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N Mekras and I Artemakis
ANTER Ltd., Feidippidou 22 Str., 11527, Athens, Greece
Email:
[email protected];
[email protected]
Abstract. In this paper a methodology and an application example
are presented aiming to show how Artificial Neural Networks (ANNs)
can be used to model manufacturing processes when mathematical
models are missing or are not applicable e.g. due to the micro-
& nano- scaling, due to non-conventional processes, etc.
Besides the ANNs methodology, the results of a Software System
developed will be presented, which was used to create ANNs models
for micro & nano manufacturing processes. More specifically
results of a specific application example will be presented,
concerning the modeling of extrusion processes for polymeric
micro-tubes. ANNs models are capable for modeling manufacturing
processes as far as adequate experimental and/or historical data of
processes’ inputs & outputs are available for their training.
The POLYTUBES ANNs models have been trained and tested with
experimental data records of process’ inputs and outputs concerning
a micro-extrusion process of polymeric micro-tubes for several
materials such as: COC, PC, PET, PETG, PP and PVDF. The main ANN
model of the extrusion application example has 3 inputs and 9
outputs. The inputs are: tube’s inner & outer diameters, and
the material density. The model outputs are 9 process parameters,
which correspond to the specific inputs e.g. process temperature,
die inner & outer diameters, extrusion pressure, draw speed
etc. The training of the ANN model was completed, when the errors
for the model’s outputs, which expressed the difference between the
training target values and the ANNs outputs, were minimized to
acceptable levels. After the training, the micro-extrusion ANN is
capable to simulate the process and can be used to calculate
model’s outputs, which are the process parameters for any new set
of inputs. By this way a satisfactory functional approximation of
the whole process is achieved. This research work has been
supported by the EU FP7 NMP project POLYTUBES.
1. Introduction In this paper a proposed methodology will be
presented concerning the modeling of processes for the
manufacturing of polymeric micro-tubes using Artificial Neural
Networks (ANNs). Also the results of an ANNs processes modeling
software application will be presented, which has been developed by
the authors of this paper for the implementation of the proposed
methodology. An existing micro- extrusion process for manufacturing
polymeric micro-tubes will be used as case example. The physical
process data were used by the authors of this paper at ANTER Ltd to
create, train and test the micro- extrusion Neural Network.
Artificial Neural Networks (ANNs) are considered a research area of
Artificial Intelligence (AI) with significant applications in
several domains, including the manufacturing processes domain
[7-11]. ANNs are useful on several applications like for example on
applications of systems modeling, materials modeling [1, 5, 12,
15], function approximation, forecasting & prediction [2, 4,
14], classification problems, pattern recognition, etc. Generally,
ANNs
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
Published under licence by IOP Publishing Ltd 1
can be used in cases that mathematical models are completely
missing or are not adequate and accurate enough to represent a real
world model of a physical process or system. In most cases ANNs are
capable to learn and approximate real world models when adequate
historical and/or experimental data sets of model’s inputs &
outputs are available for the training of the ANN.
In the following Figureure 1 the ANNs structure that was used for
creating the POLYTUBES micro-manufacturing processes models is
given. This structure represents the MLP (Multilayer Perceptron)
feed-forward Neural Network model with 1 input layer, 2 hidden
layers and 1 output layer, which uses the back-propagation training
algorithm [3, 6]. In the cases of the POLYTUBES processes, the
number of inputs and the number of neurons of the output layer
correspond to the inputs and outputs of the physical process as
these are defined by the relevant manufacturing theory and the
needs of the process engineers. After the ANNs training is
completed the ANNs model can be used to simulate the processes
behavior and provide output results for any set of inputs provided
by the user.
Figure 1. MLP Feed forward ANN (R–S1–S2–S3) with 2 hidden
layers.
2. Application case: Extrusion of polymer micro-tubes The first
step for modeling the micro-extrusion process concerned the
selection of the Neural Network architecture and the selection of
the number of the hidden neurons. The numbers of neurons of the
1st
and 2nd hidden layers for the micro-extrusion process were
estimated during the ANNs training and were adjusted through a test
& trial procedure in an effort to minimize the training error
for all process’ outputs [13]. For the specific micro-extrusion ANN
a 3-20-20-9 architecture was applied, with 20 neurons in the 1st
hidden layer and 20 neurons in the 2nd hidden layer respectively.
