The influence of sheet conditions on the formability of titanium alloys at room temperature

Student thesis: Doctoral Thesis


Titanium is the material of choice for use in most high value engineering components due to their good physical and chemical properties. However, their adoption are hindered by their poor room temperature formability as a result of the nature of their crystallographic structure and their high susceptibility to surface inhomogeneity. The main gaps in knowledge identified were the non-availability of data and research on the impact of machining induced edge conditions on the room temperature forming tendencies of titanium alloys. In addition, the availability of literature on the potential influence of post-processing induced surface defects on titanium sheet forming capabilities at room temperature was scanty. To assess these issues, statistical predictive numerical models and experimental trials have been undertaken in this research work, to examine the influence of titanium sheet conditions on their room temperature formability. The hole expansion test (HET) and uniaxial tensile tests were conducted on titanium sheets machined with either laser, electric discharge machining (EDM) or abrasive water jet (AWJ) machining. The goal was to examine the impact of the machining induced edge surface defects on the edge formability of titanium at room temperature. The research found that titanium edges machined with EDM exhibited high edge forming performance, followed by the AWJ machined edges with the laser machined edges performing the least. The performance trends observed were attributable to the influence of the machining induced edge surface micro feature alterations. Forming parameters such as the machining method, tool geometry, machining parameters and the metallographic properties of titanium were found to influence the edge forming performance of titanium. This research work proposed the GOM Atos metrology as a supplementary method for characterising the edge forming performance of materials, based on the principle of sheet thinning being an indicator of formability. For the uniaxial tensile test trials, the machining methods and machining parameters were found to have no significant impact on the deformation behaviour of titanium. Digital image correlation (DIC) evaluation results showed that the strains were concentrated within the bulk material and away from the machined edges during the tensile deformation process. Also, this research proposed a novel statistical numerical regression model expression for predicting the edge forming performance of titanium alloys for edges prepared with AWJ and deformed with a Nakajima punch. The proposed model expression showed an average absolute error of 8.8% and a correlation coefficient of 0.9884 between the experimental and the predicted values. Sheet metal surface defects in the form of indents and scratches are sometimes introduced during either transportation, storage or handling on the workshop floor. An approach was proposed to compute the localised strain evolution at an indent defect micro-surface of titanium sheets during uniaxial tensile deformation. The approach entails the use of a notched tensile sample, scanning electron microscope (SEM) and a Matlab based DIC system for offline strain measurement. A notched tensile sample was employed to ensure that only regions of high strain concentration was captured. The results found that during the tensile deformation process, the emergent localisation of deformation led to the formation of bands, which assembled at the sharp defect edges. These bands formed at the tip of the indent defect hindered the plastic flow by acting as an initiation zone for early crack nucleation resulting in reduced localised fracture strain values at micro scale. Further studies was carried out to assess the impact of longitudinal scratches on the bendability of titanium alloys. For t
Date of Award14 Jul 2021
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorPaul Blackwell (Supervisor) & Evgenia Yakushina (Supervisor)

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