AI 3D Printing Process Parameters Optimization

Park Hong Seok

and Nguyen Dinh Son

Department of Mechanical and Aerospace, University of Ulsan, 93-Daehak-ro, Ulsan, South Korea

Keywords: Artificial Neural Network, Selective Laser Melting, Ti-6Al-4V, Optimization.

Abstract: Optimization parameters of Selective Laser Melting (SLM) process is a significant question currently. Due to

attractive advantages, namely high density of printed products and freely design, the SLM has been

increasingly applied in industrial manufacturing. However, not only various influenced factors but also their

range affects to the printing process. Therefore, it is difficult and requires much testing time and cost to select

a suitable process parameter for manufacturing a desirable product. In this article, a supervised learning

Artificial Neural Network was applied to build an optimization system for finding out optimal process

parameters. Inputs of the system are desirable properties of a product as relative density ratio while outputs

are the crucial parameters as laser power, laser velocity, hatch distance, and layer thickness. The developed

system is a powerful contribution to industrial SLM manufacturing. By applying the system, it requires less

pre-manufacturing expenditure and also helps the printing users to choose approximately process parameters

for printing out a desirable product.

1 INTRODUCTION

Selective laser melting (SLM) powder bed fabrication

is additive manufacturing (AM) that starting from

metal powder, allows achieving desirable density

products layer-by-layer. Each layer anchors to the

previous one as the result of a complete fusion of

metal powder obtained by a laser beam. The laser spot

moves along a scanning pattern generated and

controlled by a CAD model of the part to be built. The

schematics representation of typical powder-bed

fusion system explained in Figure 1. The strength of

the SLM process is possible to create any possible

geometry (Srivatsan and Sudarshan, 2015a).

Therefore, the SLM is attractive to the aerospace,

automotive and other industries for present and

future.

Due to its excellent mechanical properties such as

good stability, good corrosion resistance, and high

specific strength, the titanium alloy Ti-6Al-4V is

widely used in many industries (Alcisto et al., 2011).

However, its high cost and challenging machinability

somewhat restrict its use in conventional

manufacturing methods, namely forging and casting

(Tan et al., 2018). Nevertheless, the use of Ti-6Al-4V

https://orcid.org/0000-0002-8382-4843

https://orcid.org/0000-0001-7509-3422

in SLM offers the benefits of recycling the

unprocessed metal powder, geometrical freedom in

product design, and time and energy efficiencies

(Kruth et al., 2005).

Figure 1: A schematic of a Selective Laser Melting method.

Although an attracting manufacturing method for

the aerospace, automotive and other technological

industries, printing a desirable product is complicated

because of many affected factors and their wide range

that significantly impacts on the mechanical

properties of the printed product. Many papers have

tried to optimize the SLM process. It is evident in all

356

Seok, P. and Son, N.

AI 3D Printing Process Parameters Optimization.

DOI: 10.5220/0008903303560361

In Proceedings of the 12th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2020) - Volume 2, pages 356-361

ISBN: 978-989-758-395-7; ISSN: 2184-433X

these methods that investigating process parameters

independently is incorrect because the printed

product quality is a function of the relationship

between several crucial process parameters.

Therefore, determining the optimal process

parameters by applying an artificial neural network to

print a desirable density product is the goal of this

research.

Artificial neural networks (ANNs) motivated by

modeling of human brains and nerve cells are

currently considered as one of the neuro-modeling

techniques used in associate with robust optimization.

Using artificial neural networks to optimize

engineering problems has been a great application,

such a welding process (Sivagurumanikandan et al.,

2018). In SLM, there were some research analysed

applying ANNs but individual process parameters

(Mertens et al., 2014)(Kempen et al., 2011). In this

article, four crucial process parameters as laser

power, laser scanning speed, hatch distance and layer

thickness are optimized to get a desirable density of

the manufactured product.

