Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning

Nguyen, Dinh Son; Park, Hong Seok; Lee, Chang-Myung

doi:10.1016/j.jmapro.2020.04.014

Cited by 74 publications

(23 citation statements)

References 29 publications

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“…In addition to the flat and round samples, miniature flat specimens (M-TT [9,10], Figure 3) were excised from the 6 mm diameter round ZXY specimens, as shown in Figure 4.…”

Section: Processing and Specimen Preparationmentioning

confidence: 99%

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Build Size and Orientation Influence on Mechanical Properties of Powder Bed Fusion Deposited Titanium Parts

Mertová

Džugan

Roudnická

et al. 2020

Metals

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This paper explores the influence of sample thickness and build orientation on the microstructure and mechanical properties of electron beam melting (EBM) additive manufactured Ti-6Al-4V titanium alloy and compared to previously published work on SLM-processed material. In particular, the various mechanical properties (tensile yield strength, ultimate tensile strength) were investigated with attempts to correlate with various microstructural features, including lamellae thickness, porosity and the size of prior-beta grains. However, it is shown that the surface exerts a dominant effect on mechanical properties with as-deposited surfaces. These observations provide the possibility for the further improvement of processing/property relations with as-deposited surfaces.

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“…In addition to the flat and round samples, miniature flat specimens (M-TT [9,10], Figure 3) were excised from the 6 mm diameter round ZXY specimens, as shown in Figure 4.…”

Section: Processing and Specimen Preparationmentioning

confidence: 99%

“…The effect of build parameters, position within the chamber, or sample orientation for both SLM and EBM have been described in multiple studies [1,2,4,[8][9][10][11][12]. However, most of the studies have been based on standardized specimen sizes that are quite large.…”

Section: Introductionmentioning

confidence: 99%

Build Size and Orientation Influence on Mechanical Properties of Powder Bed Fusion Deposited Titanium Parts

Mertová

Džugan

Roudnická

et al. 2020

Metals

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show abstract

“…Machine learning (ML) techniques can carry out complex pattern recognition and regression analysis without constructing and solving the underlying physical models. This method is widely used in modeling, prediction, and analyzing the interaction of parameters in different industries, such as manufacturing, aerospace, and biomedicine [32,33]. Among ML algorithms, artificial neural networks (ANNs), which are mathematical models mapping an input space to an output space, are the most extensively used techniques because of their strong computational power and sophisticated architectures [34].…”

Section: Introductionmentioning

confidence: 99%

“…Their proposed model works based on classification algorithms and it can classify the failures in the process caused by residual stresses, cracking, and delamination. Nguyen et al [33] presented an optimization tool that worked based on ML to predict the density of Ti-6Al-4V parts manufactured with any variation in the L-PBF process parameters, including laser power (80-180 W), scanning speed (800-2500 mm/s), layer thickness (20-80 µM), and hatch spacing (30-100 µM). Zhang et al [44] developed a prediction model for the high-cycle fatigue life of L-PBF-manufactured stainless steel parts using ML approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Li and Anand [45] trained a feed-forward back-propagation ANN to predict inherent strain obtained by thermo-mechanical simulation for different given hatch patterns that are adopted during the L-PBF process. Bhardwaj and Shukla [33] simulated a single track deposition in DED of Ti-15 Mo alloy and used their simulation results for training a feed-forward back-propagation ANN with seven hidden layers and Levenberg-Marquardt training algorithm. They used three input parameters, i.e., laser power, scanning speed, and powder feed rate, to predict the track dilution as the single response of the network.…”

Section: Introductionmentioning

confidence: 99%

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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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A Research Status of 3D Printing of Different Metal Forms

Liu

Li²,

Fan³

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning

Cited by 74 publications

References 29 publications

Build Size and Orientation Influence on Mechanical Properties of Powder Bed Fusion Deposited Titanium Parts

Build Size and Orientation Influence on Mechanical Properties of Powder Bed Fusion Deposited Titanium Parts

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

A Research Status of 3D Printing of Different Metal Forms

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