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使用机器学习技术对D2钢进行各种处理的维克尔硬度的预测建模
Claudia Lorena Mambuscay1,2, Carolina Ortega-Portilla3, Jeferson Fernando Piamba1,2
1Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia.
Materials (Basel, Switzerland)
|May 25, 2024
概括
机器学习可以准确地从缩影图像中预测维克尔硬度,从而消除了手动测量. 随机森林模型表现出卓越的性能,能够快速评估材料和简化工业质量控制.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机科学 计算机科学
背景情况:
- 硬度是材料选择和性能评估的关键机械性质.
- 机器学习 (ML) 为预测材料特性提供了有效的方法.
- 传统的硬度测试需要精确的对角测量痕.
研究的目的:
- 为了预测维克斯硬度值,只使用扫描的单色图像的缩影印记.
- 评估非深度ML回归方法对此任务的有效性.
- 消除在硬度评估中需要手动对角测量.
主要方法:
- 应用了包括决策树,自适应增强,极端梯度增强和随机森林在内的回归技术.
- 模型被训练在54张D2钢的图像在各种条件下 (商业,火,炼,TiNbN涂层).
- 维克尔硬度直接从图像数据中预测,绕过几何测量.
主要成果:
- 随机森林模型实现了最佳性能.
- 关键性能指标包括0.95的根平均平方误差 (RMSE),0.12的平均绝对误差 (MAE) 和约1.2的确定系数 (R2).
- 随机森林模型显著优于其他评估的回归方法.
结论:
- 机器学习算法可以从缩影图像准确地预测维克斯硬度.
- 这种基于图像的ML方法为传统测量方法提供了快速而精确的替代方案.
- 这些发现表明,在工业材料评估中提高质量控制的潜力.
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