2 METHODOLOGY

The material selected for this study was the titanium-

alloy Ti-6Al-4V ELI, Grade 23 (SLM Solutions

Group AG, Germany), supplied in powder form with

an average particle size of about 20-63μm. An SLM

printer (MetalSys150; WinforSys Co., Ltd., Korea)

with an IPG ytterbium fiber laser, (YLR-200-AC-

Y11; IPG Photonics) 200W maximum output, air-

cooled, was used to process the powder. Scanning

electron microscope (SEM) image of powder material

and particle size distribution were shown in Figure 2a

and 2b, respectively. An SLM machine (MetalSys150,

Winforsys co., Ltd) with the YLR-200-AC-Y11, IPG

Ytterbium Fiber Laser, 200W maximum output, air-

cooled, was used to process. Table 1 shows the

technical parameters of the SLM printer. The printing

process used the meander laser scanning strategy in

which the laser scan direction in n

layer is

perpendicular to that of the n+1

layer, which is the

same as the n-1

layer.

Table 1: Technical parameters of the MetalSys150.

Item Value

Wavelength 1,075nm

Output power 200W max

Beam quality M2,1.1

Beam spot 70µm

Building size 150×150×250mm3

Max scanning 7 m/s max

Argon gas was filled into the chamber to maintain

oxygen degree at below 0.5 percent. The chamber

temperature was at 28

C. The process parameters for

experiments are shown in Table 2. Parameters were in

the range of settings recommended by the machine

manufacturers. After printing, SLM processed parts

were tested. A GR-200 analytical balance (A&D

Company, Ltd., Tokyo, Japan) was combined with the

AD-1653 density determination kit to measure the

density of the printed parts based on the Archimedes’

principle. The density of the samples is obtained

according to the weight of the sample in the air, the

weight of the sample in liquid, the distilled water (DI

water) and the density of the liquid.

Figure 2: SEM image (a) and particle size distribution (b)

of the used material powder.

Table 2: SLM process parameters used for experiments.

Factor

Level

Laser power (W) 80 120 180

Laser scanning speed(mm/s) 800 1200 2500

Layer thickness(µm) 20 50 100

Hatch distance(µm) 35 50 90

2.1 Neural Network Parameters

A feedforward ANN includes four input nodes as four

process parameters, while output was the density of

the printed part. Two hidden layers (Rojas, 1996)

with ten, eight nodes (L. Fletcher ; V. Katkovnik ;

F.E. Steffens ; A.P. Engelbrecht, 1998) in first,

second hidden layer respectively with a full

connection.

2.1.1 Data Processing

Significant differences in the values of four

investigated process parameters as the inputs and

product quality as the output leads complicate the

learning process of the neural network. For solving

this potential problem, all input and output were

previously scaled by using a standardization:

=





(1)

With mean:

AI 3D Printing Process Parameters Optimization

357

μ=





∑(





)





(2)

and standard deviation:

=







∑(





−μ

)







(3)

2.1.2 Neural Network Architecture

The number of neurons in the input layer is equal to

the number of the investigating parameter. The

quantity of hidden layer usually is set between the

size of the input and size of the output. Selecting the

number of neurons in the hidden layers is very

important. One hand, using too few neurons in hidden

layer will result the underfitting. On the other hand,

too many of them can result in overfitting. There are

many methods for determining a suitable number of

hidden nodes. In this research, two hidden layers were

applied and the number of hidden node was

calculated by the following:













(









)



(4)

Ni: number of input neurons

: number of output neurons

: number of samples in training data set

: an arbitrary scaling factor usually 2-10

2.1.3 K-fold Cross-validation

For training the model, the dataset is usually split into

training data and test data. The training dataset

includes a known output, and the model learns on this

data to be described to other coming data. The test

dataset is used to test the prediction of the model.

However, splitting data that is required to reflect the

essential characteristics of the problem is not easy.

Additionally, by partitioning the available data

into other sets, the data will significantly decrease the

number of samples which can be used for learning the

model, and the results can depend on a particular

random selection for the pair of sets. Therefore, in this

article, the k-fold cross-validation is applied to solve

the mentioned problems. In k-fold cross-validation,

the original sample is randomly partitioned into k

equal sized subsamples. Of the k subsets, a single

subsample is retained as the validation data for testing

the model, and the remaining k-1 subsamples are used

as training data. The cross-validation process is then

repeated k times (the folds), with each of the k

subsamples used exactly once as the validation data.

