Prediction of notch strength ratio of a notched tensile ductile iron using multiple linear regression model

Yıl 2025, Cilt: 27 Sayı: 2, 475 - 487

Gülcan Toktaş

https://doi.org/10.25092/baunfbed.1628397

Öz

This study aims to create a model by examining the effect of the matrix structure and notch root radius on the notch strength ratio (NSR) in an alloyed ductile cast iron through multiple linear regression analysis. For this purpose, several heat treatments were applied to obtain ferritic, pearlitic/ferritic, pearlitic, tempered martensitic, lower bainitic, and upper bainitic matrix structures in cast iron. Hardness and tensile tests were applied to determine the hardness and 0.2 yield strength of matrix structures, which were then considered independent variables in the regression analysis. Additionally, tensile tests were conducted on circumferentially V-notched samples with a notch root radius range of 0.05-0.8 mm, and the notch radius was used as the third independent variable in the analysis. A model was developed by multiple regression to predict the NSR with the aid of the hardness, 0.2 yield strength, and notch radius independent variables. The analysis had an adjusted R2 value of 0.886, which explains that 88.6 % of predicted NSR values can be varied by the hardness, 0.2 yield strength, and notch root radius. The model predicted satisfactory NSR values in matrix structures, exhibiting only minimal residuals.

Anahtar Kelimeler

Ductile iron, matrix structure, regression analysis, notch strength ratio.

Sünek dökme demirde çentik mukavemet oranının çoklu doğrusal regresyon modeli ile tahmini

Öz

Bu çalışmada, alaşımlı sünek dökme demirde matris yapısı ve çentik kök yarıçapının çentik mukavemet oranına olan etkisi çoklu doğrusal regresyon analizi ile incelenerek bir model oluşturulması hedeflenmiştir. Bu amaçla, dökme demirde ferritik, perlitik/ferritik, perlitik, temperlenmiş martensitik, alt beynitik ve üst beynitik matris yapıları elde etmek için çeşitli ısıl işlemler uygulanmıştır. Matris yapıların sertliğini ve 0,2 akma dayanımını belirlemek için sertlik ve çekme testleri uygulanmış ve bunlar regresyon analizinde bağımsız değişkenler olarak ele alınmıştır. Ayrıca, 0,05-0,8 mm çentik kök yarıçapı aralığına sahip çevresel V çentikli numuneler üzerinde çekme testleri uygulanmış ve elde edilen çentikli çekme dayanımları regresyon analizinde üçüncü bağımsız değişken olarak kullanılmıştır. Sertlik, 0,2 akma dayanımı ve çentik kök yarıçapı bağımsız değişkenleri ile çentik mukavemet oranını tahmin etmek için çoklu regresyon analizi yardımıyla bir model geliştirilmiştir. Analiz sonucunda, 0,886 değerinde ayarlanmış bir R2 değeri elde edilmiştir. Bu sonuç, tahmin edilen çentik mukavemet oranı değerlerinin %88,6'sının sertlik, 0,2 akma dayanımı ve çentik kök yarıçapı ile değiştirilebileceğini açıklamaktadır. Elde edilen model ile, matris yapılarda yalnızca minimal farklar sergileyen tatmin edici çentik mukavemet oranları öngörülmüştür.

Anahtar Kelimeler

Sünek dökme demir, matris yapı, regresyon analizi, çentik mukavemet oranı.

