School of Computing, Engineering and Technology

Smart and Sustainable Manufacturing of Advanced Materials using Additive and Subtractive Processes

This research explores advanced manufacturing by integrating additive and subtractive processes for high-performance materials. This project aims to advance precision manufacturing through sensor-based monitoring, adaptive control, and AI-driven optimisation, improving cutting performance and sustainability in high-value manufacturing applications.

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

Summary

The use of additively manufactured components for high-value engineering applications is gaining interest due to their ability to produce complex geometries and lightweight parts. However, their post-processing and superior strength present machining challenges to achieve the desired accuracy and process outputs. This research project aims to adopt innovative methods to improve the machinability of advanced materials by developing new approaches and innovative tools to enhance sustainability, precision, and manufacturing performance.

Objectives

The main objectives of this research are to:

Investigate the cutting mechanics of conventional and additively manufactured materials using innovative tooling approaches.
Integrate real-time process monitoring through embedded sensors to capture key machining data.
Develop data-driven strategies to evaluate and optimise cutting performance using analytics and machine learning techniques.
Assess the energy consumption and environmental impact of machining processes through a comprehensive life cycle assessment.

The project employs a combination of numerical and experimental approaches to investigate the complex tool–workpiece interactions in advanced material machining. This involves the use of CNC machines equipped with smart sensors to collect key process data, enabling detailed characterisation of process mechanics. The collected data will also support evaluation of environmental impacts and energy consumption through a comprehensive life cycle assessment (LCA), guiding the development of sustainable practices and technological interventions to minimise environmental impact, reduce energy usage, and enhance overall machining sustainability. Additionally, statistical analysis and AI-based modelling will be applied to optimise machining parameters and improve process efficiency.

This opportunity is suitable for undergraduate or postgraduate students in mechanical, manufacturing, or materials engineering. The ideal candidate should have an interest in advanced material science, machining processes, digital manufacturing, or smart systems. Experience with CAD/CAM, data analysis (e.g., MATLAB or Python), or experimental work would be advantageous but not essential.

Expected outcomes

RGU provides the facilities to carry out this research entirely in-house. The outcomes are expected to include publications in high-quality journals and conferences, while also contributing directly to the UK manufacturing industry by improving machinability and sustainability performance. Beyond disseminating core research findings, this project will support current and future collaborations with industry partners.