Improvement of Manufacturing Processes by Artificial Neural Networks Analysis
Manufacturing processes consist of activitiesaffected by a large number of variables. Theaim of this study is to show that improvementscan be made by using artificial neural networkmethods at stages of manufacturing such asplanning of processes, forecasting of the futuresituation, monitoring and control. In the study, amanufacturing process with 15 input variables wasmodeled using artificial neural networks, networktraining was provided, and a trained network wasused to obtain the best output performance inthe current situation. Artificial neural networksare useful tools in finding out the consequencesof any change that may occur in variables and inimproving the processes with this way. The resultsshow that artificial neural network models can bewell adapted to manufacturing processes.
___
- Abbasi, B. (2009) “A neural network applied to estimate
process capability of non-normal processes” Expert
Systems with Applications, 36: 3093-3100.
- Alguindigue, I. E., Loskiewicz-Buczak, A. ve Uhrig,
R. E. (1993) “Monitoring and diagnosis of rolling
element bearings using artificial neural Networks” IEEE
transactions on industrial electronics, 40(2): 209-217.
- Andersen, K., Cook, G. E., Karsai, G. ve Ramaswamy, K.
(1990) “Artificial neural networks applied to arc welding
process modeling and control” IEEE Transactions on
industry applications, 26(5): 824-830.
- Azadeh, A., Saberi, M. ve Anvari, M. (2010) “An integrated
artificial neural network algorithm for performance
assessment and optimization of decision making units”
Expert Systems with Applications, 37(8): 5688-5697.
- Azimi, P. ve Soofi, P. (2017) “An ANN-based optimization
model for facility layout problem using simulation
technique” Scientia Iranica E, 24(1): 364-377.
- Basheer, I.A. ve Hajmeer, M. (2000) “Artificial neural
networks: fundamentals, computing, design, and
application” Journal of Microbiological Methods, 43:
3-31.
- Burduk, A., Chlebus, T. ve Waszkowski, R. (2017, Eylül)
“Assessment of the Feasibility of a Production Plan
with the Use of an Artificial Neural Network Model”
In: International Conference on Intelligent Systems in
Production Engineering and Maintenance, s. 179-188.
Springer, Cham.
- Carbonneau, R., Laframboise, K. ve Vahidov, R. (2008)
“Application of machine learning techniques for
supply chain demand forecasting” European Journal
of Operational Research, 184(3): 1140-1154.
- Chen, F. L. ve Liu, S. F. (2000) “A neural-network approach
to recognize defect spatial pattern in semiconductor
fabrication” IEEE transactions on semiconductor
manufacturing, 13(3): 366-373.
- Chou, P. Y., Tsai, J. T. ve Chou, J. H. (2016) “Modeling
and optimizing tensile strength and yield point on a
steel bar using an artificial neural network with taguchi
particle swarm optimizer” IEEE Access, 4: 585-593.
- Confalonieri, M., Barni, A., Valente, A., Cinus, M. ve
Pedrazzoli, P. (2015, Haziran) “An AI based decision
support system for preventive maintenance and
production optimization in energy intensive
manufacturing plants” In: Engineering, Technology
and Innovation/International Technology Management
Conference (ICE/ITMC), IEEE International Conference on,
s. 1-8, IEEE.
- Ding, L. ve Matthews, J. (2009) “A contemporary study
into the application of neural network techniques
employed to automate CAD/CAM integration for die
manufacture” Computers and Industrial Engineering,
57(4): 1457-1471.
- Dorofki, M., Elshafie, A. H., Jaafar, O., Karim, O. A. ve
Mastura, S. (2012) “Comparison of artificial neural
network transfer functions abilities to simulate extreme
runoff data” International Proceedings of Chemical,
Biological and Environmental Engineering, 33: 39-44.
Yapay Sinir Ağları Analizi İle İmalat Süreçlerinin İyileştirilmesi
271
- Efendigil, T., Önüt, S. ve Kahraman, C. (2009) “A
decision support system for demand forecasting with
artificial neural networks and neuro-fuzzy models: A
comparative analysis” Expert Systems with Applications,
36: 6697–6707.
- Fast, M. ve Palme, T. (2010) “Application of artificial
neural networks to the condition monitoring and
diagnosis of a combined heat and power plant” Energy,
35(2), 1114-1120.
