2026-04-21T08:09:17Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/63532024-04-16T10:43:30Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Ugarte Querejeta, Miriam author Illarramendi, Miren author 2023 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. 0278-6125 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174282 https://hdl.handle.net/20.500.11984/6353 PLC Automatic program generation Structural testing Adaptive manufacturing systems Industry 4.0 Knowledge graph Graph neural network ODS 9 Industria, innovación e infraestructura PLC orchestration automation to enhance human-machine integration in adaptive manufacturing systems