Numerical Modeling of the Additive Manufacturing (AM) Processes of Titanium Alloy

IntroductionIt is easy to understand why industry and, especially, aerospace engineers love titanium.Titanium parts weigh roughly half as much as steel parts, but its strength is far greater than the strength of many alloy steels giving it an extremely high strength-to-weight ratio.Most titanium alloys are poor thermal conductors, thus heat generated during cutting does not dissipate through the part and machine structure, but concentrates in the cutting area.The high temperature generated during the cutting process also causes a work hardening phenomenon that affects the surface integrity of titanium, and could lead to geometric inaccuracies in the part and severe reduction in its fatigue strength [Benes, 2007].On the contrary, additive manufacturing (AM) is an effective way to process titanium alloys as AM is principally thermal based, the effectiveness of AM processes depends on the material's thermal properties and its absorption of laser energy rather than on its mechanical properties.Therefore, brittle and hard materials can be processed easily if their thermal properties (e.g., conductivity, heat of fusion, etc.) are favorable, such as titanium.Cost effectiveness is also an important consideration for using additive manufacturing for titanium processing.Parts or products cast and/or machined from titanium and its alloys are very expensive, due to the processing difficulties and complexities during machining and casting.AM processes however, have been found to be very cost effective because they can produce near-net shape parts from these high performance metals with little or no machining [Liou & Kinsella, 2009].In the aerospace industry, titanium and its alloys are used for many large structural components.When traditional machining/cast routines are adopted, conversion costs for these heavy section components can be prohibitive due to long lead time and low-yield material utilization [Eylon & Froes, 1984].AM processes have the potential to shorten lead time and increase material utilization in these applications.The following sections 1.1, 1.2 and 1.3 summarize the fundamental knowledge for the modeling of additive manufacturing processes. Additive manufacturingAdditive manufacturing can be achieved by powder-based spray (e.g., thermal spray or cold spray), sintering (e.g., selective laser sintering), or fusion-based processes (or direct metal deposition) which use a laser beam, an electron beam, a plasma beam, or an electric arc as an www.intechopen.comTitanium Alloys -Towards Achieving Enhanced Properties for Diversified Applications 4 energy source and either metallic powder or wire as feedstock [Kobryn et al., 2006].For the aerospace industry which is the biggest titanium market in the U.S. [Yu & Imam, 2007], fusion-based AM processes are more advantageous since they can produce 100% dense functional metal parts.This chapter will focus on fusion-based AM processes with application to titanium.Numerical modeling and simulation is a very useful tool for assessing the impact of process parameters and predicting optimized conditions in AM processes.AM processes involve many process parameters, including total power and power intensity distribution of the energy source, travel speed, translation path, material feed rate and shielding gas pressure.These process parameters not only vary from part to part, but also frequently vary locally within a single part to attain the desired deposit shape [Kobryn et al., 2006].Physical phenomena associated with AM processes are complex, including melting/solidification and vaporization phase changes, surface tension-dominated free-surface flow, heat and mass transfer, and moving heat source.The variable process parameters together with the interacting physical phenomena involved in AM complicate the development of processproperty relationships and appropriate process control.Thus, an effective numerical modeling of the processing is very useful for assessing the impact of process parameters and predicting optimized conditions.Currently process-scale modeling mainly addresses transport phenomena such as heat transfer and fluid dynamics, which are closely related to the mechanical properties of the final structure.For example, the buoyancy-driven flow due to temperature and species gradients in the melt pool strongly influences the microstructure and thus the mechanical properties of the final products.The surface tension-driven free-surface flow determines the shape and smoothness of the clad.In this chapter, numerical modeling of transport phenomena in fusion-based AM processes will be presented, using the laser metal deposition process as an example.Coaxial laser deposition systems with blown powder as shown in Fig. 1 are considered for simulations and experiments.The material studied is Ti-6Al-4V for both the substrate and powder.As the main challenges in modeling of fusionbased AM processes are related to melting/solidification phase change and free-surface flow in the melt pool, modeling approaches for these physical phenomena will be introduced in Sections 1.2 and 1.3. Modeling of melting/solidification phase changeFusion-based AM processes involve a melting/solidification phase change.Numerical modeling of the solidification of metal alloys is very challenging because a general solidification of metal alloys involves a so-called "mushy region" over which both solid and liquid coexist and the transport phenomena occur across a wide range of time and length scales [Voller, 2006].A rapidly developing approach that tries to resolve the smallest scales of the solid-liquid interface can be thought of as direct microstructure simulation.In order to simulate the microstructure development directly, the evolution of the interface between different phases or different microstructure constituents has to be calculated, coupled with the physical fields such as temperature and concentration [Pavlyk & Dilthey, 2004].