Mustafa Batuhan KURT, Yelda KARATEPE MUMCU, Lütfi ÖZDEMİR

Flekso Baskı Sisteminde Farklı Tramlama Yöntemlerine Göre Tram Yoğunluğunun Yapay Sinir Ağı Yöntemi ile Tahmini

Tram türü, flekso baskı sisteminde baskı kalitesini etkileyen en önemli faktördür. Makine operatörü tarafından baskı işlemi sırasında yapılan işlemlerin (yoğunluk ölçümü, mürekkep ayarları, vb.) amacı, ilk baskıdan son baskıya kadar aynı kaliteyi sağlamaktır. Bu çalışmada, aynı polimer yapısına sahip (DFR) 175 Lpi tram sıklığında, 10 farklı tramlama yapısından elde edilen tram yoğunluğu değerleri Yapay Sinir Ağları Yöntemi (ANN) kullanılarak tahmin edilmiştir. Deneysel çalışmalar sonucunda hesaplamalar için gerekli olan değerler; tram noktalarının densitometrik ölçümlerinden elde edilmiştir. Tram yoğunluğu değerleri için YSA ile oluşturulan modelden elde edilen verilerin korelasyon katsayısı %98,902 olarak bulunmuştur ve bu değer bilimsel değerlerle uyumlu bulunmuştur. Çıkan sonuçlara göre, YSA modelinde flekso baskı sisteminde kullanılan farklı tramlama yöntemlerinin baskı sonucuna etkisi önceden tahmin edilebilir.

Estimation Of Screen Density According To Different Screening Methods With Artificial Neural Network Method In Flexo Printing System

Choice of dot shape is the most important factors that affect the printing quality in the flexographic printing system. The aim of the operations performed by the machine operator during the printing process (densitometric measurements, ink settings, etc.) is to achieve the same quality from the first printing to last printing. This study attempts to estimate screen density values obtained from the same polymer structure (DFR), 175 Lpi screening and 10 different screen structures using the Artificial Neural Networks method (ANN). Data necessary for calculations were obtained from real values as a result of experimental studies. The correlation coefficient of the data obtained from the model created with ANN for screen density values was found to be 98,902% and this value was found to be consistent with scientific values. According to the results, the neural network model used in flexographic printing systems of different screening methods predictable effect on the printing result.

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