Pint of Robotics: Anna-Grace Linton, Dan Davie, and the University of the West Indies

Speaker 1: Anna-Grace Linton (Centre for Doctoral Training in AI for Medical Diagnosis and Care at the University of Leeds)

Title: Natural Language Processing methods to explore responses to open-ended questions in Patient-Reported Outcome Measures (PROMs)

Bio: PhD student in the Centre for Doctoral Training in AI for Medical Diagnosis and Care at the University of Leeds. Her research focuses on using Natural Language Processing methods to explore responses to open-ended questions in Patient-Reported Outcome Measures (PROMs). This analysis aims to gain deeper insights into the health-related quality of life experienced by cancer patients.

Speaker 2: Dan Davie (Future Manufacturing Processes, School of Mechanical Engineering, University of Leeds).

Title: Process Development and Control for Sensor-informed Hybrid-Additive Manufacturing

Bio: Dan obtained a Master’s degree in Mechanical Engineering in 2021 from the University of Leeds. His extended project involved the design and construction of a mobile robot for direct ink writing (DIW) of large-area printed electronics. Now, he is in the final year of a PhD, under Professor Robert Kay, in the Future Manufacturing Processes group, developing a hybrid-additive process for technical ceramics. Dan’s interests are process integration, design, and control, applications of functional ceramics.

Abstract: Hybrid-Additive Manufacturing (hybrid-AM) systems present means to produce high quality components by leveraging the advantages of multiple distinct process in a single manufacturing platform. However, novel process combinations create challenges for machine control due to the lack of machine-agnostic hybrid manufacturing software. This work presents improvements to a hybrid-AM platform for technical ceramics, incorporating multi-material (polymer filament and ceramic paste) extrusion, infra-red drying, green machining, and multi-modal sensing. A bespoke software package was created to facilitate control of the distinct technologies and sensing operations using a single production file. Sensor data is used for process calibration and dynamic process parameter adjustment, increasing the systems intelligence. For example, layer height measurements using a laser profilometer are used to tune the paste extrusion parameters. This enabled the integrated fabrication of ceramic components with sub-mm precision, complex geometries and less than 5% fabrication porosity, creating new manufacturing opportunities in the technical ceramics market.

Mentors:  Lecturers of the Department of Computing at the University of the West Indies, Mona, Jamaica.

• Sean Miller,
• Dr. Carl Beckford (PhD),
• Dr. Ricardo Anderson (PhD).