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

abstract

• Mechanical properties of high-density powder metallurgy (PM) steels have been evaluated using standard tests, and a theoretical model using the artificial neural network (ANN) has been developed. Various heat treatments were carried out to study their influence on mechanical properties, viz. endurance limit (EL), yield strength (YS), and hardness, and also on the carbon content in PM steel. The material containing 0.47% C that was quenched and tempered 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 excellent agreement with experimental results. Prediction models based on the ANN are demonstrated for YS as well as for the EL as a function of heat treatment (ranging from QT 400 K to QT 900 K) and percent carbon (%C) (between 0.1 and 0.5). This would help the materials engineer suitably design the heat-treatment schedule to obtain the desired/best combination of fatigue and strength properties.

authors

• Haque, Mohammed

published proceedings

• JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE

author list (cited authors)

• Sudhakar, K. V., & Haque, M. E.

citation count

• 5

complete list of authors

• Sudhakar, KV||Haque, ME

publication date

• January 2001

publisher

• Springer Nature  Publisher

published in

• Journal of Materials Engineering and Performance  Journal

keywords

• Artificial Neural Network
• Endurance Limit
• Heat Treatment
• Microstructure
• Pm Steel
• Yield Strength

Digital Object Identifier (DOI)

• 10.1361/105994901770345303

start page

• 31

end page

• 36

volume

• 10

issue

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1361%2F105994901770345303