The k results from the folds can then be averaged to

produce a single estimation (James et al., 2013). The

advantage of this method is that all observations are

applied for both training and validation, and each

observation is used for validation exactly once. In this

article, the 10- fold was selected

2.1.4 Activation Function

The rectified linear unit (ReLU), 

(



)

=max

(

0,

)

was used when going from one layer to the next as an

active function (Glorot et al., 2011). It is the most

popular non-linear function recently because it learns

much faster in networks with many layers, typically

comparing the others (LeCun et al., 2015).

Nevertheless, the sigmoid function, f(z) = 1/(1+ exp(-

z)), was used as the active function for the output

layer because of real-valued output.

2.1.5 Neural Network Validation

The ANN was trained by minimizing the mean square

error as a loss function with the Adam Optimizer

algorithms (Kingma and Ba, 2017). The loss function

was the mean absolute error and calculated as:





(

,

)

∑















(5)

Where  and y, N, w, b are predicted value of the

model, experimental output, total of samples, weight,

and bias of the neural, respectively.

2.1.6 Neural Network Optimization

The Gradient Descent method (Kingma and Ba,

2017) was applied to minimize the loss function by

changing the values of the  and  parameters as the

following equation:





=



−





(





)

(6)

In which:

: is a neuron network parameter







(





)

: is the derivation of the loss function at a

point  at the 



loop.

: learning rate, 0.05.

2.1.7 Dropout

Overfitting is a problem that often happened in

machine learning. It is a phenomenon that model is

too fit the training data, and it will fit the noise in the

data rather than finding a general predictive rule

(Tušar et al., 2017). A signal to recognize the

overfitting is that training error is small while the

testing error is high. For preventing overfitting, the

dropout methods were used. Dropout means shutting

down units in a neural network (Srivastava et al.

2014) (Dahl et al., 2013) (de Rosa et al., 2018). It

temporarily deactivates it from the network. The

selection of ignored units is random. Each unit has

remained with a fixed probability  independent of

another one. In this research, the probability of

retaining a unit in the network was 0.8. After training,

at the testing set, the network is used without dropout

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

358

in which the weights and biases are scaled as 



()



()

2.2 Optimization System Algorithm

The Generated Data was created by integrating from

four the investigated process parameters, as shown in

Table 2. Laser power changes from 80W to 180W

with 5W variation. Laser scanning velocity values

change between 800mm/s to 2500mm/s with

100mm/s variation. Layer thickness and hatch

distance change from 20µm to 100µm and 35µm to

90µm respectively with 5µm variation.

Table 3: Combination of Generated data for predicting.

Factor Min

value

Max

Value

Deviation

Laser Power (W) 80 180 5

Laser scanning speed

(mm/s)

800 2500 100

Layer thickness (µm) 20 80 5

Hatch distance (µm) 30 100 5

The optimization algorithm is shown in Figure 3

and the following:

1. At the first time, develop an ANN

2. Predicting density ratios from the Generated Data

that was created by combining levels of four

process parameters. Laser power changes from

80W to 180W with 5W variation. Laser scanning

velocity values change between 800mm/s to

2500mm/s with 100mm/s variation while layer

thickness and hatch distance change from 20µm to

80µm and 30µm to 100µm respectively with 5µm

difference as shown in Table 3.

3. Input a required density ratio of the user that is

limited from 75 to 100, in the scope of this paper.

4. Predicted data as the density ratio from the ANN

was compared with user requirements.

5. Then process parameters in the Generated Data of

which predicted data fit with user needs are

indexed.

6. The indexed data were filtered by maximizing the

value of the productivity, calculated by: 



=.ℎ.

to point out the optimal parameters. It is a

relationship of layer thickness (t), laser scan speed

(v), and hatch distance (h).

In this research, ANN implementation, training and

the optimization system were developed using the

Python programming language, in which the

TensorFlow library (Abadi et al. 2016) was

applied.

