This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Wire-based directed energy deposition (DED) additive manufacturing (AM) uses an intense energy source, such as an electric arc, laser, and electron beam, to melt metal wire feedstock, which is deposited layer by layer along a planned path for building a structural part. The wire-based DED AM process is advantageous thanks to its large-scale deposition capacity, high efficiency of material and energy use, and wide applicability to different industrial applications. However, research is still needed to enhance the process productivity and part quality. Wire preheating is a feasible method to significantly enhance deposition rate. It can also help reduce heat input of the energy source, inhibit pore formation and refine grains, thereby enhancing mechanical properties of the deposited part. Induction heating (IH) is a highly controllable non-contact heating method suited for rapidly and precisely preheating the wire feedstock to target temperature. In addition, compared with conventional weld wire preheating methods such as resistance heating, bypass heating and auxiliary arc, IH avoids magnetic blow and is applicable to most metals with flexible set up. However, IH preheating of moving wire feedstock is complicated and underexplored for AM applications. In this study, to understand the complex electromagnetic heating mechanism, a multiphysics finite element model of coupled electromagnetic and thermal fields is developed based on the Eulerian framework, which improves the computational efficiency by 60% compared to the model in Lagrangian framework. On the other hand, in the case of feedstock passing through a stationary magnetic field at a constant wire feed speed, a more efficient steady-state approach is proposed with 90% computational time saving than the transient model. The temperature predictions by the model are validated by thermocouple measurements. Parametric sensitivity analysis was also performed to evaluate a range of coil geometries through the developed efficient model, revealing the effects of different parameters on the preheating temperature and energy consumption to guide AM process optimisation.