-process inspection of multi-pass weldingcitations
- 2020Machine learning at the interface of structural health monitoring and non-destructive evaluationcitations
- 2020Quantifying impacts on remote photogrammetric inspection using unmanned aerial vehiclescitations
- 2020Laser-assisted surface adaptive ultrasound (SAUL) inspection of samples with complex surface profiles using a phased array roller-probe
- 2019Ultrasonic phased array inspection of wire + arc additive manufacture samples using conventional and total focusing method imaging approachescitations
- 2019Electromagnetic acoustic transducers for guided-wave based robotic inspection
- 2019A probabilistic compressive sensing framework with applications to ultrasound signal processingcitations
- 2019Ultrasonic phased array inspection of a Wire + Arc Additive Manufactured (WAAM) sample with intentionally embedded defectscitations
- 2019Towards guided wave robotic NDT inspection
- 2018Machining-based coverage path planning for automated structural inspectioncitations
- 2018Ultrasonic phased array inspection of wire plus arc additive manufacture (WAAM) samples using conventional and total focusing method (TFM) imaging approaches
- 2016Investigation of synthetic aperture methods in ultrasound surface imaging using elementary surface typescitations
- 2016Robotic ultrasonic testing of AGR fuel claddingcitations
- 2016Conformable eddy current array deliverycitations
- 2016Robotic path planning for non-destructive testing - a custom MATLAB toolbox approachcitations
- 2014Automatic ultrasonic robotic arraycitations
- 2014Robotic path planning for non-destructive testing of complex shaped surfaces
- 2013The feasibility of synthetic aperture guided wave imaging to a mobile sensor platformcitations
- 2012Features for damage detection with insensitivity to environmental and operational variationscitations
- 2011Some experimental observations on the detection of composite damage using lamb wavescitations
- 2011On impact damage detection and quantification for CFRP laminatescitations
- 2010A comparison of methods used to predict the vibrational energy required for a reliable thermosonic inspection
- 2010Monitoring crack propagation in turbine blades caused by thermosonic inspection
- 2008Damage localisation in a stiffened composite panelcitations
- 2007Damage detection using stress waves and multivariate statistics, an experimental case study of an aircraft componentcitations
- 2007Damage location in a stiffened composite panel using Lamb waves and neural networks
- 2006On the reproducibility of transducer coupling for acoustic emission testing
- 2001On the long-term stability of normal condition for damage detection in a composite panel
Places of action
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document
Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing
Abstract
The scale of the global market size for metal Additive Manufacturing (AM) in recent years, at €2.02 billion in 2019, and predictions for the continuous growth up to 27.9% annually until 2024 highlight the key role that these processes will play in the future of high-value manufacturing. AM technology leverages the concepts of the latest industrial revolution, Industry 4.0, where the manufacturing is made more flexible and smarter capable of fabricating cost-effective high-quality customized products. Among the various AM technologies, only a few such as the Wire + Arc Additive Manufacturing (WAAM) process can meet some industries’ demands for producing large components in a short time. The process typically involves industrial robots and arc-based welding performing layer-by-layer deposition on substrates and building up components to their final desired shape. The process is majorly used to manufacture low volume, high mix, critical components for applications in aerospace and nuclear industries. Therefore, the imposed inspection requirements demand very high detection sensitivities. Robotically-deployed Non-Destructive Evaluation (NDE) during the manufacturing process might just be the inspection process needed to ensure the component’s integrity as it is being built, paving the way for an easier part certification process which is normally of concern to many end-users of the technology. In-process automated inspection of WAAM, deployed after deposition of every few layers, adds to the process cost-effectiveness as the early defect detection capability provides the opportunity for the process intervention and taking remedial rework actions reducing the time/material waste.<br/>This work presents a demonstration of the concept of in-process NDE of WAAM using two different modalities: a) a high temperature phased array Ultrasound Testing (UT) roller-probe, and b) a high-temperature flexible Eddy Currents (EC) testing array. The automation cell is composed of two robots, where one is dedicated to the WAAM deposition process and the other to NDE sensor delivery on the WAAM. A titanium WAAM component with a straight geometry was deposited using the plasma-arc process and oscillation strategy, where the deposition path and process parameters were controlled by software. Intentionally-embedded tungsten tube and ball reflectors of varying sizes/orientations were inserted in between different WAAM layers to assess the in-process detectability of each of the employed NDE modalities. Full external control of the sensor-enabled adaptive motion control for the NDE robot and the integrated UT and EC array controllers and array probes were achieved through a central program developed in the LabVIEW platform. Moreover, real-time robot motion corrections, driven by the Force-Torque sensor feedback, were established to adjust the contact force and orientation of the sensors to the component surface during the scan. The high-temperature (up to 350 °C), dry-coupled UT roller-probe inspection of WAAM was conducted in-process at the dwelling time between the layers while the surface was at a maximum temp of 130C. Subsequently, EC scan was also carried out in the dwell time at high temperature. The C-scans were produced live from both UT and EC arrays demonstrating the successful detection of embedded tungsten defects with high SNRs. The WAAM component was X-ray CT scanned after production to confirm the exact location if the defects and compare it against the other NDE findings.