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Lozano Torrubia, Pablo (2016) Stochastic modelling of abrasive waterjet controlled-depth machining. PhD thesis, University of Nottingham.

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Abstract

Abrasive micro-waterjet processing is a non-conventional machining method that can be used to manufacture complex shapes in difficult-to-cut materials. The constant development of new materials with enhanced properties has sparked the interest in alternative machining technologies. Among these non-conventional machining methods, Abrasive Waterjet Machining is regarded as a flexible technology with potential to cope with a wide range of materials and applications. The use of a soft tool (i.e. the jet) is very advantageous because it makes it possible to perform different operations without modifying the equipment. However, this advantage poses a significant challenge: the erosion power of the jet is controlled through a set of operating parameters, and it is therefore necessary to have a deep understanding of the relation between such parameters and the effect of the jet on the surface. The process itself is subject to strong random variations, and this makes it even more complex to develop a detailed understanding of the optimum strategies to control the jet.

The main objective of this thesis is to develop mathematical frameworks that account for the stochastic nature of the process, and that have the capability to predict detailed statistical information of the eroded surfaces for different operating parameters. This is addressed with two modelling approaches: a finite element model where the system is regarded as a set of multiple particles hitting a workpiece at very high speed, and a geometrical model built on the idea of considering the abrasive waterjet as a generic energy beam and exploiting the geometrical properties of the system.

In the Finite Element approach, a modelling framework with the capability of predicting the average shape of abrasive waterjet machined footprints and the variability along the trench has been developed for the first time. This is achieved by combining finite element analysis and Monte Carlo methods, and the model is validated at different feed speeds and tilt angles. The random nature of the system is included by considering the input parameters (i.e. size, relative orientation, or position within the jet of the abrasive particles) as random variables with associated probability distributions.

The geometrical approach is a method to predict the variability of the jet footprint for different jet feed speeds. Since the objective is to incorporate the stochastic nature of the system in the model, a stochastic partial differential equation is used to describe the machined surface as the jet is moved over it. This framework is greatly advantageous because it can be used to make fast predictions of the variability of the trench profiles (to within <8%), and it can therefore be implemented into CAD/CAM packages.

The modelling work, focused on the understanding of how the operating parameters changes the effect of the jet on the surface, is accompanied by an experimental study to uncover how the material properties of the workpiece will affect the erosion process. This is carried out by machining trenches on model materials (i.e. materials with the same chemical composition, but different grain size), and performing and in-depth analysis of the material response, which shows how the machining process has a strong impact on the microstructure of the target material.

The work developed in this thesis contributes to the understanding of the erosion process during abrasive waterjet machining and how stochastic methods can be used to enhance the current capabilities of this technology.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Axinte, D.A. Billingham, J.
Keywords:	Water jet cutting, Machining
Subjects:	T Technology > TJ Mechanical engineering and machinery
Faculties/Schools:	UK Campuses > Faculty of Engineering
Item ID:	35119
Depositing User:	Lozano Torrubia, Pablo
Date Deposited:	15 Oct 2016 06:40
Last Modified:	18 Oct 2017 00:42
URI:	https://eprints.nottingham.ac.uk/id/eprint/35119

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