Huiqi Lu

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University of Oxford
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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.

Year:

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

Year:

2022

Year:

2022

Collaborators (5)

Foteini Kavvoura

University of Oxford

UNITED KINGDOM
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Ping Lu

Assistant Professor in Computer Science

University of Leeds

UNITED KINGDOM
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Jane Hirst

University of Oxford

UNITED KINGDOM
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Dr. Vinod Kumar Chauhan

University of Oxford

UNITED KINGDOM
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Maxine Whelan

Coventry University

UNITED KINGDOM
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