Machinability study of plain carbon steels using data mining technique.

Steel has found exhaustive engineering applications due to the abundant availability of its main constituent (Fe) in the earth‟s crust in the form of Fe₂O₃. It is produced with the requisite addition of carbon with iron. It can be made to exhibit a great variety of microstructures and thus has a wide range of mechanical properties to meet the demands of diverse engineering applications. Among enormous steel specifications, plain carbon steel accounts for more than 90% of the total steel production due to its favorable ductility, toughness and elongation properties, which also make it compliant to simple heat treatment operations. The objective of this paper is to investigate the effects of chemical composition (carbon, manganese, phosphorus, sulphur and iron) of plain carbon steel on its various mechanical properties using one of the data mining techniques, i.e. classification. The decision tree analysis is performed using classification and regression tree (C&RT) algorithm to identify the most significant chemical elements influencing machinability, Brinell hardness, tensile strength and elongation of plain carbon steels. The overall classification accuracy and prediction risk are estimated to explore the performance of C&RT algorithm. [ABSTRACT FROM AUTHOR]