Novel Computational Techniques for Machining Advanced Materials

The aim of the project is to generate novel mathematical techniques and machine learning for investigating cutting tool design and material cutting mechanism for advanced alloys. This will include developing finite element models and sensor based digital twins for various machining scenarios.
The majority of high precision components across various industries are made by machining. It is a process of converting raw material into finished products by removing material using a hard cutting tool. On average, 17% of the total manufacturing cost is associated with cutting tools. This can be as high as 25% for specialist alloys where limited knowledge exists on their machining behaviour. The introduction of new materials and composites requires daily adjustments to the cutting geometries and manufacturing processes which is not viable experimentally. Machining is a complex multiphysics problem where mechanical energy is used for plastically deforming the workpiece material. Majority of the mechanical energy used for cutting transforms into heat leading to thermochemical and thermomechanical tool wear and surface damage. Developing the digital twin of the machining processes can lead to better understanding of material behaviour during machining and facilitate decision making during machining and cutting tool design for optimum productivity.