Abstract
Laser powder bed fusion exhibits many advantages for manufacturing complex geometries from hard to machine alloys such as IN625. However, a major drawback is the formation of high tensile residual stresses, and the complex relationship between the process parameters and the residual stresses has not been fully investigated. The current study presents multi-scale models to examine the variation of process parameters on melt pool dimensions, cyclic temperature evolutions, cooling rate, and cyclic stress generation and how they affect the stress end state. In addition, the effect of the same energy density, which is often overlooked, on the generated residual stresses is investigated. Multi-level validation is performed based on melt pool dimensions, temperature measurements with a two-color pyrometer, and finally, in-depth residual stress measurement. The results show that scan speed has the strongest effect on residual stresses, followed by laser power and hatch spacing. The results are explained in light of the non-linear temperature evolution, temperature gradient, and cooling rate during laser exposure, cooling time, and the rate during recoating time.
Highlights
Laser powder bed fusion (LPBF) [1] is an additive manufacturing technology that has gained momentum in recent years due to design freedom to manufacture highly complex and customizable geometries along with its capability of processing metals and ceramics [2]
Calculating the melt pool width and depth for the parameters examined in the current study (Figure 11) shows that the melt pool dimensions decrease with increased scan speed, which agrees with the literature [68,69,73]
The results presented in Sections 4.2.2 and 4.2.3 show that the increase in scan speed caused an increase in surface residual stresses (RS); the increase in laser power caused a decrease in surface RS
Summary
Laser powder bed fusion (LPBF) [1] is an additive manufacturing technology that has gained momentum in recent years due to design freedom to manufacture highly complex and customizable geometries along with its capability of processing metals and ceramics [2]. CFD models simulate single tracks but with the added benefit of including fluid dynamics effects such as the Marangoni effect, which drives the convection inside the melt pool, affecting the melt pool shape and CR that controls the final microstructure [25,26,27,28]. Both models were extended to simulate the effect of adjacent tracks on melt pool dimensions and temperature gradients; beyond that, there is little focus on the impact of process parameters [29,30,31].
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