Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

Applications involving high-power ultrasound are expanding rapidly as ultrasonic intensification opportunities are identified in new fields. This is facilitated through new technological developments and an evolution of current systems to tackle challenging problems. It is therefore important to continually update both the scientific and commercial communities on current system performance and limitations. To achieve this objective, this paper addresses two key aspects of high-power ultrasonic systems. In the first part, the review of high-power applications focuses on industrial applications and documents the developing technology from its early cleaning applications through to the advanced sonochemistry, cutting, and water treatment applications used today. The second part provides a comprehensive overview of measurement techniques used in conjunction with high-power ultrasonic systems. This is an important and evolving field which enables design and process engineers to optimize the behavior and/or operation of key metrics of system performance, such as field distribution or cavitation intensity.

This version is available at https://strathprints.strath.ac.uk/51770/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. RSCPublishing COMMUNICATIONThis journal is © The Royal Society of Chemistry 2012 J. Name., 2012, 00, 1-3 | 1 Ultrasound, i.e. high frequency oscillating pressure waves, is commonly used to overcome kinetic barriers associated with dissolution, assembly and gelation. We demonstrate that ultrasound energy may also be used to achieve transient reorganization of supramolecular nanostructures, which revert back to the original state when sound is switched off. Aromatic peptide amphiphiles, Fmoc-FL and -YL were used to study the transient acoustic response. These systems showed temporary supramolecular transitions that were sequence dependent. The changes observed were due to an altered balance between Hbonding and -stacking, giving rise in changes in chiral organisation of peptide building blocks. Transient reconfiguration was visualized by TEM and changes in supramolecular interactions characterized by fluorescence, FT-IR and CD. Remarkably, significant differences are observed when compared to thermal heating, which shows relates to the oscillating and directional characteristics of ultrasound when delivering heat to a system.

We demonstrate an in situ ultrasonic approach to influence self-assembly across the supramolecular to micron length scales, showing enhancement of supramolecular interactions, chirality and orientation, which depends on the peptide sequence and solvent environment. This is the first successful demonstration of using oscillating pressure waves to generate anisotropic organo- and hydrogels consisting of oriented tripeptides structures.

Imaging defects in austenitic welds presents a significant challenge for the ultrasonic non-destructive testing community. Due to the heating process during their manufacture, a dendritic structure develops, exhibiting large grains with locally anisotropic properties which cause the ultrasonic waves to scatter and refract. When basic imaging algorithms, which typically make constant wave speed assumptions, are applied to datasets arising from the inspection of these welds, the resulting defect reconstructions are often distorted and difficult to interpret correctly. However, knowledge of the underlying spatially varying material properties allows correction of the expected wave travel times and thus results in more reliable defect reconstructions. In this paper, an approximation to the underlying, locally anisotropic structure of the weld is constructed from ultrasonic time of flight data. A new forward model of wave front propagation in locally anisotropic media is presented and used within the reversible-jump Markov chain Monte Carlo method to invert for the map of effective grain orientations across different regions of the weld. This forward model and estimated map are then used as the basis for an advanced imaging algorithm and the resulting defect reconstructions exhibit a significant improvement across multiple flaw characterization metrics.