Politeknik Dergisi 2147-9429 Gazi Üniversitesi Engineering Mühendislik Yapay Sinir Ağları ile Dinamik Ağırlık Tahmin Uygulaması Dynamic Weight Estimation Application with Artificial Neural Networks Yumurtacı Mehmet Yabanova İsmail 03 01 2017 20 1 37 41 05 27 2016 Copyright © 1998, Politeknik Dergisi 1998 Politeknik Dergisi

Üretim kapasitelerinin hızla arttığı günümüzde üretilen ürünlerin hızlı bir şekilde tartımı önemli bir konu haline gelmiştir. Bu hız ihtiyacından dolayı da ürünlerin tartım platformu üzerinde durdurulmadan tartılması gerekmektedir. Ürünlerin tartım platformu üzerinde durdurulmadan hareket halinde iken tartılması işlemine dinamik tartım denmektedir. Ancak dinamik tartım sistemlerinde tartılan ürünün hareketli olmasından dolayı ölçüm sinyali gürültülü olmaktadır. Ürünün ağırlığının belirli bir süre içerisinde tespit edilebilmesi için çeşitli yöntemlerin kullanılması gerekmektedir. Bu çalışmada hareket halinde tartılan ürünlerin ağırlıklarının tahminini gerçekleştirmek için yapay sinir ağları kullanılmıştır. Ölçüm sisteminden alınan ağırlık verileri ile yapay sinir ağı eğitilmiş ve daha sonra ağın performansı test verileri ile değerlendirilmiştir. Sonuçlar değerlendirildiğinde dinamik ölçüm sistemlerinde ağırlık tahmininin yapay sinir ağları ile başarılı bir şekilde yapılabileceği görülmektedir.

In our day when the capacities of manufacturing have increased rapidly, also the speedy weighting of manufactured products has become an important subject. Because of this speed requirement, the products must be weighted on the weighing platform without being stopped. The weighting process of products in motion on the weighting platform without being stopped is called as dynamic weighting. However, in the dynamic weighting systems, the measurement signal is noisy as the weighted product is in motion and various methods must be used to determine the weight of product within a definite period of time. In this study, artificial neural networks were used to predict the weights of the weighted products in motion. The artificial neural network was trained with the weight data taken from the measurement system and then, performance of the network was evaluated with the test data. When the results were assessed, it has been determined that the weight prediction could be made successfully with the artificial neural networks in the dynamic measurement systems.

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