Application of Fuzzy Integrated JAYA Algorithm for the Optimization of Surface Roughness of DMLS Made Specimen: Comparison with GA

Gajera, Hiren; Darji, Veera P.; Dave, Komal

doi:10.1007/978-981-13-8196-6_13

Cited by 1 publication

(1 citation statement)

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“…El-Sayed et al [25] used the RSM to propose the optimum process parameters, including laser power, scanning speed, and hatch spacing, for Ti-6Al-4V medical implant applications and concluded that higher energy densities resulted in lower surface roughness and lower porosity levels. Gajera et al [26] used the Box-Behnken design of the RSM and established a relationship between the L-PBF process parameters and surface roughness values of CL50 WS steel parts to compare two optimization algorithms: a genetic algorithm and the Jaya algorithm. Bartolomeu et al [27] manufactured Ti-6Al-4V samples by varying three processing parameters (i.e., laser power, scanning speed, and hatch spacing) at three levels in the L-PBF process and used the RSM to analyze the experimental results of shear stress, hardness, and density.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

2020

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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.

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Section: Introductionmentioning

confidence: 99%