Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

References

References
1.
Bourell
,
D. L.
,
Leu
,
M. C.
, and
Rosen
,
D. W.
,
2009
, “
Roadmap for Additive Manufacturing: Identifying the Future of Freeform Processing
,” University of Texas at Austin Laboratory of Freeform Fabrication Advance Manufacturing Center, Austin, TX.
2.
Gibson
,
I.
,
Rosen
,
D.
, and
Stucker
,
B.
,
2014
,
Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing
,
Springer
,
New York
.
3.
Wang
,
X.
,
Jiang
,
M.
,
Zhou
,
Z.
,
Gou
,
J.
, and
Hui
,
D.
,
2017
, “
3D Printing of Polymer Matrix Composites: A Review and Prospective
,”
Compos. Part B Eng.
,
110
, pp.
442
458
.
4.
Rabinovich
,
J. E.
,
2002
, “
Rapid Manufacturing System for Metal, Metal Matrix Composite Materials and Ceramics
,” U.S. Patent No.
6,459,069
.https://patents.google.com/patent/US6459069
5.
Batchelder
,
J. S.
,
Curtis
,
H. W.
,
Goodman
,
D. S.
,
Gracer
,
F.
,
Jackson
,
R. R.
,
Koppelman
,
G. M.
, and
Mackay
,
J. D.
,
1994
, “
Model Generation System Having Closed-Loop Extrusion Nozzle Positioning
,” U.S. Patent No.
5,303,141
.https://patents.google.com/patent/US5303141A/en
6.
Hilmas
,
G. E.
,
Lombardi
,
J. L.
,
Hoffman
,
R. A.
, and
Stuffle
,
K.
,
1996
, “
Recent Developments in Extrusion Freeform Fabrication (EFF) Utilizing Non-Aqueous Gel Casting Formulations
,”
Solid Freeform Fabrication Symposium
(
SFF
), Austin, TX, Aug. 12–14, pp.
443
450
.https://repositories.lib.utexas.edu/handle/2152/70267
7.
Bellehumeur
,
C.
,
Li
,
L.
,
Sun
,
Q.
, and
Gu
,
P.
,
2004
, “
Modeling of Bond Formation Between Polymer Filaments in the Fused Deposition Modeling Process
,”
J. Manuf. Process.
,
6
(
2
), pp.
170
178
.
8.
Atif Yardimci
,
M.
, and
Güçeri
,
S.
,
1996
, “
Conceptual Framework for the Thermal Process Modelling of Fused Deposition
,”
Rapid Prototyp. J.
,
2
(
2
), pp.
26
31
.
9.
Yardimci
,
M. A.
,
Guceri
,
S. I.
,
Agarwala
,
M.
, and
Danforth
,
S. C.
,
1996
, “
Part Quality Prediction Tools for Fused Deposition Processing
,”
International Solid Freeform Fabrication Symposium
(
SFF
), Austin, TX, Aug. 12–14, pp.
539
548
.http://sffsymposium.engr.utexas.edu/Manuscripts/1996/1996-62-Yardimci.pdf
10.
Witherell
,
P.
,
Narayanan
,
A.
, and
Lee
,
J.
,
2011
, “
Using Metamodels to Improve Product Models and Facilitate Inferencing
,”
Fifth IEEE International Conference on Semantic Computing
(
ICSC
), Palo Alto, CA, pp.
506
513
.
11.
Papadrakakis
,
M.
,
Lagaros
,
N. D.
, and
Tsompanakis
,
Y.
,
1998
, “
Structural Optimization Using Evolution Strategies and Neural Networks
,”
Comput. Methods Appl. Mech. Eng.
,
156
(
1–4
), pp.
309
333
.
12.
Varadarajan
,
S.
,
Chen
,
W.
, and
Pelka
,
C. J.
,
2000
, “
Robust Concept Exploration of Propulsion Systems With Enhanced Model Approximation Capabilities
,”
Eng. Optim.
,
32
(
3
), pp.
309
334
.
13.
Atashkari
,
K.
,
Nariman-Zadeh
,
N.
,
Gölcü
,
M.
,
Khalkhali
,
A.
, and
Jamali
,
A.
,
2007
, “
Modelling and Multi-Objective Optimization of a Variable Valve-Timing Spark-Ignition Engine Using Polynomial Neural Networks and Evolutionary Algorithms
,”
Energy Convers. Manage.
,
48
(
3
), pp.
1029
1041
.
14.
Magnier
,
L.
, and
Haghighat
,
F.
,
2010
, “
Multiobjective Optimization of Building Design Using TRNSYS Simulations, Genetic Algorithm, and Artificial Neural Network
,”
Build. Environ.
,
45
(
3
), pp.
739
746
.
15.
Yegnanarayana
,
B.