The Hyperbolic Tangent Sigmoid [3] was chosen as transfer function
for both the 1st and the 2nd hidden layers and the linear function
was chosen for the output layer. More specifically:
f1() =
, f2() =
, f3() = (1)
The extrusion process ANN model that was created had 3 inputs and 9
outputs. The 3 model inputs are: the micro-tube inner & outer
diameters (mm), and also the material type, which is expressed by
the material density (Kg/cm3). The 9 model outputs are: the process
die temperature (C), the melt density (Kg/m3), the melt flow (rpm),
the extrusion die inner diameter (mm), the extrusion die
outer
Output Layer2nd Layer1st Layer
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
2
diameter (mm), the extrusion die pressure (bar), the die Nitrogen
inside pressure (bar), the distance to the cooling media (water)
(cm) and the draw speed (m/min). 30000 iterations were applied for
the training of the ANN, with a learning rate of 0.005, and the
training was stopped when the errors for all model’s outputs, which
was expressed by the absolute value of the difference between the
training target values and the ANNs calculations results, were
minimized to satisfactory levels for all the process outputs. The
Mean Absolute Errors (MAE) achieved for the 9 process output
parameters are:
Table 1. MAE achieved for the 9 process outputs of the 3-20-20-9
ANN model.
Output No Process Parameters (Outputs) Units MAE MAE (%) 1 Process
die temperature °C 1.12651 0.50182 2 Melt density Kg/m3 6.37156
0.63903 3 Melt flow rpm 1.43085 5.93685 4 Extrusion die inner
diameter mm 0.00571 0.69026 5 Extrusion die outer diameter mm
0.00596 0.35029 6 Extrusion die pressure bar 0.78528 5.66754 7 Die
Nitrogen inside pressure bar 0.00325 0.31442 8 Distance to the
cooling media cm 0.03320 3.13089 9 The draw speed m/min 1.90809
9.15724
In Figureure 2, we show 9 comparison charts, which were created
after the training of the ANN that correspond to all the 9 process
parameters (models’ outputs). Each chart includes two curve lines.
The first line represents the training target values (red dashed
line) and the second line represents the Neural Network calculation
results (blue line) for each of the corresponding outputs. For
testing the behavior of the trained model and for creating the 9
comparison charts, the 100 records of the training inputs data set
were used as inputs to the trained ANN, which provided the 9
corresponding calculation outputs (process parameters) for each
triad of input values. The convergence of the micro- extrusion ANN
training was satisfactory, considering the fact that our model was
a complicated model that had only 3 inputs concerning tube features
(outer and inner diameter and polymer material density) and is
providing 9 outputs, which are the 9 process parameters. After the
training and the testing of the micro-extrusion ANN, the model was
capable to provide as outputs the required 9 process parameters for
any set of inputs, which correspond to the 3 product features
(outer & inner diameter, and material density).
Additionally and besides the above 3-20-20-9 model, the proposed
methodology and the software tool was used and tested also for the
inverse process model. In the inverse model, we used as inputs the
above 9 process parameters and as outputs the above 3 product
features. For this model a 9-15-15- 3 feed-forward Neural Network
was created and trained using the back-propagation algorithm. After
training and testing this ANN, the Mean absolute Errors (MAE)
achieved for all the 3 outputs are:
Table 2. MAE achieved for the 3 process outputs of the 9 Inputs - 3
Outputs ANN model.
Output No Product features Units MAE MAE (%) 1 Outer Diameter mm
0.02371 2.16339 2 Inner Diameter mm 0.02428 3.35821 3 Material
Density Kg/m3 1.22700 0.10206
In Figureure 3, we show also the 3 comparison charts, which were
created after the training of the second ANN model and which
correspond to the above 3 product features (models’ outputs). For
testing the behavior of the 9-15-15-3 trained ANN model and for
creating the 3 comparison charts, the training data set, consisting
of the 100 records, was used with the 9 process parameters being
used as inputs.
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
3
Figure 2.2. Output2 - Melt density (Kg/m3).
Figure 2.3. Output3 - Melt flow (rpm).
Figure 2.4. Output4 - Extrusion die inner diameter (mm).
Figure 2.5. Output5 - Extrusion die outer diameter (mm).
Figure 2.6. Output6 - Extrusion die pressure (bar).
Figure 2.7. Output7 - Die Nitrogen inside pressure (bar).
Figure 2.8. Output8 - Distance to cooling media (water) (cm).
Figure 2.9. Output9 - Draw speed (m/min).
ANNs Results Training Output Target Values
Figure 2. Process outputs comparison charts between ANN’s results
and Target Outputs of the 3- 20-20-9 micro-extrusion ANN
model.
