French, R., Benakis, M. and Marin-Reyes, H. orcid.org/0000-0002-2919-5388 (2018) Intelligent Sensing for Robotic Re-Manufacturing in Aerospace - An Industry 4.0 Design Based Prototype. In: 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS),. 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 05-07 Oct 2017, Ottawa, Canada. IEEE , pp. 272-277. ISBN 978-1-5386-1342-9
Abstract
Emerging through an industry-academia collaboration between the University of Sheffield and VBC Instrument Engineering Ltd, a proposed robotic solution for remanufacturing of jet engine compressor blades is under ongoing development, producing the first tangible results for evaluation. Having successfully overcome concept adaptation, funding mechanisms, design processes, with research and development trials, the stage of concept optimization and end-user application has commenced. A variety of new challenges is emerging, with multiple parameters requiring control and intelligence. An interlinked collaboration between operational controllers, Quality Assurance (QA) and Quality Control (QC) systems, databases, safety and monitoring systems, is creating a complex network, transforming the traditional manual re-manufacturing method to an advanced intelligent modern smart-factory. Incorporating machine vision systems for characterization, inspection and fault detection, alongside advanced real-time sensor data acquisition for monitoring and evaluating the welding process, a huge amount of valuable industrial data is produced. Information regarding each individual blade is combined with data acquired from the system, embedding data analytics and the concept of ìInternet of Thingsî (IoT) into the aerospace re-manufacturing industry. The aim of this paper is to give a first insight into the challenges of the development of an Industry 4.0 prototype system and an evaluation of first results of the operational prototype.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Robot sensing systems; Non-Destructive Testing; Manufacturing automation; Additive Manufacturing; Welding |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Funding Information: | Funder Grant number INNOVATE UK (TSB) UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Nov 2017 10:54 |
Last Modified: | 02 Feb 2018 10:07 |
Published Version: | https://doi.org/10.1109/IRIS.2017.8250134 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/IRIS.2017.8250134 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124274 |