Prof Jin is a professor of Smart Manufacturing and Robotics in the School of Mechanical and Aerospace Engineering at Queen’s University Belfast. He has been working in the school as a research fellow (2007-08), a lecturer (2009-15), and a senior lecturer (2015-18). He obtained both his Bachelor and Master degree in mechanical engineering in Dalian University of Technology China, in 1998 and 2002 respectively, and received his PhD from Nanyang Technological University Singapore in 2007. Prof Jin’s research interest is in parallel kinematic machines, robotics, digital lean and green manufacturing, high performance machining, and production management. He has been a principal investigator or co-investigator for a number of research programmes, funded by DTI, EPSRC, Innovate UK, Royal Academy of Engineering, EU H2020, Invest Northern Ireland and industry (e.g. EADS/Airbus and Bombardier Aerospace Belfast). He is a member of the prestigious EPSRC Early Career Forum in Manufacturing Research, a Marie Skłodowska-Curie fellow, and a member of technical committee of UK-RAS network. He is an associate editor of IMechE Journal of Engineering Manufacture and an editorial board member of Chinese Journal of Mechanical Engineering. He is general chair of ICMR2019 and Parallel2020 international conferences, and has been invited in the organising/programme committee of 20+ international conferences ICRA2017 and ICIEA2018. Prof. Jin has published over 100 peer-reviewed technical papers. He is a chartered engineer, chair of robotics committee in UK IFToMM, board member of IMechE MICG, and a member of IMechE and IEEE.
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Prof Yan Jin
RIED Specific Links & Papers
A review of design frameworks for human-cyber-physical systems moving from industry 4 to 5 (September 2023)
Published in the IET Cyber-Physical Systems: Theory and Applications Journal
Within the Industry 4.0 landscape, humans collaborate with cyber and physical elements to form human-cyber-physical systems (HCPS). These environments are increasingly complex and challenging workspaces due to increasing levels of automation and data availability. An effective system design requires suitable frameworks that consider human activities and needs whilst supporting overall system efficacy.
Although several reviews of frameworks for technology were identified, none of these focused on the human in the system (moving towards Industry 5). The critical literature review presented provides a summary of HCPS frameworks, maps the considerations for a human in HCPS, and provides insight for future framework and system development. The challenges, recommendations, and areas for further research are discussed.
A bio-inspired evolution-development method for modelling and optimisation of buffer allocation in unreliable serial production line (December 2023)
This paper was presented at the 2023 3rd International Conference on Mechanical, Aerospace and Automotive Engineering (CMAAE 2023) held in Nanjing, China.
A buffer is an important element in a production line and its allocation influences the throughput and inventory of the line. The buffer allocation problem can be framed as a multi-objective optimisation problem and is often addressed by using meta-heuristic algorithms, such as evolutionary algorithms. However, these algorithms primarily focus on the “genetic evolution” aspect and do not take in to account the impact of the biological “organism development” process, potentially constraining the exploration of the solution space. In this paper, a bio-inspired evolution-development (evo-devo) approach for modelling and optimising buffer allocations is proposed. The organism representing a production line is defined and modelled, and the evolution and development processes of organisms are developed for researching optimised solutions. The method has been validated by a simulation of a buffer allocation optimisation in an unreliable serial production line with multi-objectives, aiming to maximize production throughput and minimize the total buffer size. Results show that the
proposed method can efficiently obtain solutions, while also achieving greater exploration of the solution space than competing evolutionary algorithms such as the Non-Dominated Sorting Genetic Algorithm II. The proposed approach’s functionality means that it could be applied to other areas of generative design of future factories.
Human Robot Collaboration (HRC) Taxonomy (June 2022)
Conference Publication by Laura McGirr in June 2022 at the ISR EU2022 Conference in Munich, Germany.
The goal of the research outlined in this paper is to facilitate improved communication and
enable co-ordinated research collaboration across academic researchers and industry in the implementation of HRC installations.