Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons

Abstract

This research evaluates the effect of temperature and time austempering on microstructural characteristics and hardness of ductile iron, validating the results by means of a statistical method for hardness prediction. Ductile iron was subjected to austenitization at 950 °C for 120 min and then to austempering heat treatment in a salt bath at temperatures of 290, 320, 350 and 380 °C for 30, 60, 90 and 120 min. By increasing austempering temperature, a higher content of carbon-rich austenite was obtained, and the morphology of the thin acicular ferrite needles produced at 290 °C turned completely feathery at 350 and 380 °C. A thickening of acicular ferrite needles was also observed as austempering time increased. An inversely proportional behavior of hardness values was thus obtained, which was validated through data analysis, statistical tools and a regression model taking temperature and time austempering as input variables and hardness as the output variable, which achieved a correlation among variables of about 97%. The proposal of a mathematical model for the prediction of hardness in austempered ductile iron represents a numerical approximation which validates the experimental results at 95.20%.

Rule modeling of ADI cast iron structure for contradictory data

“…The fact that there are contradictions in a set, even if they are numerous, does not make it impossible to build a model. The properties of ductile iron have been repeatedly modeled with the use of soft mathematical models, but prediction models were usually used such as: multiple linear regression, artificial neural networks, support vector machine, projection pursuit regression (Kochański et al, 2012;Perzyk & Kochański, 2001;Perzyk et al, 2015;Rodríguez-Rosales et al, 2022;Wilk-Kołodziejczyk et al, 2018). Less frequently, work was undertaken on property modeling with the use of rule-creating tools based on the theory of fuzzy sets and decision trees (Kochański et al, 2013(Kochański et al, , 2014Perzyk & Soroczyński, 2008Perzyk et al, 2011).…”