Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Abstract

Purpose Depending on an experimental approach to find optimal parameters for producing fully dense (relative density > 99%) Inconel 718 (IN718) components in the selective laser melting (SLM) process is expensive and offers no guarantee of success. Accordingly, this study aims to propose a multi-scale simulation framework to guide the choice of processing parameters in a more pragmatic manner. Design/methodology/approach In the proposed approach, a powder layer, ray tracing and heat transfer simulation models are used to calculate the melt pool dimensions and evaporation volume corresponding to a small number of laser power and scanning speed conditions within the input design space. A layer-heating model is then used to determine the inter-layer idle time required to maximize the temperature convergence rate of the solidified layer beneath the power bed. The simulation results are used to train surrogate models to construct SLM process maps for 3,600 pairs of the laser power and scanning speed within the input design space given three different values of the underlying solidified layer temperature (i.e., 353 K, 673 K and 873 K). The ideal selection of laser power and scanning speed of each process map is chosen based on four quality-related criteria listed as follows: without the appearance of key-hole melting; an evaporation volume less than the volume of the d90 powder particles; ensuring the stability of single scan tracks; and avoiding a weak contact between the melt pool and substrate. Finally, the optimal laser power and scanning speed parameters for the SLM process are determined by superimposing the optimal regions of the individual process maps. Findings The feasibility of the proposed approach is demonstrated by fabricating IN718 test specimens using the optimal processing conditions identified by the simulation framework. It is shown that the maximum density of the fabricated parts is 99.94%, while the average density is 99.88% and the standard deviation is less than 0.05%. Originality/value The present study proposed a multi-scale simulation model which can efficiently predict the optimal processing conditions for producing fully dense components in the SLM process. If the geometry of the three-dimensional printed part is changed or the machine and powder material is altered, users can use the proposed method for predicting the processing conditions that can produce the high-density part.

Mechanical Properties of Titanium/Nano-Fluorapatite Parts Produced by Laser Powder Bed Fusion

“…It was seen that even though all three samples were fabricated using the optimal processing conditions (as determined by surface scanning experiments), the cross-sections still contained pores, voids, and (in some cases) cracks. It was reported in [ 31 ] that the origins of the pores formed in the L-PBF process can be classified into three main types: (1) poor-adhesion, (2) key-hole, and (3) gas bubble entrapment during the solidification process [ 32 , 33 ]. It was additionally noted that the pores formed by gas bubble entrapment have a characteristic size of around 10 µm.…”

Mitigation of Gas Porosity in Additive Manufacturing Using Experimental Data Analysis and Mechanistic Modeling

“…First, a mechanistic model [42,43] of the LPBF process was tested, calibrated using experimental results, and used to compute temperature fields and molten pool geometry. Then, experimental data [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] on porosity during LPBF of stainless steel 316, Ti-6Al-4V, Inconel 718, and AlSi10Mg were collected from the literature. The results from the well-tested model were used to derive and calculate a gas porosity index corresponding to all experimental cases.…”

Development of process-structure linkage for Inconel 718 processed by laser powder bed fusion: a numerical modeling approach

“…Liquid fraction f L , which has a value of 0 in solid phase, 1 in liquid phase and a linear trend in the liquid–solid mushy zone, is used to distinguish between the solid and liquid phases (Tran et al , 2022). f L can be calculated by using equation (4): Here, T L and T S represent the temperatures of liquidus and solidus, respectively.…”

“…The degree of undercooling at the liquid/solid interface acts as the growth-promoting force. Liquid fraction f L , which has a value of 0 in solid phase, 1 in liquid phase and a linear trend in the liquid-solid mushy zone, is used to distinguish between the solid and liquid phases (Tran et al, 2022). f L can be calculated by using equation ( 4):…”