3 RESULTS AND DISCUSSIONS

3.1 Process Parameters and Printed

Part Qualities Relationship

Figure 4 indicates the relationship between individual

four process parameters and the density of the

manufactured part. Figure 4a shows the influence of

laser power on density at 1400mm/s of laser speed

and 40µm of hatch distance. Increasing laser power

increased the relative density because of sufficient

powder molten. Moreover, Figure 4a illustrates the

effect of layer thickness. The blue, green, red and

black lines present the influence of laser power at

20µm, 40µm, 60µm, and 80µm of layer thickness

respectively. It is precise that increasing layer

thickness reduces the density ratio.

Figure 3: Algorithms for optimization system.

At a specific laser power, a thicker layer material

powder will less be moult. Figure 4b presents the

effect of laser scanning speed at different hatch

distances. It shows a severe incline during increasing

laser speed. High scanning speed of laser reduces

interaction time of laser spot on material that

generates lower densities because of incomplete

melting of powder (Cherry et al., 2014). Additionally,

AI 3D Printing Process Parameters Optimization

359

the effect of hatch spaces on density illuminates in

Figure 4b. The hatch distance relates to the

overlapping area of two adjacent melting lines.

Reducing the hatch distance leads over-burning and

vaporization within the melting pool. However,

insufficient material moulted due to a large hatch

distance generated low relative densities. A smaller

hatch distance often increases density ratio. However,

a combination of low speed of laser scanning velocity

and too small hatch space leads balling phenomenon

and vaporization of material that was significantly

affected by density (Khan and Dickens, 2012). It

explains for a lower density of blue line at 800mm/s

compared to others.

Figure 4: Relationship of individual process parameters and

product properties.

3.2 Neural Network Evaluation

In order to validate the developed ANN, the mean

absolute error (MAE) of the training and testing

process is shown in Figure 5. The dark blue line and

the orange line present the training and validation of

the network. They converge after 3000 epochs. The

result elucidates that the network prevents overfitting

problem. Additionally, the results show that the

maximum error percentage is 1.5% approximately,

which is an acceptable value. Therefore, the

developed ANN is valid for predicting.

3.3 Optimization System Verification

99 percentage of density ratio was used as a request

of the user to verify the developed system. Table 4

shows the parameters given by the system. Using the

optimal process parameters suggested form the

system, a product was printed one more time to

confirm the performance of the system. By applying

the Archimedes principle, the result indicates 99,8

percentage of relative density. The part was mounted

by hot pressing, polished and examined for porosity.

Figure 6 shows a cross-section of the part.

Additionally, the optimal parameters confirm a

similar result by the response surface methodology

(RSM)- based method

(Li et al., 2018). It proved the

success of the developed optimization system for SLM

printing.

Figure 5: Mean absolute error of training and testing

processes of ANN.

Table 4: Optimal process parameter set.

Parameter Value

Layer thickness, t, (µm) 20

Hatch distance, h, (µm) 80

Laser power, P, (W) 180

Laser scanning speed, v,(mm/s) 900

Figure 6: A cross-section of a product manufactured by the

optimal process parameters set.

4 CONCLUSIONS

In this article, a system combining an ANN for

optimizing process parameters to fabricate a desirable

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

360

product was developed. A supervised neural network

was built for predicting a generated data set. Training

and testing data set of the ANN were collected from

the experiment. The gradient descent method was

applied to minimize the loss function. The dropout

technique and k-fold validate were used to prevent the

overfitting problem. After building the network, a

user requirement was compared with predicted data.

Process parameters of which predicted values

satisfied with user needs were indexed from the

generated data set. In order to achieve the optimal

process parameters, productivity was added as

filtering conditions finally. In the future, the system

will be implemented, and training data will more be

collected to achieve more accurate results.

ACKNOWLEDGEMENTS

This work was supported by the Development of PBF

3D printing analysis SW Technology for

manufacturing simulation of metal parts in power

generation or shipbuilding project.

REFERENCES

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean,

J., et al. (2016). TensorFlow: A System for Large-Scale

Machine Learning. In 12th USENIX Symposium on

Operating Systems Design and Implementation (OSDI

’16).

Alcisto, J., Enriquez, A., Garcia, H., Hinkson, S., Steelman,

T., Silverman, E., et al. (2011). Tensile properties and

microstructures of laser-formed Ti-6Al-4V. Journal of

Materials Engineering and Performance. SPRINGER.

