Correlation between Machining Monitoring Signals, Cutting Tool Wear and Surface Integrity on High Strength Titanium Alloys.

It is widely accepted that tool wear has a direct impact on a machining process, playing a key part in surface integrity, part quality, and therefore, process efficiency. By establishing the state of a tool during a machining process, it should be possible to estimate both the surface properties and the optimal process parameters, while allowing intelligent predictions about the future state of the process to be made; thus ultimately reducing unexpected component damage. This thesis intends to address the problem of tool wear prediction during machining where wear rates vary between components; for instance, due to the relatively large size of the component forging and, therefore, inherent material variations when compared to existing research. In this case, the industrial partner, Safran Landing Systems, is interested in the ability to predict tool wear during the finish milling of large, curved, titanium components, despite differing material properties and, therefore, tool wear rates. This thesis is split into four key parts, the first of which describes in detail the formulation and implementation of an experimental procedure, intended to provide a working set of industrially representative monitoring data that can be used throughout the remainder of the thesis. This part includes development of a relevant machining strategy, material specimen extraction, sensor selection and placement, and 3D tool geometry measurement, all of which have been completed at industrial partners facilities. It finishes with a preliminary investigation into the data collected during the machining process from the tools, material specimens, and sensors placed in close proximity to the cutting zone. The second, third, and fourth parts follow logically from one-another, beginning with a state classification problem, and ending with a full dynamic model prediction of wear during the machining of large landing gear components; this method, however, is applicable to many other machining scenarios using the new technique applied in this thesis. The state classification chapter is a necessary first step in developing a predictive model as is aims to prove the data is indeed separable based upon the generating wear state. Once confirmed, given the sequential nature of tool wear, the order of observations can be included in the modelling, in an attempt to improve classification accuracy. This forms the basis of the state tracking chapter, and leads naturally into the full dynamic model prediction in the final part. This is a promising result for the machining community, as process monitoring often relies on operator expertise to detect wear rate fluctuations and, in turn, results in over-conservative tool usage limits, adding time and expense to many complex machining processes. It also presents the opportunity to predict part quality through pre-existing relationships between the acquired signals and material surface finish - correlations which are explored and presented as part of this thesis. The solution to predicting a varying wear rate within a harsh machining environment introduced in this thesis is based around the application of a Gaussian process (GP) NARX (Nonlinear Auto-Regressive with eXogenous inputs) model borrowed from the machine learning prediction and, more recently, structural health monitoring (SHM) communities. The GP-NARX approach is found to be well suited to the application of wear prediction during machining, and forms a promising contribution to the development of autonomous manufacturing processes.