Kaynakça

• T. Ikedaa, N. Nodab, Y. Sano, Conditions for Notch Strength to be Higher Than Static Tensile Strength in High–Strength Ductile Cast Iron, Engineering Fracture Mechanics, 206, 75–88, (2019).
• L. Collini, A. Pirondi, Microstructural, Multilevel Simulation of Notch Effect in Ferritic Ductile Cast Iron Under Low Cycle Fatigue, International Journal of Fatigue, 162, (2022).
• H. T. Quraishi, M. A. Siddiqui, D. M. Naqvi, M. Zeeshan, M. Waseem, Z. Noreen, Machine Learning Prediction of Mechanical Properties in Reinforcement Bars: A Data-Driven Approach, International Journal of Innovations in Science & Technology, 6, 2, 413–425, (2024).
• H. Yurtkuran, M. E. Korkmaz, M. Günay, Modelling and Optimization of the Surface Roughness in High-Speed Hard Turning with Coated and Uncoated CBN Insert, Gazi University Journal of Science GU J Sci, 29, 4, 987–995, (2016).
• D. M. Jones, J. Watton, K. J. Brown, Comparison of Hot Rolled Steel Mechanical Property Prediction Models Using Linear Multiple Regression, Non-Linear Multiple Regression, and Non-Linear Artificial Neural Networks, Ironmaking and Steelmaking, 32, 5, 435–442, (2005).
• K. A. Kasvayee, E. Ghassemali, I. L. Svensson, J. Olofsson, A.E.W. Jarfors, Characterization and Modeling of the Mechanical Behavior of High Silicon Ductile Iron, Materials Science & Engineering A, 708, 159–170, (2017).
• L.P. Dix, R. Ruxanda, J. Torrance, M. Fukumoto, D.M. Stefanescu, Static Mechanical Properties of Ferritic and Pearlitic Lightweight Ductile Iron Castings, AFS Transactions, 03, 109, 1149–1164, (2003).
• S. Biswas, C. Monroe, T. Prucha, Use of Published Experimental Results to Validate Approaches to Gray And Ductile Iron Mechanical Properties Prediction, International Journal of Metalcasting, 11, 4, 656–674, (2017).
• D. Franzen, B. Pustal, A. Bührig-Polaczek, Influence of Graphite-Phase Parameters on The Mechanical Properties of High-Silicon Ductile Iron, International Journal of Metalcasting 17, 1, 4–21, (2023).
• J. Laine, J. Vaara, K. Jalava, K. Soivio, J. Orkas, T. Frondelius, The Mechanical Properties of Ductile Iron at Intermediate Temperatures: The Effect of Silicon Content and Pearlite Fraction, International Journal of Metalcasting 15, 2, 538–547, (2021).
• N. Ram, V. Gautam, Prediction of Effect of Alloying Elements on Properties of Ferritic Grade Si-Mo Ductile Cast Iron Using Regression Analysis, Materials Today: Proceedings, 62, 3855–3859, (2022).
• G. Matache, A. Paraschiv, M. R. Condruz, Tensile Notch Sensitivity of Additively Manufactured IN 625 Superalloy, Materials, 13, 4859, (2020).
• ISO 6506-1, Metallic materials-Brinell hardness test, Part 1: Test method, Switzerland, (2014).
• L. Wang, Mechanical Properties of Tempered Martensite, PhD Thesis, Department of Materials Science and Engineering, Monash University Victoria, Australia, (2020).
• R. Qu, P. Zhang, Z. Zhang, Notch Effect of Materials: Strengthening or Weakening?, Journal of Materials Science and Technology, 30, 6, 599–608, (2014).
• C. Pandey, M.M. Mahapatra, P. Kumar, N. Saini, Effect of Strain Rate and Notch Geometry on Tensile Properties and Fracture Mechanism of Creep Strength Enhanced Ferritic P91 Steel, Journal of Nuclear Materials, 498, 176–186, (2018).
• A. Durmus, H. Aydin, M. Tutar, A. Bayram, K. Yigit, Effect of the Microstructure on the Notched Tensile Strength of As-cast and Austempered Ductile Cast Irons, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, (2012).
• C. Hagquist, M. Stenbeck, Goodness of Fit in Regression Analysis – R2 and G2 Reconsidered, Quality & Quantity 32, 229–245, (1998).
• A. A. Kanaeva, E. N. Reshetkinab, A. T. Kanaev, Use of the Multiple Regression Analysis for Quantitative Estimation of the Mechanical Properties of Strengthened Rebars, Steel in Translation, 49, 8, 568–573, (2019).
• A. Bayram, A. Uguz, A. Durmus, Rapid Determination of the Fracture Toughness of Metallic Materials Using Circumferentially Notched Bars, Journal of Materials Engineering and Performance, 11, 5, 571–576, (2002).