AISI 1040 Çeliğinin Mikroyapı Resimlerinden Mekanik Özelliklerinin Derin Öğrenme ile Tahmini

Abstract

Malzeme biliminde işlem-mikroyapı ve mekanik özellikler arasındaki çok iyi bir ilişki bulunmaktadır. Çeliklerin oda sıcaklığındaki mekanik özellikleri doğrudan mikroyapıda bulunan ferrit, sementit ve perlit hacim oranlarına ve tane boyutlarına bağlıdır. Bu çalışmada, AISI 1040 çeliğinin mikroyapı görüntülerinden yapay zekâ ile oda sıcaklığındaki çekme özelliklerinin tahmini gerçekleştirilmiştir. AISI 1040 çeliğinden ASTM-E8/E8M standardına uygun olarak hazırlanan çekme numuneleri oda sıcaklığında çekme testine tabii tutulmuştur. Sonraki adımda aynı çekme numunelerinin deforme olmamış bölgelerinden metalografik numune hazırlanıp mikroyapı resimleri elde edilmiş, ferrit ve perlit hacim oranları görüntü analizi yazılımıyla hesaplanmıştır. Bu veriler ile özgün bir veri seti oluşturulmuştur. Evrişimsel Sinir Ağı kullanılarak, mikroyapı resimlerinden akma, çekme ve kopma gerilimi değerleri tahmin edilmiştir. Gerçekleştirilen deneyler sonucunda mikroyapı resimlerinden AISI 1040 çeliğinin mekanik özelliklerinin başarılı bir şekilde tahmininin gerçekleştirilebileceği ortaya konulmuştur (MSE=4,36, RMSE=2,08, MAE=1,66, R2=0,99).

Keywords

• AISI 1040 çeliği
• mikroyapı
• mekanik özellikler
• derin öğrenme
• evrişimsel sinir ağı

Prediction of Mechanical Properties of AISI 1040 Steel from Microstructure Images with Deep Learning

Abstract

In materials science, there is a well-established relationship between processing, microstructure, and mechanical properties. The mechanical properties of steels at room temperature are directly dependent on the volume fractions and grain sizes of ferrite, cementite, and pearlite present in the microstructure. This study has implemented the prediction of tensile properties at room temperature for AISI 1040 steel using artificial intelligence based on microstructural images. Tensile specimens prepared according to the ASTM-E8/E8M standard from AISI 1040 steel were subjected to tensile testing at room temperature. Subsequently, metallographic samples were prepared from the undeformed regions of the same tensile specimens and microstructural images were obtained, with ferrite and pearlite volume fractions calculated using image analysis software. These data have contributed to a unique dataset. Using a Convolutional Neural Network, yield strength, tensile strength, and elongation at fracture values were predicted from the microstructural images. The experiments demonstrated that the mechanical properties of AISI 1040 steel can be successfully predicted from microstructural images (MSE=4.36, RMSE=2.08, MAE=1.66, R2=0.99).

Keywords

• AISI 1040 steel
• microstructure
• mechanical properties
• deep learning
• convolutional neural network

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