,
2009
,
Artificial Neural Networks
,
PHI Learning
, New Delhi, India.
16.
Wang
,
G. G.
, and
Shan
,
S.
,
2007
, “
Review of Metamodeling Techniques in Support of Engineering Design Optimization
,”
ASME J. Mech. Des.
,
129
(
4
), pp.
370
380
.
17.
Schmidhuber
,
J.
,
2015
, “
Deep Learning in Neural Networks: An Overview
,”
Neural Networks.
,
61
, pp.
85
117
.
18.
Bengio
,
Y.
,
Goodfellow
,
I. J.
, and
Courville
,
A.
,
2015
, “
Deep Learning
,”
Nature
,
521
, pp.
436
444
.https://www.nature.com/articles/nature14539
19.
Coatanéa
,
E.
,
Roca
,
R.
,
Mokhtarian
,
H.
,
Mokammel
,
F.
, and
Ikkala
,
K.
,
2016
, “
A Conceptual Modeling and Simulation Framework for System Design
,”
Comput. Sci. Eng.
,
18
(
4
), pp.
42
52
.
20.
Montgomery
,
D. C.
,
2017
,
Design and Analysis of Experiments
,
Wiley
, Hoboken, NJ.
21.
Dowdy
,
S.
,
Wearden
,
S.
, and
Chilko
,
D.
,
2011
,
Statistics for Research
,
Wiley
, Hoboken, NJ.
22.
Levy
,
P. S.
, and
Lemeshow
,
S.
,
2013
,
Sampling of Populations: Methods and Applications
,
Wiley
, Hoboken, NJ.
23.
Schillewaert
,
N.
,
Langerak
,
F.
, and
Duharnel
,
T.
,
1998
, “
Non-Probability Sampling for WWW Surveys: A Comparison of Methods
,”
Mark. Res. Soc. J.
,
40
(
4
), pp.
1
13
.
24.
Marshall
,
M. N.
,
1996
, “
Sampling for Qualitative Research
,”
Fam. Pract.
,
13
(
6
), pp.
522
526
.
25.
Robbins
,
H.
,
1985
, “
Some Aspects of the Sequential Design of Experiments
,”
Herbert Robbins Selected Papers
,
Springer
, Berlin, pp.
169
177
.
26.
Plackett
,
R. L.
, and
Burman
,
J. P.
,
1946
, “
The Design of Optimum Multifactorial Experiments
,”
Biometrika
,
33
(
4
), pp.
305
325
.
27.
Roy
,
R. K.
,
2001
,
Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement
,
Wiley
, New York.
28.
Maghsoodloo
,
S.
,
Ozdemir
,
G.
,
Jordan
,
V.
, and
Huang
,
C.-H.
,
2004
, “
Strengths and Limitations of Taguchi's Contributions to Quality, Manufacturing, and Process Engineering
,”
J. Manuf. Syst.
,
23
(
2
), pp.
73
126
.
29.
Efthymiou
,
K.
,
Sipsas
,
K.
,
Mourtzis
,
D.
, and
Chryssolouris
,
G.
,
2015
, “
On Knowledge Reuse for Manufacturing Systems Design and Planning: A Semantic Technology Approach
,”
CIRP J. Manuf. Sci. Technol.
,
8
, pp.
1
11
.
30.
Witherell
,
P.
,
Feng
,
S.
,
Simpson
,
T. W.
,
Saint John
,
D. B.
,
Michaleris
,
P.
,
Liu
,
Z.-K.
,
Chen
,
L.-Q.
, and
Martukanitz
,
R.
,
2014
, “
Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models
,”
ASME J. Manuf. Sci. Eng.
,
136
(
6
), p.
061025
.
31.
Hirtz
,
J.
,
Stone
,
R. B.
,
McAdams
,
D. A.
,
Szykman
,
S.
, and
Wood
,
K. L.
,
2002
, “
A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts
,”
Res. Eng. Des.
,
13
(
2
), pp.
65
82
.
32.
Karnopp
,
D. C.
,
Margolis
,
D. L.
, and
Rosemberg
,
R. C.
,
2012
,
System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems
, Wiley, Hoboken, NJ.
33.
Shim
,
T.
,
2002
, “
Introduction to Physical System Modelling Using Bond Graphs
,” University of Michigan-Dearborn, Dearborn, MI.
34.
Coatanéa
,
E.
,
2005
, “
Conceptual Modelling of Life Cycle Design: A Modelling and Evaluation Method Based on Analogies and Dimensionless Numbers
,” Ph.D. dissertation, Helsinki University of Technology, Espoo, Finland.
35.
Mokhtarian
,
H.
,
Coatanéa
,
E.
,
Paris
,
H.
,
Ritola
,
T.
,
Ellman
,
A.
,
Vihinen
,
J.
,
Koskinen
,
K.
, and
Ikkala
,
K.
