Scholarlink

<p>Current research presents a Monte Carlo simulation (MCS)-generated multi-objective optimization framework to determine the optimal rolling parameters for AZ61 magnesium alloy. Due to the strength–ductility trade-off inherent in hexagonal close-packed magnesium alloys, selecting suitable thermo-mechanical conditions is challenging. Quadratic regression models for hardness and tensile strength were developed using 250 experimentally validated samples. The models were highly predictive, with <i>R</i><sup>2</sup> = 0.94 and <i>R</i><sup>2</sup> = 0.91, and prediction errors were under 5%. The optimal condition was determined as 200 °C by stochastic sampling of rolling temperature (100-400&#xa0;°C), number of passes (1-4), and initial thickness (4-10&#xa0;mm). The yield strength was experimentally verified at 120&#xa0;MPa, tensile strength at 290&#xa0;MPa, and elongation at 13%, which is much better than the as-cast condition. XRD analysis at 200&#xa0;°C confirmed <i>α</i>-Mg phase stability with controlled <i>β</i>-Mg<sub>17</sub>Al<sub>12</sub> precipitation and moderated basal texture intensity, explaining the enhanced mechanical response. The proposed MCS framework provides a statistically robust and computationally efficient approach for optimizing AZ-series alloy rolling parameters in lightweight structural applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-objective Computational Optimization of AZ61 Magnesium Alloy Rolling Parameters Using Monte Carlo Simulation Framework

  • 期刊论文

  • 发表日期：

    2026年5月23日

查看全文
  • Amit Tiwari,
  • Aman Sharma,
  • Sasmita Nayak

摘要

Current research presents a Monte Carlo simulation (MCS)-generated multi-objective optimization framework to determine the optimal rolling parameters for AZ61 magnesium alloy. Due to the strength–ductility trade-off inherent in hexagonal close-packed magnesium alloys, selecting suitable thermo-mechanical conditions is challenging. Quadratic regression models for hardness and tensile strength were developed using 250 experimentally validated samples. The models were highly predictive, with R2 = 0.94 and R2 = 0.91, and prediction errors were under 5%. The optimal condition was determined as 200 °C by stochastic sampling of rolling temperature (100-400 °C), number of passes (1-4), and initial thickness (4-10 mm). The yield strength was experimentally verified at 120 MPa, tensile strength at 290 MPa, and elongation at 13%, which is much better than the as-cast condition. XRD analysis at 200 °C confirmed α-Mg phase stability with controlled β-Mg17Al12 precipitation and moderated basal texture intensity, explaining the enhanced mechanical response. The proposed MCS framework provides a statistically robust and computationally efficient approach for optimizing AZ-series alloy rolling parameters in lightweight structural applications.