468
O. Ogorodnyk et al.
Gao H et al (2018) Process parameters optimization using a novel classification model for plastic
injection molding. Int J Adv Manuf Technol 94(1–4):357–370
Guo W et al (2012) Prediction of warpage in plastic injection molding based on design of
experiments. J Mech Sci Technol 26(4):1133–1139
ISO (2012) ISO 527-2:2017 Plastics—Determination of tensile properties—Part 2: Test conditions
for moulding and extrusion plastics. 2012 [cited 2019 31.01.2019]; Available from: https://www.
iso.org/standard/56046.html
ISO (2015) ISO 16012:2015 Plastics—Determination of linear dimensions of test specimens. [cited
2019 25.06.2019]; Available from: https://www.iso.org/standard/63481.html
Johnston S et al (2015) On-line multivariate optimization of injection molding. Polym Eng Sci
55(12):2743–2750
Kalmegh S (2015) Analysis of weka data mining algorithm reptree, simple cart and randomtree for
classification of indian news. Int J Innovative Sci Eng Technol 2(2):438–446
Kononenko I, Kukar M (2007) Machine learning and data mining: introduction to principles and
algorithms. Horwood Publishing
Kozjek D et al (2019) Data mining for fault diagnostics: A case for plastic injection molding. In:
52nd CIRP conference on manufacturing systems (CMS), June 12–14, 2019. Ljubljana, Slovenia:
Procedia CIRP
Kuo C-FJ, Su T-L, Li Y-C (2007) Construction and analysis in combining the Taguchi method and
the back propagation neural network in the PEEK injection molding process. Poly-Plast Technol
Eng 46(9):841–848
Liau Y, Lee H, Ryu K (2018) Digital twin concept for smart injection molding. IOP Conf Ser: Mater
Sci Eng. IOP Publishing
Liu J et al (2017) Multiobjective optimization of injection molding process parameters for the
precision manufacturing of plastic optical lens. Math Probl Eng 2017
Lotti C, Ueki M, Bretas R (2002) Prediction of the shrinkage of injection molded iPP plaques using
artificial neural networks. J Injection Molding Technol 6(3):157
Manjunath PG, Krishna P (2012) Prediction and optimization of dimensional shrinkage variations
in injection molded parts using forward and reverse mapping of artificial neural networks. Adv
Mater Res. Trans Tech Publ
Mathivanan D, Nouby M, Vidhya R (2010) Minimization of sink mark defects in injection molding
process–taguchi approach. Int J Eng Sci Technol 2(2):13–22
Nagorny P et al (2017) Quality prediction in injection molding. in 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and
applications (CIVEMSA). IEEE
Ogorodnyk O et al (2018) Application of machine learning methods for prediction of parts quality
in thermoplastics injection molding. Int Workshop Adv Manuf Autom Springer
Ogorodnyk O, Martinsen K (2018) Monitoring and control for thermoplastics injection molding a
review. Procedia CIRP 67:380–385
Panchal RR, Kazmer DO (2010) In-situ shrinkage sensor for injection molding. J Manuf Sci Eng
132(6):064503
Scikit-Learn (2018) Choosing the right estimator. [cited 2018 13.05]; Available from: https://scikitlearn.org/stable/tutorial/machine_learning_map/index.html
Seaholm SK, Ackerman E, Wu S-C (1988) Latin hypercube sampling and the sensitivity analysis
of a monte carlo epidemic model. 23(1–2):97–112
Shi F et al (2003) Optimisation of plastic injection moulding process with soft computing. Int J
Adv Manuf Technol 21(9):656–661
Tercan H et al (2018) Transfer-learning: bridging the gap between real and simulation data for
machine learning in injection molding. Procedia CIRP 72:185–190
WEKA—Waikato environment for knowledge analysis. [cited 2019 04.03.2019]; Available from:
https://www.cs.waikato.ac.nz/ml/weka/
Wortberg J, Schiffers R (2006) Online quality prediction in injection molding processes (ICM 2006).
