基于基因算法和304不钢模拟分析的约翰逊-库克构成模型的参数识别
Xinyang Jiang1, Jinfu Ding1, Chengwu Wang2,3
1College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
Scientific reports
|September 11, 2024
概括
这项研究使用遗传算法改进了约翰逊-库克 (J-C) 对304不钢的构成模型. 与传统方法相比,增强的JCM模型在预测切削力方面显著减少了错误.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算力学 计算力学 计算力学
背景情况:
- 传统的构成模型在预测切削力时经常显示差异.
- 在加工条件下对材料行为的准确建模对于过程优化至关重要.
研究的目的:
- 为304不钢开发一个更可靠的约翰逊-库克 (J-C) 构成模型.
- 使用基于遗传算法的方法,提高预测切削力的准确性.
主要方法:
- 开发了一个不平等的分割剪切区域模型来计算剪切区域应力.
- 研究了J-C参数C对切割形态学的影响.
- 使用遗传算法精确识别J-C的组成参数.
- 使用3D有限元素方法 (FEM) 模拟用于模型验证.
主要成果:
- 改进的JCM模型实现了最大切削力预测误差14.8%,平均误差为6.38%.
- 与JC1 (Split Hopkinson Pressure Bar) 和经验 (EXP) 模型相比,JCM模型显示出更高的准确性.
- 确定了J-C参数C的最佳值,以及其他参数的精确值.
结论:
- 通过遗传算法识别的JCM模型提供了一个更可靠的方法来预测304不钢的切削力.
- 该研究强调了遗传算法在优化机械加工应用的构成模型参数方面的有效性.
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