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
4
3. Conclusions The proposed Neural Network methodology was
implemented as a Web application using the .NET programming
environment of Visual Studio 2010. More specifically the
programming languages C#, ASP.NET were used and also the MS-SQL
Server as Database Management system. The whole research work was
supported by the EU FP7 NMP project POLYTUBES (2009-2012) and the
implementation case example, which was presented, concerned a
functional approximation of the POLYTUBES extrusion process for the
production of polymeric micro-tubes based on COC, PC, PET, PETG, PP
and PVDF materials. Especially for the extrusion process, the main
ANN model created included 3 inputs and 9 outputs, in an effort to
support process engineers to calculate the required 9 process
parameters when the 3 product features (tube’s outer/inner diameter
and material density) are provided as inputs. Even though the 3
Inputs / 9 Outputs ANN model is considered a complicated model and
the achieved Mean Absolute training Error (MAE) varies among the 9
model’s outputs, it is considered useful for process engineers
since it gives the possibility to estimate with a satisfactory
approximation of less than one 1% most of the process parameters,
which are required for specific micro-tube’s dimensions and
material type. Additionally, the inverse 9 inputs / 3 outputs ANN
model was created giving the possibility to the process engineers
to cross check the output calculation results of both models.
4. Acknowledgments The authors would like to thank the Swedish
Research Center SWEREA IVF AB and more specifically Dipl. Eng.
Daniel Wendels and Dr. Erik Perzon of SWEREA, who provided the
experimental data concerning the micro-extrusion physical process
of the polymer micro-tubes. Without their contribution, the
training and the testing of the micro-extrusion ANNs models would
not be feasible. Also the authors would like to acknowledge the
support of the European Commission, which funded this research work
through the EU FP7 NMP project POLYTUBES
(http://www.polytubes.net).
References [1] Chronakis I S, Mekras N D, Wiesauer K, Breuer E,
Stifter D, Fuentes G F and Qin Yi 2009
MASMICRO micro-/nano-materials processing, analysis, inspection and
materials knowledge
Figure 3.1. Output1- Outer Diameter (mm)
MAE1 = 2.16339 %.
MAE2 = 3.35821 %.
MAE3 = 0.10206 %.
ANNs Results Training Output Target Values
Figure 3. Process outputs comparison charts between ANN’s results
and Target Outputs of the inverse 9-15-15-3 micro-extrusion ANN
model.
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
5
management Int. J. of Advanced Manufacturing Technology 47 963-971
[2] Duce C et al 2006 Prediction of Polymer Properties from their
Structure by Recursive Neural
Networks J. Macromolecular Rapid Communications 27 711-715 [3]
Hagan M T, Demuth H B and Beale M 1995 Neural Network Design
(Boston: PWS Publishing
Co) p 11.1 [4] Jiang Z et al 2007 Prediction on wear properties of
polymer composites with artificial neural
networks J. Composite Science and Technology 67 168-176 [5] Kadi H
E 2006 Modeling the mechanical behavior of fiber-reinforced
polymeric composite
materials using artificial neural networks – A review J. Composite
Structures 73 1-23 [6] Kasabov N 1996 Foundations of Neural
Networks, Fuzzy Systems, and Knowledge Engineering
(Massachusetts: MIT Press) p 267 [7] Liao T W and Chen L J 1998
Manufacturing Process Modeling and Optimization Based on
Multi-Layer Perceptron Network J. Manuf. Sci. Eng. 120 109-119 [8]
Mok S L et al 2000 An intelligent hybrid system for initial process
parameter setting of
injection moulding International Journal of Production Research 38
4565-4576 [9] Noor R A M, Ahmad Z, Don M M and Uzir M H 2010
Modelling and control of different types
of polymerization processes using neural networks technique:A
review Canadian Journal of Chemical Engineering 88 1065–1084
[10] Nascimento C, Giudici R and Guardani R 2000 Neural network
based approach for optimization of industrial chemical processes J.
Computers & Chemical Engineering 24 2303-2314
[11] Qi L H, Li H J, Hou J J and Cui P L 2000 Research on the
Neural Networks Used for Shaping Tubes by the Liquid Extrusion
Process J. of Materials Engineering and Performance 9 28-32.
[12] Sha W and Edwards K L 2007 The use of artificial Neural
Networks in materials science based research J. Materials &
Design 28 1747-1752
[13] Swingler K 1996 Applying Neural Networks (London: Academic
Press Ltd) p 10 [14] Tzafestas S and Mekras N 1999 Industrial
Forecasting using Knowledge Based Techniques and
Artificial Neural Networks in Advances in Manufacturing: Decision,
Control and Information Technology ed S. Tzafestas (Berlin:
Springer-Verlang) p 171
[15] Zhang Z and Friedrich K 2003 Artificial neural networks
applied to polymer composites: a review J. Composites Science and
Technology 63 2029-2044
International Conference on Structural Nano Composites (NANOSTRUC
2012) IOP Publishing IOP Conf. Series: Materials Science and
Engineering 40 (2012) 012041
doi:10.1088/1757-899X/40/1/012041
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