“…A significant amount of SLM research has been devoted to optimize the process parameters (laser power, scanning speed, scanning strategy, layer thickness, building chamber atmosphere, powder bed preheating temperature, and post processing heat treatment) and on numerical simulations [ 2 , 11 , 14 , 15 , 16 ]. On the other hand, only a few works have been devoted towards the influence of powder characteristics on the quality of SLM fabricated parts.…”
Selective laser melting (SLM) is one of the additive manufacturing technologies that allows for the production of parts with complex shapes from either powder feedstock or from wires. Aluminum alloys have a great potential for use in SLM especially in automotive and aerospace fields. This paper studies the influence of starting powder characteristics on the processability of SLM fabricated AlSi12 alloy. Three different batches of gas atomized powders from different manufacturers were processed by SLM. The powders differ in particle size and its distribution, morphology and chemical composition. Cubic specimens (10 mm × 10 mm × 10 mm) were fabricated by SLM from the three different powder batches using optimized process parameters. The fabrication conditions were kept similar for the three powder batches. The influence of powder characteristics on porosity and microstructure of the obtained specimens were studied in detail. The SLM samples produced from the three different powder batches do not show any significant variations in their structural aspects. However, the microstructural aspects differ and the amount of porosity in these three specimens vary significantly. It shows that both the flowability of the powder and the apparent density have an influential role on the processability of AlSi12 SLM samples.
“…A significant amount of SLM research has been devoted to optimize the process parameters (laser power, scanning speed, scanning strategy, layer thickness, building chamber atmosphere, powder bed preheating temperature, and post processing heat treatment) and on numerical simulations [ 2 , 11 , 14 , 15 , 16 ]. On the other hand, only a few works have been devoted towards the influence of powder characteristics on the quality of SLM fabricated parts.…”
Selective laser melting (SLM) is one of the additive manufacturing technologies that allows for the production of parts with complex shapes from either powder feedstock or from wires. Aluminum alloys have a great potential for use in SLM especially in automotive and aerospace fields. This paper studies the influence of starting powder characteristics on the processability of SLM fabricated AlSi12 alloy. Three different batches of gas atomized powders from different manufacturers were processed by SLM. The powders differ in particle size and its distribution, morphology and chemical composition. Cubic specimens (10 mm × 10 mm × 10 mm) were fabricated by SLM from the three different powder batches using optimized process parameters. The fabrication conditions were kept similar for the three powder batches. The influence of powder characteristics on porosity and microstructure of the obtained specimens were studied in detail. The SLM samples produced from the three different powder batches do not show any significant variations in their structural aspects. However, the microstructural aspects differ and the amount of porosity in these three specimens vary significantly. It shows that both the flowability of the powder and the apparent density have an influential role on the processability of AlSi12 SLM samples.
“…Density of the additively manufactured part built via L-PBF has a crucial impact on mechanical properties of the fabricated component. An approach to identify processing parameters for producing high-density parts was employed to select the processing conditions, as described in the previous studies [31][32][33][34]. Tong Tai AM250 selective laser melting (SLM) machine (Kaohsiung, Taiwan), equipped with a 50-400 W YAG laser with the laser spot size of D4sigma = 54 µm, was used to fabricate rectangular bar shape samples with dimensions of 10 × 5 × 5 mm 3 .…”
Rapid and accurate prediction of residual stress in metal additive manufacturing processes is of great importance to guarantee the quality of the fabricated part to be used in a mission-critical application in the aerospace, automotive, and medical industries. Experimentations and numerical modeling of residual stress however are valuable but expensive and time-consuming. Thus, a fully coupled thermomechanical analytical model is proposed to predict residual stress of the additively manufactured parts rapidly and accurately. A moving point heat source approach is used to predict the temperature field by considering the effects of scan strategies, heat loss at part’s boundaries, and energy needed for solid-state phase transformation. Due to the high-temperature gradient in this process, the part experiences a high amount of thermal stress which may exceed the yield strength of the material. The thermal stress is obtained using Green’s function of stresses due to the point body load. The Johnson–Cook flow stress model is used to predict the yield surface of the part under repeated heating and cooling. As a result of the cyclic heating and cooling and the fact that the material is yielded, the residual stress build-up is precited using incremental plasticity and kinematic hardening behavior of the metal according to the property of volume invariance in plastic deformation in coupling with the equilibrium and compatibility conditions. Experimental measurement of residual stress was conducted using X-ray diffraction on the fabricated IN718 built via laser powder bed fusion to validate the proposed model.
“…Density of the additively manufactured part built via L-PBF has a crucial impact on mechanical properties of the fabricated component. An approach to identify processing parameters for producing high-density parts is employed to select the processing conditions as described in the previous studies [33][34][35][36]. The processing conditions used to fabricate high-density samples for measuring the residual stress are listed in Table 2.…”
Section: Prediction Of Johnson-cook (J-c) Parametersmentioning
Rapid and accurate prediction of residual stress in metal additive manufacturing processes is of great importance to guarantee the quality of the fabricated part to be used in a mission-critical application in the aerospace and automotive industries. Experimentation and numerical modeling are valuable tools for measuring and predicting the residual stress; however, to-date conducting experimentation and numerical modeling is expensive and time-consuming. Thus, herein, a physics-based thermomechanical analytical model is proposed to predict the residual stress of the additively manufactured part rapidly and accurately. A moving point heat source approach is used to predict the temperature field by considering the effects of scan strategies, heat loss, and energy needed for solid-state phase transformation. Due to the high temperature gradient in this process, part experiences a high amount of thermal stress following solidification which may exceed the yield strength of the material. The thermal stress is obtained using Green’s function of stresses due to the point body load. The Johnson-Cook flow stress model is used to predict the yield surface of the part under repeated heating and cooling. As a result of the cyclic heating and cooling and the fact that the material is yielded, the residual stress build-up is predicted based on incremental plasticity and kinematic hardening behavior of the metal according to the property of volume invariance in plastic deformation in coupling with the equilibrium and compatibility conditions. The computational methodology is realized with the laser powder fusion of maraging steel 350 as a material of example. The validation of the predictive models has been presented in terms of the comparison of predicted and measured scan-direction and build-direction residual stress distributions along depth of build under various process parameter combinations. Moreover, for the first time, the Jonson-Cook parameters of maraging steel 350 are predicted using analytical modeling of machining forces and non-linear optimization techniques.
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