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A quantitative investigation for deployment of mobile collaborative robots in high-value manufacturing

Autor: Hifi, Amine, Jackson, W., Loukas, C., Shields, M., Poole, A., Mohseni, E., MacLeod, C. N., Dobie, G., Pierce, S. G., O'Hare, T., Munro, G., O'Brian-O'Reilly, J., Vithanage, R. W. K.

Component inspection is often the bottleneck in high-value manufacturing, driving industries like aerospace toward automated inspection technologies. Current systems often employ fixed arm robots, but they lack the flexibility in adapting to new comp

Externí odkaz: http://arxiv.org/abs/2406.06353

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3-Dimensional residual neural architecture search for ultrasonic defect detection

Autor: McKnight, Shaun, MacKinnon, Christopher, Pierce, S. Gareth, Mohseni, Ehsan, Tunukovic, Vedran, MacLeod, Charles N., Vithanage, Randika K. W., OHare, Tom

This study presents a deep learning methodology using 3-dimensional (3D) convolutional neural networks to detect defects in carbon fiber reinforced polymer composites through volumetric ultrasonic testing data. Acquiring large amounts of ultrasonic t

Externí odkaz: http://arxiv.org/abs/2311.01867

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GANs and alternative methods of synthetic noise generation for domain adaption of defect classification of Non-destructive ultrasonic testing

Autor: McKnight, Shaun, Pierce, S. Gareth, Mohseni, Ehsan, MacKinnon, Christopher, MacLeod, Charles, OHare, Tom, Loukas, Charalampos

This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was repre

Externí odkaz: http://arxiv.org/abs/2306.01469

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