Araştırma Makalesi
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Improvement of Manufacturing Processes by Artificial Neural Networks Analysis

Yıl 2018, Cilt: 18 Sayı: 2, 261 - 271, 01.04.2018
https://doi.org/10.21121/eab.2018237354

Öz

Manufacturing processes consist of activities

affected by a large number of variables. The

aim of this study is to show that improvements

can be made by using artificial neural network

methods at stages of manufacturing such as

planning of processes, forecasting of the future

situation, monitoring and control. In the study, a

manufacturing process with 15 input variables was

modeled using artificial neural networks, network

training was provided, and a trained network was

used to obtain the best output performance in

the current situation. Artificial neural networks

are useful tools in finding out the consequences

of any change that may occur in variables and in

improving the processes with this way. The results

show that artificial neural network models can be

well adapted to manufacturing processes.

Kaynakça

  • 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.
Yıl 2018, Cilt: 18 Sayı: 2, 261 - 271, 01.04.2018
https://doi.org/10.21121/eab.2018237354

Öz

Kaynakça

  • 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.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Salih Aka Bu kişi benim 0000-0002-6386-8582

Gökhan Akyüz 0000-0002-6386-8582

Yayımlanma Tarihi 1 Nisan 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 18 Sayı: 2

Kaynak Göster

APA Aka, S., & Akyüz, G. (2018). Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. Ege Academic Review, 18(2), 261-271. https://doi.org/10.21121/eab.2018237354
AMA Aka S, Akyüz G. Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. eab. Nisan 2018;18(2):261-271. doi:10.21121/eab.2018237354
Chicago Aka, Salih, ve Gökhan Akyüz. “Improvement of Manufacturing Processes by Artificial Neural Networks Analysis”. Ege Academic Review 18, sy. 2 (Nisan 2018): 261-71. https://doi.org/10.21121/eab.2018237354.
EndNote Aka S, Akyüz G (01 Nisan 2018) Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. Ege Academic Review 18 2 261–271.
IEEE S. Aka ve G. Akyüz, “Improvement of Manufacturing Processes by Artificial Neural Networks Analysis”, eab, c. 18, sy. 2, ss. 261–271, 2018, doi: 10.21121/eab.2018237354.
ISNAD Aka, Salih - Akyüz, Gökhan. “Improvement of Manufacturing Processes by Artificial Neural Networks Analysis”. Ege Academic Review 18/2 (Nisan 2018), 261-271. https://doi.org/10.21121/eab.2018237354.
JAMA Aka S, Akyüz G. Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. eab. 2018;18:261–271.
MLA Aka, Salih ve Gökhan Akyüz. “Improvement of Manufacturing Processes by Artificial Neural Networks Analysis”. Ege Academic Review, c. 18, sy. 2, 2018, ss. 261-7, doi:10.21121/eab.2018237354.
Vancouver Aka S, Akyüz G. Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. eab. 2018;18(2):261-7.