CFD-Informed Finite Element Analysis for Thermal Control in Wire-Arc Directed Energy Deposition PhD

Cranfield University

We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research opportunity focuses on advancing the field of large-scale additive manufacturing, utilising metal wire as the feedstock and electric arc as the heat source. The project aims to enhance our understanding of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field, ultimately optimising the deposition process.

Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace, energy, and automotive. Among its variants, wire-arc directed energy deposition (WA-DED) is emerging as an increasingly compelling approach for building large-scale structural components. This process involves feeding a metal filler wire, either coaxially or off-axis, into an electric arc to create a molten pool that solidifies on a substrate, enabling the layer-by-layer construction of 3D objects. The temperature field generated by the interaction between the arc and the material plays a critical role in determining the microstructure, residual stress, and distortion of the built parts-all of which profoundly affect their mechanical properties and overall performance. Therefore, understanding the temperature field and developing effective thermal control techniques are vital to ensuring a high-quality WA-DED process.



Finite element analysis (FEA) is widely used to predict the temperature field during the WA-DED process. Traditional FEA models rely heavily on empirical heat source definitions, such as the double ellipsoidal model, to represent energy input. While effective in reproducing the temperature field for analysing residual stresses and distortions after experimental calibration, these models lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused on thermal conduction in solid medium and often overlook the impact of liquid metal convection within the molten pool. Although using an artificial compensation through calibration with experiments can improve the temperature prediction, the predictive accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition.