Abstract
Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components.
Highlights
Laser-based powder-bed fusion (L-PBF) is a widely adopted additive manufacturing (AM)methodology used to manufacture high-performance metallic parts with complex geometries via selective laser scanning of thin layers of metal powders
Khorasani et al [52] implemented an artificial neural network (ANN) with three hidden layers with four, three, and two hidden nodes to predict a single response of the top surface roughness of Ti-6 Al-4V parts based on the input parameters of laser power, scanning speed, hatch spacing, scan pattern increment angle, and heat treatment (HT) condition, i.e., different HT temperatures and cooling times
All the raw measurements for all the L-PBF process parameters listed in Tables A1 and A2
Summary
Laser-based powder-bed fusion (L-PBF) is a widely adopted additive manufacturing (AM). Wang et al [31] combined the two methods, i.e., Taguchi and RSM, to study the effect of the laser power, scanning speed, and hatch spacing, on the mechanical properties and microstructure of nickel-based superalloy samples fabricated by the PBF process They applied linear, two-factor-interaction, and quadradic models to obtain response surfaces for the tensile strength of the manufactured samples and observed the quadratic modeling of this response yields to the lowest error value among all the tested models. Khorasani et al [52] implemented an ANN with three hidden layers with four, three, and two hidden nodes to predict a single response (output) of the top surface roughness of Ti-6 Al-4V parts based on the input parameters of laser power, scanning speed, hatch spacing, scan pattern increment angle, and heat treatment (HT) condition, i.e., different HT temperatures and cooling times According to their results, the HT condition, which is a post-process parameter, was the dominant factor in determining the top surface roughness of. Of optimum processing parameters predicted by each method are determined and compared
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