Cherry, J. A., Davies, H. M., Mehmood, S., Lavery, N. P.,

Brown, S. G. R., & Sienz, J. (2014). Investigation into

the effect of process parameters on microstructural and

physical properties of 316L stainless steel parts by

selective laser melting. International Journal of

Advanced Manufacturing Technology. SPRINGER.

Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013).

Improving deep neural networks for LVCSR using

rectified linear units and dropout. In ICASSP, IEEE

International Conference on Acoustics, Speech and

Signal Processing - Proceedings. IEEE.

de Rosa, G. H., Papa, J. P., & Yang, X. S. (2018). Handling

dropout probability estimation in convolution neural

networks using meta-heuristics. Soft Computing.

ELSEVIER

Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse

rectifier neural networks. AISTATS ’11: Proceedings of

the 14th International Conference on Artificial

Intelligence and Statistics.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013).

An Introduction to Statistical Learning (Vol. 103).

Springer-Verlag New York.

Kempen, K., Thijs, L., Yasa, E., Badrossamay, M.,

Verheecke, W., & Kruth, J. P. (2011). Process

optimization and microstructural analysis for selective

laser melting of AlSi10Mg. 22nd Annual International

Solid Freeform Fabrication Symposium - An Additive

Manufacturing Conference, SFF 2011.

Khan, M., & Dickens, P. (2012). Selective laser melting

(SLM) of gold (Au). Rapid Prototyping Journal.

Kingma, D. P., & Ba, J. L. (2017). Adam: a Method for

Stochastic Optimization. International Conference on

Learning Representations 2015.

Kruth, J., Vandenbroucke, B., Vaerenbergh, J., & Mercelis,

P. (2005). Benchmarking of different SLS/SLM

processes as rapid manufacturing techniques. In Int.

Conf. Polymers & Moulds Innovations (PMI), Gent,

Belgium.

L. Fletcher ; V. Katkovnik ; F.E. Steffens ; A.P.

Engelbrecht. (1998). Optimizing the Number of Hidden

Nodes of a Feedforward Artificial Neural Network. In

IEEE International Conference on Neural Networks

Proceedings.

LeCun, Y. A., Bengio, Y., & Hinton, G. E. (2015). Deep

learning. Nature.

Li, Z., Kucukkoc, I., Zhang, D. Z., & Liu, F. (2018).

Optimising the process parameters of selective laser

melting for the fabrication of Ti6Al4V alloy. Rapid

Prototyping Journal. EMERALD INSIGHT

Mertens, R., Clijsters, S., Kempen, K., & Kruth, J.-P.

(2014). Optimization of Scan Strategies in Selective

Laser Melting of Aluminum Parts With Downfacing

Areas. Journal of Manufacturing Science and

Engineering. ASME

Rojas, R. (1996). Neural Networks. Neural Networks: A

Systematic Introduction (Vol. 7). Springer-Verlag.

Sivagurumanikandan, N., Saravanan, S., Kumar, G. S.,

Raju, S., & Raghukandan, K. (2018). Prediction and

optimization of process parameters to enhance the

tensile strength of Nd: YAG laser welded super duplex

stainless steel. Optik. ELSEVIER.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., &

Salakhutdinov, R. (2014). Dropout: A Simple Way to

Prevent Neural Networks from Overfitting. Journal of

Machine Learning Research.

Srivatsan, T. S., & Sudarshan, T. S. (2015a). Additive

Manufacturing: Innovations, Advances, and

Applications. CRC Press, London.

Tan, F. B., Song, J. L., Wang, C., Fan, Y. B., & Dai, H. W.

(2018). Titanium clasp fabricated by selective laser

melting, CNC milling, and conventional casting: a

comparative in vitro in vitro study. Journal of

Prosthodontic Research. ELSIVIER

Tušar, T., Gantar, K., Koblar, V., Ženko, B., & Filipič, B.

(2017). A study of overfitting in optimization of a

manufacturing quality control procedure. Applied Soft

Computing Journal. ELSIVIER.

AI 3D Printing Process Parameters Optimization

361