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<p>This study develops an Artificial Neural Network (ANN) model to predict the hardness of industrial C–Mn and low-alloy cast steels using chemical composition and heat-treatment parameters. The optimized 19–20–20–1 architecture achieved about 95% accuracy for training and 90% for testing datasets. Error analysis confirmed high reliability, with 78.5% of training and 74% of testing samples showing prediction errors below 2%, and only 6% exceeding 6%. Although the testing R<sup>2</sup> value was relatively low due to repeated hardness values (148–155 BHN dominating most samples), the model achieved low mean absolute (3.30 BHN) and percentage (2.15%) errors, indicating strong predictive agreement. Sensitivity analysis identified carbon, soaking time, and cooling time as the key factors affecting hardness. The proposed ANN provides a cost-effective, data-driven framework for virtual experimentation, alloy design, and process optimization in industrial steel casting and heat-treatment applications.</p>

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Modeling Hardness in Industrial C–Mn Cast Steels with Neural Network Models

  • 期刊论文

  • 发表日期：

    2026年5月17日

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  • Saurabh Tiwari,
  • Muhammad Ishtiaq,
  • Niveditha Yeddula,
  • M. Mohan Reddy,
  • Jae-Bok Seol,
  • Nokeun Park,
  • N. S. Reddy

摘要

This study develops an Artificial Neural Network (ANN) model to predict the hardness of industrial C–Mn and low-alloy cast steels using chemical composition and heat-treatment parameters. The optimized 19–20–20–1 architecture achieved about 95% accuracy for training and 90% for testing datasets. Error analysis confirmed high reliability, with 78.5% of training and 74% of testing samples showing prediction errors below 2%, and only 6% exceeding 6%. Although the testing R2 value was relatively low due to repeated hardness values (148–155 BHN dominating most samples), the model achieved low mean absolute (3.30 BHN) and percentage (2.15%) errors, indicating strong predictive agreement. Sensitivity analysis identified carbon, soaking time, and cooling time as the key factors affecting hardness. The proposed ANN provides a cost-effective, data-driven framework for virtual experimentation, alloy design, and process optimization in industrial steel casting and heat-treatment applications.