Arman Mozaffary

Process Informed Material Model for Digitalisation of Subtractive Manufacturing Operations

Supervised by: Dr Hassan Ghadbeigi, Dr Joshua Priest, Dr Anders Liljerehn, Dr Ashwin Devotta 

Sponsored by: Sandvik Cormorant 

I chose the EngD because it combines advanced research with real-world industry experience. Unlike a traditional PhD, it allows me to work on practical challenges with direct impact, while still developing the depth of expertise expected at doctoral level. With the support of sponsorship, I can focus on creating innovative solutions that not only advance academic knowledge but also benefit industry and society.

The aim of my research is to develop a reliable digital model that captures material deformation during the turning process. By doing so, I hope to better understand the underlying mechanics of machining and create predictive tools that can accurately predict the material deformation of workpiece and tool under specific machining conditions.

My research is exciting because it focuses on developing a digital model to better understand how materials deform during the machining process, specifically turning. This approach is new because it allows us to predict and optimize machining performance without relying only on costly and carbon-intensive trial-and-error experiments. By improving accuracy in modelling, the project helps reduce material waste, lower carbon emissions, and cut overall machining costs - making the process more efficient, sustainable, and industry-ready.

Ultimately, I want to build a new and accurate digital modelling of mechanical properties deformation during high speed and high temperature turning process. which has directly impact on reducing material waste, lower carbon emissions, and decrease the cost of machining while maintaining high-quality results.