使用随机森林机器学习算法和多层感知器分析AW-5251合金板的摩擦性能
Tomasz Trzepieciński1, Sherwan Mohammed Najm2,3, Omar Maghawry Ibrahim4
1Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland.
Materials (Basel, Switzerland)
|August 12, 2023
概括
这项研究确定了使用AW-5251合金板的金属成型拉杆的摩擦系数 (COF). 油的粘度,表面粗度和材料特性显著影响COF,为减少摩擦确定了最佳范围.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 部落学 (tribology) 是一个学科.
背景情况:
- 摩擦系数 (COF) 在金属成型过程中至关重要,影响工具磨损和产品质量.
- 了解拉珠区域的摩擦对于优化板材成型操作至关重要.
研究的目的:
- 为了在各种条件下确定AW-5251合金板在拉珠区域的COF.
- 通过实验数据和机器学习模型,确定影响COF的关键参数.
主要方法:
- 使用拉珠模拟器进行实验测试,使用不同的合金硬化条件,反样本粗度和滑剂类型 (机油,液压油,发动机油).
- 应用随机森林 (RF) 和人工神经网络 (ANN) 来分析实验结果并确定参数的重要性.
- 对ANN中的12个激活函数和RF中的9个损失函数进行分析,以评估模型性能.
主要成果:
- 与横向方向相比,沿滚动方向切割的样品的COF更高.
- 石油粘度是最重要的因素 (0.59),其次是粗度 (Ra,0.30),产应力 (Rp0.2,0.05) 和强度系数 (K,0.06).
- 降低COF的最佳条件包括Ra > 1.1,Rp0.2在105-190MPa之间,油粘度在9-35cP之间.
结论:
- 该研究成功地确定了拉珠区域的COF,并确定了关键影响参数.
- 机器学习模型,特别是射频,提供了COF的准确预测 (R2 = 0.90 ± 0.028).
- 确定了油粘度,表面粗度和产量压力的特定范围,以最大限度地减少板材成型中的COF.
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