Our Team > Our Researchers > Dr Zhiwei Zhao

Dr Zhiwei Zhao

Our Researchers

Research Fellow

Queen’s University Belfast

Zhiwei graduated with a PhD from college of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics in 2022.

His major is aerospace manufacturing engineering, and his PhD project titled “Deformation control method driven by deformation force monitoring data for large-scale structural parts”, in which a new physical quantity is proposed to infer the global residual stress field and a model of fusion of data and causal knowledge is proposed to determine the machining process. His research interests focus on intelligent manufacturing using data-driven method, including deep learning, causal inference, Finite element method and so on.

Zhiwei joined Queen’s University Belfast as a Research Fellow in 2022, working within Thread 4.

RIED Specific Links & Papers

  • 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.