, 2016, “
A Network Based Modelling Approach Using the Dimensional Analysis Conceptual Modeling (DACM) Framework for Additive Manufacturing Technologies
,”
ASME
Paper No. DETC2016-60473.
36.
Shavlik
,
J. W.
,
Mooney
,
R. J.
, and
Towell
,
G. G.
,
1991
, “
Symbolic and Neural Learning Algorithms: An Experimental Comparison
,”
Mach. Learn.
,
6
(
2
), pp.
111
143
.
37.
Atlas
,
L.
,
Cole
,
R.
,
Muthusamy
,
Y.
,
Lippman
,
A.
,
Connor
,
J.
,
Park
,
D.
,
El-Sharkawai
,
M.
, and
Marks
,
R. J.
,
1990
, “
A Performance Comparison of Trained Multilayer Perceptrons and Trained Classification Trees
,”
Proc. IEEE
,
78
(
10
), pp.
1614
1619
.
38.
Ahmad
,
S.
, and
Tesauro
,
G.
,
1989
, “
Scaling and Generalization in Neural Networks: A Case Study
,” Advances in Neural Information Processing Systems (
NIPS
), Denver, CO, Nov. 27–30, pp.
160
168
.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1003.3492&rep=rep1&type=pdf
39.
Hochreiter
,
S.
,
Bengio
,
Y.
,
Frasconi
,
P.
, and
Schmidhuber
,
J.
,
2001
, “
Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies
,”
A field Guide to Dynamical Recurrent Neural Networks
,
IEEE Press
, New York.
40.
Panchal
,
G.
,
Ganatra
,
A.
,
Kosta
,
Y. P.
, and
Panchal
,
D.
,
2011
, “
Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network
,”
Int. J. Comput. Theory Eng.
,
3
(
2
), pp.
332
337
.
41.
Dawson
,
C.
, and
Wilby
,
R.
,
2001
, “
Hydrological Modelling Using Artificial Neural Networks
,”
Prog. Phys. Geogr.
,
25
(
1
), pp.
80
108
.
42.
Tu
,
J. V.
,
1996
, “
Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes
,”
J. Clin. Epidemiol.
,
49
(
11
), pp.
1225
1231
.
43.
Ingrassia
,
S.
, and
Morlini
,
I.
,
2005
, “
Neural Network Modeling for Small Datasets
,”
Technometrics
,
47
(
3
), pp.
297
311
.
44.
Towell
,
G. G.
, and
Shavlik
,
J. W.
,
1994
, “
Knowledge-Based Artificial Neural Networks
,”
Artif. Intell.
,
70
(
1–2
), pp.
119
165
.
45.
Psichogios
,
D. C.
, and
Ungar
,
L. H.
,
1992
, “
A Hybrid Neural Network‐First Principles Approach to Process Modeling
,”
AIChE J.
,
38
(
10
), pp.
1499
1511
.
46.
Mokhtarian
,
H.
,
Coatanéa
,
E.
,
Paris
,
H.
,
Mbow
,
M. M.
,
Pourroy
,
F.
,
Marin
,
P. R.
,
Vihinen
,
J.
, and
Ellman
,
A.
,
2018
, “
A Conceptual Design and Modeling Framework for Integrated Additive Manufacturing
,”
ASME J. Mech. Des.
,
140
(
8
), p.
081101
.
47.
Moré
,
J. J.
,
1978
, “
The Levenberg-Marquardt Algorithm: Implementation and Theory
,”
Numerical Analysis
,
Springer
, Berlin, pp.
105
116
.
48.
Anderson
,
J. D.
, and
Wendt
,
J.
,
1995
,
Computational Fluid Dynamics
,
Springer
, Berlin.
49.
Bakrani Balani
,
S.
,
Chabert
,
F.
,
Valerie
,
N.
,
Cantarel
,
A.
, and
Christian
,
G.
,
2017
, “
Toward Improvement of the Properties of Parts Manufactured by FFF (Fused Filament Fabrication) Through Understanding the Influence of Temperature and Rheological Behaviour on the Coalescence Phenomenon
,”
AIP Conf. Proc.
,
1896
(
1
), p.
040008
.
50.
Bridgman
,
P.
,
1922
, “
Dimensional Analysis
,”
Philos. Mag.
,
2
(
12
), pp.
1263
1266
.
51.
Szirtes
,
T.
,
2007
,
Applied Dimensional Analysis and Modeling
,
Butterworth-Heinemann
, Oxford, UK.
52.
Mokhtarian
,
H.
,
Coatanéa
,
E.
, and
Paris
,
H.
,
2017
, “
Function Modeling Combined With Physics-Based Reasoning for Assessing Design Options and Supporting Innovative Ideation
,”
AI Edam
,
31
(
4
), pp.
476
500
.
You do not currently have access to this content.