PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems

Mo, Fan; Ugarte Querejeta, Miriam; Hellewell, Joseph; Rehman, Hamood Ur; Illarramendi Rezabal, Miren; Chaplin, Jack C.; Sanderson, David; Ratchev, Svetan

Abstract

Current approaches to manufacturing must evolve to respond to increasing demands for short product life cycles and customised products. Adaptive manufacturing systems integrate advanced technologies, automation, and data-driven methodologies to develop adaptable, efficient, and responsive production processes. Central to this concept is the emphasis on human involvement and fostering synergy between human operators and the manufacturing system. Significant changes to the system's controller are required to achieve adaptivity, with programmable logic controllers (PLCs) being a common controller type. After the necessary changes to the configuration of the manufacturing system, the PLC should be reconfigured to orchestrate the new required behaviour. Automated reconfiguration is vital to rapidly responding to change, but some changes cannot be entirely achieved without human input in collaboration with automated methods. Conventional practices in PLC programming include manual, repetitive coding practices subject to errors. As a result, to ensure operational safety, the changes must be tested before being deployed to operations, ensuring it is error-free. This paper presents a methodology to automatically reconfigure the simulation environment and controller in response to a new product request. We automate the PLC code generation and testing practices to support and free up the operators when performing repetitive manufacturing reconfiguration tasks. The methodology is based on human learning, software automation, customised program development, knowledge graphs, and Graph Neural Networks (GNNs). The presented solution is a generic, vendor-agnostic, and interoperable solution that facilitates information exchange among multiple heterogeneous environments. Lastly, we have validated the methodology as a proof of concept at an adaptive assembly cell at the University of Nottingham in the United Kingdom.

Citation

Mo, F., Ugarte Querejeta, M., Hellewell, J., Rehman, H. U., Illarramendi Rezabal, M., Chaplin, J. C., …Ratchev, S. (2023). PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems. Journal of Manufacturing Systems, 71, 172-187. https://doi.org/10.1016/j.jmsy.2023.07.015

• Journal Article Type: Article
• Acceptance Date: Jul 25, 2023
• Online Publication Date: Sep 19, 2023
• Publication Date: 2023-12
• Deposit Date: Oct 5, 2023
• Publicly Available Date: Oct 5, 2023
• Journal: Journal of Manufacturing Systems
• Print ISSN: 0278-6125
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 71
• Pages: 172-187
• DOI: https://doi.org/10.1016/j.jmsy.2023.07.015
• Keywords: PLC; Automatic program generation; Structural testing; Adaptive manufacturing systems; Industry 4.0; Knowledge graph; Graph neural network
• Public URL: https://nottingham-repository.worktribe.com/output/25393876
• Additional Information: This article is maintained by: Elsevier; Article Title: PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems; Journal Title: Journal of Manufacturing Systems; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jmsy.2023.07.015; Content Type: article; Copyright: © 2023 The Author(s). Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.