Mechanical behavior of powder metallurgy steel - Experimental investigation and artificial neural network-based prediction model

Mechanical properties of high-density powder metallurgy (PM) steels have be en evaluated using standard tests, and a theoretical model using the artifi cial neural network (ANN) has been developed. Various heat treatments mere carried out to study their influence on mechanical properties, viz. enduran ce limit (EL), yield strength (YS), and hardness, and also on the carbon co ntent in PM steel. The material containing 0.47% C that was quenched and te mpered at 503 K (QT 503 K) showed the optimum combination of yield strength /ultimate tensile strength (YS/UTS) and EL. The ANN-based model showed exce llent agreement with experimental results. Prediction models based on the A NN are demonstrated for YS as well as for the EL as a function of heat trea tment (ranging from QT 400 K to QT 900 K) and percent carbon (%C) (between 0.1 and 0.5). This mould help the materials engineer suitably design the he at-treatment schedule to obtain the desired/best combination of fatigue and strength properties.