STP1532

    Modeling of Hardness of Low Alloy Steels by Means of Neural Networks

    Published: Apr 2012


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    Abstract

    The tempering process aims to get the microstructures that lead to service mechanical properties and to promote the relaxation of the residual stresses generated during quenching. The goal of this work is to predict the effect of tempering time and tempering temperature on hardness by means of neural networks (NNs). Five types of steels, SAE 4140, SAE 4340, SAE 5160, SAE 6150, and SAE 52 100, were tempered in different conditions. The inputs of the NNs were the chemical composition, the tempering time, and tempering temperature, while hardness was the output. The selected temperatures were 100, 150, 200, 250, 300, 400, 500, 600, and 700°C. The time on each temperature was 10, 90, 900, 3600, 9000, and 86 400 s. Many architectures were tested, until the best one that fitted the data was found. To evaluate this NN the correlation coefficient (R value) was calculated and an analysis of variance test was performed.

    Keywords:

    tempering, modeling, hardness, neural networks


    Author Information:

    Penha, Renata Neves
    Dept. of Materials, Engineering School of São Carlos–Univ. of São Paulo, São Carlos, SP

    Canale, Lauralice Franceschini
    Dept. of Materials, Engineering School of São Carlos–Univ. of São Paulo, São Carlos, SP

    Canale, Antonio Carlos
    Dept. of Materials, Engineering School of São Carlos–Univ. of São Paulo, São Carlos, SP


    Paper ID: STP153220120017

    Committee/Subcommittee: D02.L0

    DOI: 10.1520/STP153220120017


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