Showing papers by "Paul A. Colegrove published in 2014"

A computationally efficient finite element model of wire and arc additive manufacture

Abstract: Wire and arc additive manufacturing (WAAM) is an emerging technology which has the potential to significantly reduce material usage and manufacturing time through the production of near net-shape components with high deposition rates. One of the main problems of this process is the residual stresses and distortions of the deposited workpiece. To help understand and optimise the process, finite element (FE) models are commonly used; however, the conventional transient models are not efficient for simulating a large-scale WAAM process. In this paper, the stress evolution during the thermal cycles of the WAAM process was investigated with the help of a transient thermomechanical FE model. It was found that the peak temperatures experienced during the thermal cycles of the WAAM process determine the residual stress of that point. Based on this finding, an efficient “engineering” FE model was developed. Compared to the conventional transient thermomechanical approach, this model can save the computational time by 99 %. This new model produced distortion and residual stress predictions that were nearly identical to the original transient model and the experimental results.

High Pressure Interpass Rolling of Wire + Arc Additively Manufactured Titanium Components

Abstract: Wire + Arc Additively Manufactured components contain significant residual stresses that manifest in distortion. Each layer of an additively manufactured wall was rolled with the aim of reducing the residual stress. Neutron diffraction and contour method measurements show that the residual stresses were reduced particularly at the boundary with the substrate. The process also reduced distortion, and refined the microstructure which may facilitate implementation on aerospace components.

Energy and force analysis of Ti-6Al-4V linear friction welds for computational modeling input and validation data

Abstract: The linear friction welding (LFW) process is finding increasing use as a manufacturing technology for the production of titanium alloy Ti-6Al-4V aerospace components. Computational models give an insight into the process, however, there is limited experimental data that can be used for either modeling inputs or validation. To address this problem, a design of experiments approach was used to investigate the influence of the LFW process inputs on various outputs for experimental Ti-6Al-4V welds. The finite element analysis software DEFORM was also used in conjunction with the experimental findings to investigate the heating of the workpieces. Key findings showed that the average interface force and coefficient of friction during each phase of the process were insensitive to the rubbing velocity; the coefficient of friction was not coulombic and varied between 0.3 and 1.3 depending on the process conditions; and the interface of the workpieces reached a temperature of approximately approximately 1273 K (1000 °C) at the end of phase 1. This work has enabled a greater insight into the underlying process physics and will aid future modeling investigations.

Hybrid Modelling Approach for Investigating the Thermal Contact during Solid State Joining

Abstract: In FSW modelling, two major approaches have been used to describe the heat loss from the workpiece to the backing bar. The first method simplifies the heat loss using a convective transfer and has been used by some researchers such as Khandkar et al.[1]. The second method uses a contact gap conductance to represent the imperfect contact at the interface between the workpiece and the backing bar [1-3]. The contact gap conductance, k is defined as: k = Q/(T0-TA), where Q is the heat flux from the workpiece to the backing bar, TO is the temperature of the workpiece and TA is the temperature of the backing bar. Khandkar et al.[1] found that using the contact gap conductance method was more accurate than the convective heat transfer coefficient. Both Simar et al.[2], Colegrove and Shercliff [3] and Shi et al.[4] have used a variable contact gap conductance in their models. Shi et al.[4] applied a temperature dependent contact gap conductance method where the value increased with temperature to simulate the better contact under the tool. This paper combined the FSW process model with Artificial Neural Network (ANN) models to find the temperature dependent contact gap conductance k which is named as a hybrid models.