Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Methods and tools for the verification of plug-and-produce robots in distributed manufacturing systems


   Faculty of Engineering & Digital Technologies

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The competitiveness of manufacturing companies in modern markets increasingly depends on cost-effective flexible automation technologies. Reconfigurable manufacturing systems can accommodate changes to introduce new product variants, adjust production capacity or recover from disruptions. Robots, inspection devices, material handling units and other pieces of equipment can be added to the production system, changed or removed. This level of flexibility requires extensive human intervention and, therefore, it is time-consuming and expensive.

Plug-and-produce systems introduced a significant degree of autonomy. New modules can be “plugged” into a distributed production system and be automatically integrated and configured. However, the verification of such systems is challenging due to their dynamic architecture and the high variability of possible hardware and software configurations. Furthermore, it is highly desirable to test accurately as much as possible offline, i.e. in a simulation environment, without operating the real robots.

Plug-and-produce systems enable collaboration even between heterogeneous components, which must trust each other in order to cooperate. Ensuring a reliable cooperation in these reconfigurable systems is the key to unlocking their full potential and enabling industries to develop business models confidently.

This project aims to develop novel methods and software tools for:

  1. Automated generation of test cases for plug-and-produce robots in distributed manufacturing systems.
  2. Offline simulation of plug-and-produce behaviour.
  3. Analysis of the reliability of plug-and-produce robots in dynamic architectures.

It is anticipated that the automated generation of test cases will use model-based and search-based software testing techniques.

How to apply

Formal applications can be made via the University of Bradford web site.

Computer Science (8) Engineering (12)

Funding Notes

This is a self-funded PhD project; applicants will be expected to pay their own fees or have a suitable source of third-party funding. A bench fee may also apply to this project, in addition to the tuition fees. UK students may be able to apply for a Doctoral Loan from Student Finance for financial support.

References

1. Daniele Scrimieri, Omar Adalat, Shukri Afazov, and Svetan Ratchev. An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems. The International Journal of Advanced Manufacturing Technology, 2022, https://doi.org/10.1007/s00170-022-10553-0.
2. S. Kabir, I. Sorokos, K. Aslansefat, Y. Papadopoulos, Y. Gheraibia, J. Reich, M. Saimler, and R. Wei, “A Runtime Safety Analysis Concept for Open Adaptive Systems,” in Model-Based Safety and Assessment, Springer, Cham, 2019, pp. 332–346.
3. R. Calinescu, D. Weyns, S. Gerasimou, M. U. Iftikhar, I. Habli, and T. Kelly, “Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases,” IEEE Trans. Softw. Eng., vol. 44, no. 11, pp. 1039–1069, Nov. 2018.
4. Daniele Scrimieri, Nikolas Antzoulatos, Elkin Castro, and Svetan M. Ratchev. Automated experience-based learning for plug and produce assembly systems. International Journal of Production Research, 55(13):3674–3685, 2017.

Register your interest for this project



Where will I study?

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.