Deep learning based visual automated sorting system for remanufacturing

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Nwankpa, Chigozie E. and Eze, Solomon C. and Ijomah, Winifred I.; (2020) Deep learning based visual automated sorting system for remanufacturing. In: 2020 IEEE Green Technologies Conference (GreenTech). IEEE, USA, pp. 196-198. ISBN 9781728150178 (https://doi.org/10.1109/GreenTech46478.2020.928982...)

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Abstract

Remanufacturing is a crucial component of the circular economy concept which emphasises sustainable consumption habits. This study proposes a novel automated sorting system for remanufacturing which is based on deep convolutional neural networks(CNN). To demonstrate its applicability, the proposed deep learning (DL) system was used to distinguish among dry, wet, oily and defected surfaces. The test was conducted on four locally sourced 3" x 6" plates. Sample image data were captured using a USB webcam. The network training was done with 75% of the data while the balance data were used for testing. In this preliminary study, the DCNN classified the features with up to 99.74% accuracy on validation data and above 96% accuracy on live video feed; demonstrating that it can accurately sort components. This study is the first to propose a low-cost sorting system for remanufacturing based on the deep CNN and logic gates. The results show that the method is an accurate, reliable, cost-effective and fast technique that can potentially outperform existing sorting systems in the remanufacturing industry.

  • Item type:Book Section
    ID code:75229
    Dates:
    Date
    Event
    16 December 2020
    Published
    4 November 2019
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Manufactures
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:28 Jan 2021 17:21
    Last modified:12 Dec 2024 01:26
    URI:https://strathprints.strath.ac.uk/id/eprint/75229
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