Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595289
Title: Removal of alpha case from a titanium alloy using plain waterjets
Author: Huang, Lei
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2013
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Abstract:
As a promising non-conventional machining method, high-pressure plain waterjet (PWJ) technology has gained consideration for peening, milling and cleaning of advanced engineering materials due to its specific advantages over competitor technologies (e.g. absence of thermal effects, cost-effectiveness, and environmental friendliness). The aerospace industry has a requirement to remove alpha case (formed during heat treatment) from titanium alloys. as alpha case is detrimental to the fatigue life of the components. The existing process for alpha case removal is chemical etching which is costly and environmentally unfriendly. Thus, this research seeks to evaluate the PWJ process as. an innovative method to remove alpha case from a titanium alloy- Ti - 6AI-4V. An additional benefit of removing alpha case using PWJ technology is that PWJ can introduce compressive residual stress in the remaining surface. As an essential step towards a comprehensive understanding of the PW J alpha case removal process and the surface after alpha case removal, the response of the substrate Ti-6A I- 4V under PWJ erosion has to be investigated. Hence, the scope of this research covers material damage mechanisms under PWJ erosion both in plain Ti-6AJ-4V and in Ti- 6AI-4V with an alpha case; the effect of major process parameters on the process performance measures, process modelling. and residual stress introduced by the proposed alpha case removal process are all investigated. The current study has provided a comprehensive view of the development of material damage on Ti-6AI-4V under PWJ impingement. Damage and material removal along alpha grain boundaries are found to be the dominant initial damage modes. A detailed experimental investigation is then presented which examines the role of a number of process parameters on the removal depth, material removal rate, and surface finish in the PWJ erosion of Ti-6AI-4V. The independent effects (as well as coupled effects) on all the identified process outcomes in both PWJ linear and area erosion have been demonstrated . A numerical model is established to predict the depth of PWJ machined tracks based upon impinged energy density. It is also shown that an increase in depth of material removed is associated with increasing surface roughness, irrespective of process parameter settings. The study of residual stress on PWJ-treated Abstract Ti-6AI-4V surfaces shows that the development of high levels of compressive stress on Ti-6AI-4V is associated with initial damage while the maximum compressive stress is reached when a proportion of the original surface has been removed. Based on the knowledge established through processing of Ti-6A1-4V, a detailed experimental investigation on alpha case removal was then carried out. It has been found that the process parameters affect the alpha case removal process in a similar way to that in PWJ erosion of Ti-6AI-4V. However. the material damage mechanisms of the alpha case contaminated Ti-6A I-4V are more complex, including brittle fracture as the initial damage mode, which then changes to ductile fracture after the most brittle surface layer has been completely removed. The results also reveal apparent proportional correlation between the removal depth and the resulting surface roughness. The compressive residual stress introduced into the material during PWJ impingement reaches the maximum when only a portion of the alpha case has been removed, implying that alpha case may be not need to be fully removed for a significant enhancement in the fatigue life to be realized. An artificial neural network (ANN) approach has been proposed to predict the performance measures in the PWJ alpha case removal process due to its complexity. The ANN model has verified statistically by assessing the model predictions with respect to the corresponding experimental data. The influence of the amount of model input data on the prediction performance has also been investigated. The results show that the proposed ANN approach can adequately predict the process performance measures in removal of small scale alpha case using PW 1. It is also shown that it is possible to reduce the number of required training data without compromising the prediction performance through using a Proper design of experiments. The knowledge established from this research can be app lied not only to PWJ alpha case removal, but also to other potential PWJ surface treatment applications, such as PWJ roughening of surfaces of medical implants or surfaces where further coating will be applied.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.595289  DOI: Not available
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