Automated PLC Code Generation in the Connected Morphing Factory
Current manufacturing automation paradigms rely on economies of scale to justify the cost, time, and manual effort involved in developing, programming, testing, and validated automation control systems. Advanced manufacturing systems in the vein of "Industry 4.0" or similar are intended to offer much shorter production lifecycles, and/or much smaller batch sizes, even down to batch-size-of-one. This is a challenge for existing automation control solutions, which rely on production engineers, automation engineers, or programmers to customise and configure them for each production process. The cost, time, and effort required for this currently manual process can be significantly reduced through the use of automated code generation techniques. By automatically generating control code for PLCs, production systems can be made more flexible, reconfigurable, and adaptive and thereby more able to accommodate changes to the production process in shorter time. This paper presents a method for automated PLC code generation designed to enable the orchestration of production operations in an automated production system. This method is validated against a robotic inspection process running on real industrial hardware in the Omnifactory demonstrator at the University of Nottingham. It is shown to successfully generate correct PLC code in significantly less time than a manual programmer, using a variety of inputs corresponding to different process variables.
Authors
- Joseph S. Hellewell (ORCID: https://orcid.org/0000-0003-1724-6464)
- David Sanderson (ORCID: https://orcid.org/0000-0002-8675-4991)
- Svetan Ratchev (ORCID: https://orcid.org/0000-0001-9955-2806)
- Zi Wang (ORCID: https://orcid.org/0000-0003-1652-7934)
Institutions
- University of Nottingham (GB)
Publication Details
- Published
- 2025-08-17
- DOI
- https://doi.org/10.1109/case58245.2025.11163764
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00