Huiqi Lu
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Recent Grants
Grant: Open
Diabetes UK PhD Studentship
Open Date: 2023-10-01
Close Date: 2027-06-01
Grant: Close
Innovation and Enterprise Fellowship Award
Open Date: 2022-01-01
Close Date: 2023-06-01
Grant: Close
Development and validation of a non-invasive device for measuring oxygen saturation with automatic adjustment according to altitude and skin color using machine learning algorithms
Open Date: 2021-11-01
Close Date: 2022-11-01
Grant: Close
Oxford John Fell Fund for PGRA Grant
Open Date: 2021-09-01
Close Date: 2022-03-01
Grant: Close
Gestational Predictive Monitoring and Management
Open Date: 2021-08-01
Close Date: 2024-02-01
Articles (19)
Year:
2025
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2025
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2025
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2025
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2024
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2024
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2024
Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model’s force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2022
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2022
Year:
2022
Collaborators (5)
Foteini Kavvoura
University of Oxford
Ping Lu
Assistant Professor in Computer Science
University of Leeds
Jane Hirst
University of Oxford
Dr. Vinod Kumar Chauhan
University of Oxford
Maxine Whelan
Coventry University
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