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PLASTİK ENJEKSİYON KALIPLAMADA ISKARTA ORANI TAHMİNİNDE YAPAY SİNİR AĞLARI VE ÇOKLU DOĞRUSAL REGRESYON MODELLERİN KARŞILAŞTIRILMASI

A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION MODELS AS PREDICTORS OF DISCARD RATES IN PLASTIC INJECTION MOLDING

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Publication Year:

  • 2015

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Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In today’s global competitive environment, it is important to be able to evaluate the efficient use of a firms’ resources. The aim of this study is to predict the discard rate for headlight frames before the project of an automotive sub-industry firm in Bursa. For this prediction, the multilayer perceptron model, the radial basis function network model and multiple linear regression models were used. Matlab R2010b software was used for the multilayer perceptron model and radial basis function network solutions, and SPSS 13 packet software was used to solve the multiple linear regressions. Comparing the three models, the multilayer perceptron model was identified as the best predictive model.
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Abstract (Original Language): 
Günümüz küresel rekabet koşullarında firmaların kaynakları etkin kullanarak değerlendirmesi oldukça önemli bir konudur. Bu çalışmanın amacı Bursa’da bir otomotiv yan sanayi firmasının proje öncesinde far çerçeve parçasının ıskarta oranını tahmin etmektir. Bu tahmin için yapay sinir ağ modellerinden çok katmanlı algılayıcı model, radyal tabanlı fonksiyon ağ modeli ve çoklu doğrusal regresyon model teknikleri kullanılmıştır. Çalışmada çok katmanlı algılayıcı model ve radyal tabanlı fonksiyon ağ model çözümleri için Matlab R2010b programı, çoklu doğrusal regresyon model çözümü için SPSS 13 paket programı kullanılmıştır. Firmanın ıskarta oranı tahmininde bu üç model kıyaslanmış ve en uygun modelin çok katmanlı algılayıcı model olduğu belirlenmiştir.
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PDF icon 10.17093-aj.2015.3.2.5000149667-270489.pdf
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REFERENCES

References: 

Arıkan Kargı, V.S.(2015). Yapay Sinir Ağ Modelleri ve Bir Tekstil Firmasında Uygulama. Bursa: Ekin Yayınevi.
Chang, P., Hwang, S., Lee, H. and Huang, D.(2007). Development of an External-Type Microinjection Molding Module for Thermoplastic Polymer. Journal of Materials Processing Technology, 184(1-3), 163-172.
Changyu, S., Lixia, W. and Qian, L.(2007). Optimization of Injection Molding Process Parameters Using Combination of Artificial Neural Network and Genetic Algorithm Method. Journal of Materials Processing Technology, 183(1), 412-418.
Elmas, Ç.(2011). Yapay Zeka Uygulamaları, Seçkin yayınevi, Ankara.
Haykın, S.(1999). Neural Networks: A Comprehensive Foundation, New Jersey.
Karataş, Ç., Sözen, A., Arcaklıoğlu and E., Ergüney, S.(2007). Modelling of Yield Length in The Mould of Commercial Plastics Using Artificial Neural Networks. Materials and Design, 28(1), 278-286.
Kaynar, O.,Taştan, S. and Demirkoparan, F.(2010). “Ham Petrol Fiyatlarının Yapay Sinir Ağları ile Tahmini”, Ege Academic Review, 10(2), 575-596.
Kim, K. B. and Kim, C. K.(2004). “Performance Improvement of RBF Network Using ART2 Algorithm and Fuzzy Logic System” , Australia Conference on Artificial Intelligence.
Kuo-Ming T. and Hao-Jhih L.(2015). “Comparison of Injection Molding Process Windows for Plastic Lens Established By Artificial Neural Network and Response Surface Methodology”, International Journal of Advanced Manufacturing Technology, 77(9), 1599- 1611.
Lau, H.C.W., Wong, T.T. and Pun, K.F. (1999). Neural-Fuzzy Modeling of Plastic Injection Molding Machine for Intelligent Control. Expert System with Applications, 17(1), 33–43.
Lippmann, R. P.(1987). An Introduction to Computing with Neural Nets, IEEE ASSP Magazine, 4(2), 4-22.
Öktem, H., Erzurumlu, T. and Erzincanlı, F.(2006). Prediction of Minimum Surface Roughness in End Milling Mold Parts Using Neural Network and Genetic Algorithm. Materials and Design, 27(1), 735-744.
Özek ,C. and Çelik, Y. H.(2011). Plastik Enjeksiyon Kalıplarında Enjeksiyon Sürelerinin Yapay Sinir Ağları ile Modellenmesi. Fırat Üniv. Mühendislik Bilimleri Dergisi, 23(1), 35-42.
Öztemel, E.(2003). Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
Rewal,N. and Toncich,D.(1998). Predicting Part Quality In Injection Molding Using Artificial Neural Networks. Journal of Injection Molding Technology, 4(2), 109-119.
Sadeghi, B.H.M.(2000). A BP- Neural Network Predictor Model for Plastic Injection Molding Process. Journal of Materials Processing Technology,103(3), 411–416.
Tso GK and Yau KK.(2007). “Predicting Electricity Energy Consumption: A Comparision of Regression Analysis, Decision Tree and Neural Networks”, Energy, 32(9), 1761-1768.
Womack, P.J., Daniel J.,T. and Ross, D.(1990). Dünyayı Değiştiren Makine, Çeviri Osman Kabak. İstanbul: Panel Matbaacılık.
Zhu, J. and Chen, J.C.(2006). Fuzzy Neural Network Based in Process Mixed Material Caused Flash Prediction (FNN-IPMFP) in Injection Molding Operations. International Journal of Advanced Manufacturing Technology, 29(1), 308-316.

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