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An analysis of automation scenarios in an aerospace environmental testing facility

An analysis of automation scenarios in an aerospace environmental testing facility

Ramchurn, Adesh, El Souri, Mohammed ORCID: 0000-0002-9651-7770, Gao, James ORCID: 0000-0001-5625-3654, Le, Chi Hieu ORCID: 0000-0002-5168-2297 and Benetier, Julien (2019) An analysis of automation scenarios in an aerospace environmental testing facility. In: Advances in Manufacturing Technology XXXIII. Advances in Transdisciplinary Engineering, 9 . IOS Press, pp. 93-98. ISBN 978-1643680088 ISSN 2352-751X (Print), 2352-7528 (Online) (doi:

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This paper presents a real-life example of using industrial robots to automate the testing procedures of aerospace products. Three different scenarios are proposed as potential solutions to allow a Mobile Manipulator to help automate the environmental testing process. An overview of different simulation and Off Line Programming (OLP) software packages are presented and discussed as methods to test and validate the scenarios. A framework is presented consisting of a product data model to be used for the identification of the different products and a unified modelling system is introduced to allow information exchange at the operational level. The focus of this paper and future work is towards integrating all the elements of the proposed framework into a human machine interface to create a collaborative process involving human, robots, machines and products.

Item Type: Conference Proceedings
Title of Proceedings: Advances in Manufacturing Technology XXXIII
Uncontrolled Keywords: design for automation, industrial robots, testing, aerospace, simulation
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TS Manufactures
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
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Last Modified: 03 Feb 2021 17:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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