In: 2006 IEEE International conference on mechatronics. IEEE
O. Ogorodnyk et al.
Gao H et al (2018) Process parameters optimization using a novel classification model for plastic
injection molding. Int J Adv Manuf Technol 94(1–4):357–370
Guo W et al (2012) Prediction of warpage in plastic injection molding based on design of
experiments. J Mech Sci Technol 26(4):1133–1139
ISO (2012) ISO 527-2:2017 Plastics—Determination of tensile properties—Part 2: Test conditions
for moulding and extrusion plastics. 2012 [cited 2019 31.01.2019]; Available from: https://www.
iso.org/standard/56046.html
ISO (2015) ISO 16012:2015 Plastics—Determination of linear dimensions of test specimens. [cited
2019 25.06.2019]; Available from: https://www.iso.org/standard/63481.html
Johnston S et al (2015) On-line multivariate optimization of injection molding. Polym Eng Sci
55(12):2743–2750
Kalmegh S (2015) Analysis of weka data mining algorithm reptree, simple cart and randomtree for
classification of indian news. Int J Innovative Sci Eng Technol 2(2):438–446
Kononenko I, Kukar M (2007) Machine learning and data mining: introduction to principles and
algorithms. Horwood Publishing
Kozjek D et al (2019) Data mining for fault diagnostics: A case for plastic injection molding. In:
52nd CIRP conference on manufacturing systems (CMS), June 12–14, 2019. Ljubljana, Slovenia:
Procedia CIRP
Kuo C-FJ, Su T-L, Li Y-C (2007) Construction and analysis in combining the Taguchi method and
the back propagation neural network in the PEEK injection molding process. Poly-Plast Technol
Eng 46(9):841–848
Liau Y, Lee H, Ryu K (2018) Digital twin concept for smart injection molding. IOP Conf Ser: Mater
Sci Eng. IOP Publishing
Liu J et al (2017) Multiobjective optimization of injection molding process parameters for the
precision manufacturing of plastic optical lens. Math Probl Eng 2017
Lotti C, Ueki M, Bretas R (2002) Prediction of the shrinkage of injection molded iPP plaques using
artificial neural networks. J Injection Molding Technol 6(3):157
Manjunath PG, Krishna P (2012) Prediction and optimization of dimensional shrinkage variations
in injection molded parts using forward and reverse mapping of artificial neural networks. Adv
Mater Res. Trans Tech Publ
Mathivanan D, Nouby M, Vidhya R (2010) Minimization of sink mark defects in injection molding
process–taguchi approach. Int J Eng Sci Technol 2(2):13–22
Nagorny P et al (2017) Quality prediction in injection molding. in 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and
applications (CIVEMSA). IEEE
Ogorodnyk O et al (2018) Application of machine learning methods for prediction of parts quality
in thermoplastics injection molding. Int Workshop Adv Manuf Autom Springer
Ogorodnyk O, Martinsen K (2018) Monitoring and control for thermoplastics injection molding a
review. Procedia CIRP 67:380–385
Panchal RR, Kazmer DO (2010) In-situ shrinkage sensor for injection molding. J Manuf Sci Eng
132(6):064503
Scikit-Learn (2018) Choosing the right estimator. [cited 2018 13.05]; Available from: https://scikitlearn.org/stable/tutorial/machine_learning_map/index.html
Seaholm SK, Ackerman E, Wu S-C (1988) Latin hypercube sampling and the sensitivity analysis
of a monte carlo epidemic model. 23(1–2):97–112
Shi F et al (2003) Optimisation of plastic injection moulding process with soft computing. Int J
Adv Manuf Technol 21(9):656–661
Tercan H et al (2018) Transfer-learning: bridging the gap between real and simulation data for
machine learning in injection molding. Procedia CIRP 72:185–190
WEKA—Waikato environment for knowledge analysis. [cited 2019 04.03.2019]; Available from:
https://www.cs.waikato.ac.nz/ml/weka/
Wortberg J, Schiffers R (2006) Online quality prediction in injection molding processes (ICM 2006).
In: 2006 IEEE International conference on mechatronics. IEEE