Automated experience-based learning for plug and produce assembly systems

Scrimeri, Daniele; Antzoulatos, Nikolas; Castro, Elkin; Ratchev, Svetan M.

Abstract

This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.

Citation

Scrimeri, D., Antzoulatos, N., Castro, E., & Ratchev, S. M. (2017). Automated experience-based learning for plug and produce assembly systems. International Journal of Production Research, 55(13), 3674-3685. https://doi.org/10.1080/00207543.2016.1207817

• Journal Article Type: Article
• Acceptance Date: Jun 19, 2016
• Online Publication Date: Jul 14, 2016
• Publication Date: Jan 1, 2017
• Deposit Date: Aug 29, 2018
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 13
• Pages: 3674-3685
• DOI: https://doi.org/10.1080/00207543.2016.1207817
• Keywords: Plug-and-produce, Adaptation, Automated assembly, Learning, Knowledge engineering
• Public URL: https://nottingham-repository.worktribe.com/output/1115895
• Publisher URL: https://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1207817
• Related Public URLs: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978488704&partnerID=40&md5=63f65809326ba90960b3b2b866209dc2