Improvement of Manufacturing Processes by Artificial Neural Networks Analysis
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.
Anahtar Kelimeler
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.
Ayrıntılar
Birincil Dil
Türkçe
Konular
İşletme
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
1 Nisan 2018
Gönderilme Tarihi
16 Haziran 2017
Kabul Tarihi
-
Yayımlandığı Sayı
Yıl 2018 Cilt: 18 Sayı: 2