TOZ METALURJİSİ YÖNTEMİYLE ÜRETİLMİŞ Ni-Ti-Cu ALAŞIMLARININ DİFÜZYON KAYNAĞINDA (BİNDİRME-KAYMA) SONUÇLARININ YAPAY SİNİR AĞLARI (ANN) İLE BELİRLENMESİ

Öz

Bu çalışmada, toz metalurjisi yöntemiyle üretilmiş Ni-Ti-Cu alaşımlarının difüzyon kaynağı sonrasında yapılan bindirme-kayma (Shear-Strength) test sonuçlarının Yapay Sinir Ağları (Artificial Neural Network) yöntemi kullanılarak yapılan eğitme sonrası elde edilen sonuçlarla tutarlılığı araştırılmıştır. Ni-Ti-Cu kompo-zit malzemelerin kimyasal bileşimi % 49  Ni - %51  Ti olup, tozlar 45mm boyutundadır. Difüzyon kaynakları, argon atmosferi altında, 5 MPa sabit basınçta, 940-970 ºC sıcaklıklarda ve 40-60 dk. sürelerde yapılmıştır. Kaynaklı numuneler birleşme bölgesine dik doğrultuda kesilerek, numunelerin optik mikroskop, SEM-EDS analizleri yapılmıştır. Numunelerin kaynak sonrası birleşme kalitesini tespit etmek için bindirme-kayma testleri yapılmıştır ve elde edilen sonuçlar bilgisayar ortamında Yapay Sinir Ağları programında test edilmiş-tir. Test programında kaynak sıcaklıkları ve kaynak süreleri girdi, bindirme-kayma sonuçları da çıktı olarak kullanılmıştır. Gerçek sonuçlar ile Yapay Sinir Ağları test analizi sonuçları birbirleriyle karşılaştırılmış, so-nuçlar arasında bir tutarlılığın olduğu bilgisayar ortamında tespit edilmiştir.

Anahtar Kelimeler

• Ni-Ti,Difüzyon Kaynağı,Bindirme-Kayma,Yapay Sinir Ağları

ARTIFICIAL NEURAL NETWORK (ANN) APPROACH THE PREDICTION OF DIFFUSION BONDING BEHAVIOR (SHEAR STRENGTH) OF Ni-Ti-Cu ALLOYS MANUFACTURED BY POWDER METALLURGY METHOD

Abstract

In this study, Artificial Neural Network approach to prediction of diffusion bonding behavior of Ni-Ti-Cu alloys, manufactured by powder metallurgy process, were obtained using a back-propagation neural network that uses gradient descent learning algorithm. Ni-Ti-Cu composite was manufactured with a chemical composition of 49 % Ni - 51 % Ti in weight percent as mixture with an average dimension of 45mm. Diffusi-on welding process have been made under argon atmosphere, with a constant load of 5 MPa, under the temperature of 940 and 970 ºC, in 40 and 60 minutes experiment time. Microstructure examination at bond interface were investigated by optical microscopy, SEM-EDS. Specimens were tested for shear strength and metallographic evaluations.  After the completion of experimental process and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. In neural networks training module, different temperatures and welding periods were used as input, shear strength of bonded specimens at interface were used as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of diffusion bonding shear strength and behavior.  

Keywords

• Ni-Ti,Diffusion Bonding,Shear Strength,Artificial Neural Network

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