- González-Romera, E., Jaramillo-Morán, M. A. ve
Carmona-Fernández, D. (2008) “Monthly electric energy
demand forecasting with neural networks and Fourier
series” Energy Conversion and Management, 49(11),
3135-3142.
- Gumus, A. T., Guneri, A. F., ve Ulengin, F. (2010) “A
new methodology for multi-echelon inventory
management in stochastic and neuro-fuzzy
environments” International Journal of Production
Economics, 128(1): 248-260.
- Haas, D.J., Milano, J. ve Flitter, L. (1995) “Prediction of
helicopter component loads using neural Networks”
Journal of the American Helicopter Society, 40(1): 72–82.
Hakimpoor, H., Arshad, K.A.B., Tat, H.H., Khani, N. ve
Rahmandoust, M. (2011) “Artificial Neural Networks’
Applications in Management” World Applied Sciences
Journal, 14 (7): 1008-1019.
- Hurrion R.D. (1997) “An example of simulation
optimization using a neural network metamodel:
Finding the optimum number of kanbans in
manufacturing system” Journal of Operations Research
Society, 48(11), 1105-1112.
- Janikova, D. ve Bezak, P. (2016, Eylül) “Prediction of
production line performance using neural Networks”
In: Artificial Intelligence and Pattern Recognition (AIPR),
International Conference on, s. 1-5, IEEE.
- Lacher, R. C., Coats, P. K., Sharma, S. C. ve Fant, L. F. (1995)
“A neural network for classifying the financial health of
a firm” European Journal of Operational Research, 85(1):
53-65.
- Lechevalier, D., Hudak, S., Ak, R., Lee, Y.T. ve Foufou,
S. (2015, Ekim) “A neural network meta-model and its
application for manufacturing” In: Big Data (Big Data),
IEEE International Conference on, s. 1428-1435, IEEE.
- Levin, A.U. ve Narendra, K.S. (1993) “Control of
nonlinear dynamical systems using neural networks:
Controllability and Stabilization” IEEE Transactions on
Neural Networks, 4(2), 192–206.
- Lin, Y. H., Shie, J. R. ve Tsai, C. H. (2009) “Using an artificial
neural network prediction model to optimize workin-
process inventory level for wafer fabrication” Expert
Systems with Applications, 36(2): 3421-3427.
- Mitoma, T., Wang, H. ve Chen, P. (2008) “ Fault diagnosis
and condition surveillance for plant rotating machinery
using partially-linearized neural network” Computers &
Industrial Engineering, 55(4), 783-794.
- Rosenblatt, F. (1958) “The perceptron: A probabilistic
model for information storage and organization in the
brain” Psychological review, 65(6): 386.
- Sciuto, G., Bonaccorso, B., Cancelliere, A. ve Rossi, G.
(2009) “Quality control of daily rainfall data with neural
Networks” Journal of Hydrology, 364(1): 13-22.
- Thomassey, S. ve Happiette, M. (2007) “A neural
clustering and classification system for sales forecasting
of new apparel items” Applied Soft Computing, 7(4):
1177-1187.
- Upadhyaya, B. R. ve Eryurek, E. (1992) “Application
of neural networks for sensor validation and plant
monitoring” Nuclear Technology, 97(2): 170-176.
- Yoo, J. S., Hong, S. R. ve Kim, C. O. (2009) “Service level
management of nonstationary supply chain using
direct neural network controller” Expert Systems with
applications, 36(2): 3574-3586.
- Yu, J., Xi L. ve Zhou, X. (2009) “Identifying source(s) of
out-of-control signals in multivariate manufacturing
processes using selective neural network ensemble”
Engineering Applications of Artificial Intelligence, 22(1):
141-152.
- Wang, Q. (2007) “Artificial neural networks as cost
engineering methods in a collaborative manufacturing
environment” International Journal of Production
Economics, 109(1), 53-64.
- Wang, T., Gao, H. ve Qiu, J. (2016) “A combined adaptive
neural network and nonlinear model predictive control
for multirate networked industrial process control”
In: IEEE Transactions on Neural Networks and Learning
Systems, 27(2): 416-425.
- Zhang, G., Patuwo, B.E ve Hu, M.Y. (1998) “Forecasting
with artificial neural networks: The state of the art”
International Journal of Forecasting, 14: 35–62.