Simulation of laser based processing of polycrystalline composites combining analytical and machine learning models - Ref: MPUF2018

Laser milling has recently gained interest in industry for finishing ultra-hard cutting tools, especially in the automotive field. In laser milling, different combinations of materials properties and machining parameters lead to various topographical profiles and the actual removed volume needs to be estimated accurately to achieve accurate simulations. For polycrystalline composites, due to the complex microstructure of grains/binder, the laser milling optimisation process is still based on trial and error experiments, resulting in a not efficient and limited use in industry.

Researchers have been investigating various way of predicting the laser milling process for monocrystalline materials. However, for polycrystalline composites analytical models representing accurately the physical behaviour of thermal ablation process remain to be developed. This project aims at developing a 3D geometrical simulation tool for laser ablation of polycrystalline ultra-hard material.
The successful candidate is expected to develop a novel model which combines machine learning models with more conventional thermal analytical models, populate the necessary databases from past and new experiments and to characterise the milled surfaces with various techniques.

This research will benefit from direct involvement of industrial partners including ultra-hard material and cutting tool manufacturers, helping the candidate to build a strong understanding of the machining involved in automotive and aerospace industries.

Entry requirements
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Manufacturing/Materials Engineering or Computer Science.

A relevant Master’s degree and/or experience in one or more of the following will be an advantage: laser processing, artificial intelligence and modelling.

How to apply
All applications should be made online. Under programme name select Mechanical and Manufacturing Engineering. Please quote reference number: MPUF2018