高强度钢轮的热处理控制技术基于支矢量机器的支持
Yanzhong Wang1,2, Libin Zhang3, Yulu Su1
1School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
Scientific reports
|March 4, 2025
概括
机器学习准确预测轮热处理结果,优化碳化层分布和硬度. 这种方法减少了高精度轮的制造周期和成本.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 由于不合理的参数选择,轮热处理往往面临着在实现所需属性的挑战,例如最小的变形和精确的硬度.
- 目前优化热处理参数的方法涉及漫长的试错过程,导致大量的资源和时间浪费.
研究的目的:
- 开发一种机器学习模型,根据热处理参数预测轮的微观结构和特性.
- 提高预测热处理结果的准确性和效率,从而降低制造成本和周期时间.
主要方法:
- 使用热处理参数构建了一个支向量机 (SVM) 预测模型,以预测轮组织分布.
- 为 SVM 选择了射线基函数 (RBF) 内核,其参数被优化以提高预测准确性.
主要成果:
- 最终的SVM模型实现了3.16%的根平均平方误差 (RMSE) 和0.993.99的确定系数 (R2).
- 该模型展示了热处理结果的准确和高效预测,包括硬度和碳化层分布.
结论:
- 开发的机器学习方法可以精确控制超高强度钢轮的硬度,碳化层和金属学组织.
- 这种方法比传统技术有了显著的改进,从而减少了制造周期和轮生产成本.
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