Prof Yan Jin

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.

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.

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.

This has been published in the “Engineering” on-line Journal, accessible on ScienceDirect.

Engineering | Journal | ScienceDirect.com by Elsevier

Abstract

Controlling machining deformations resulting from unbalanced stress fields inside structural components is a significant challenge in the manufacturing industry. Prediction of machining deformation fields is fundamental for deformation control and requires numerous iterations to optimize the machining process. Conventional prediction methods such as numerical analysis are tailored to a fixed geometry, making them time-consuming and inefficient for components with various geometries. In this study, a general data-driven model is proposed for predicting machining deformation fields in components with varying geometries and stress fields. This model is based on a geometry-oriented neural operator that incorporates global geometry information into the function space, modeling the relationship between the input function (stress fields) and the output function (deformation fields). Global geometric information is extracted using a graph neural network applied to a geometric graph and embedded into the input and output function space through an encoder-query framework. The proposed model achieved low root-mean-squared errors ranging from 0.001 to 0.016 mm, with maximum prediction errors between 0.003and 0.047 mm across different types of components, including beams and frames. The main contribution of this research is the significant advancement in the application of neural operators to the development of general models for predicting machining deformation. The underlying principles of the proposed model provide an important reference for wider applications related to the control of machining deformation in the context of digital and intelligent manufacturing.

This paper was presented by Caitlin Sands at the 9th International Workshop on New Trends in Medical and Science Robotics (MESROB 2025) Conference in Poitiers, France July 2-4. The paper can be found in the Proceedings accessible via New Trends in Medical and Service Robotics: MESROB 2025 | SpringerLink. The paper itself can also be found via https://link.springer.com/chapter/10.1007/978-3-031-96081-9_4

Abstract. Due to the high demand for ophthalmic surgery and a shortage of surgeons, robot-assisted surgical systems have attracted attention from research communities for their potential to provide superior accuracy, precision, and motion stability, thus reducing the risk of hand tremors associated with manual surgery. However, existing surgical robotic platforms are less adaptable and efficient than traditional manual procedures. Additionally, the design process for these systems is challenging and time-consuming, requiring specialised expertise from multiple stakeholders. To address these challenges, this paper proposes a framework for applying generative design (GD) methods in developing surgical robots for ophthalmic procedures. It aims to generate diverse, feasible solutions, guided by input requirements and system analyses while facilitating continuous improvements to meet future needs. The paper presents the initial implementation of the proposed framework, which includes the development of a library of link geometries and fundamental 1-degree-of-freedom (DoF) mechanisms. These serve as the building blocks for remote centre of motion (RCM) mechanisms, which are critical for minimally invasive surgery (MIS) applications. The paper discusses the future development of the GD framework, aiming to enhance its capabilities for more complex surgical robotic mechanisms. It focuses on refining the framework to address scalability and validation challenges, ultimately improving design efficiency and adaptability for advanced systems