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<p>Sustainable manufacturing of cast components requires precise prediction and control of material properties to minimize defects and enhance performance. This study presents a simulation-based framework for predicting and optimizing pearlite content in Spheroidal Graphite Iron (SGI) castings produced via sand casting. Using a Design of Experiments (DOE) approach with an L27 orthogonal array, the combined influence of pouring temperature, section thickness, and carbon equivalent value (CEV) on cooling rates and microstructure was investigated. Advanced casting simulations, validated through metallographic image analysis, enabled the development of regression models with strong predictive accuracy (R<sup>2</sup> within acceptable limits). Results show that optimized combinations of thinner sections, higher CEV, and controlled pouring temperature effectively tailor cooling behavior and pearlite formation. This sustainable, data-driven methodology enhances structural reliability, reduces waste, and supports process optimization for SGI components, offering significant benefits to automotive and heavy engineering applications where consistent material performance is critical.</p>

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Sustainable Simulation-Based Prediction of Material Properties in Cast Components

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • Digvijay Mhamane,
  • Anand Bewoor

摘要

Sustainable manufacturing of cast components requires precise prediction and control of material properties to minimize defects and enhance performance. This study presents a simulation-based framework for predicting and optimizing pearlite content in Spheroidal Graphite Iron (SGI) castings produced via sand casting. Using a Design of Experiments (DOE) approach with an L27 orthogonal array, the combined influence of pouring temperature, section thickness, and carbon equivalent value (CEV) on cooling rates and microstructure was investigated. Advanced casting simulations, validated through metallographic image analysis, enabled the development of regression models with strong predictive accuracy (R2 within acceptable limits). Results show that optimized combinations of thinner sections, higher CEV, and controlled pouring temperature effectively tailor cooling behavior and pearlite formation. This sustainable, data-driven methodology enhances structural reliability, reduces waste, and supports process optimization for SGI components, offering significant benefits to automotive and heavy engineering applications where consistent material performance is critical.