数字建模用于预测化汽车钢件的微观结构和机械性能
Carlos Coroas1, Iván Viéitez1, Elena Martín2
1Departamento de Matemática Aplicada II, Escuela de Ingeniería de Telecomunicación, Campus Marcosende, Universidade de Vigo, 36310 Vigo, Spain.
Materials (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种高效的数值工具,用于预测汽车钢轴在火后的性能. 该模型准确地预测了微结构和机械性能,有助于过程优化和数字双胞胎集成.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机建模 计算建模
背景情况:
- 汽车钢经过火,以获得所需的机械性能.
- 在火过程中预测微观结构和特性是复杂的.
- 工业火过程需要优化效率和质量.
研究的目的:
- 开发一种高效的数值工具,用于预测汽车钢的最终微观结构,机械性能和扭曲.
- 为了验证数值工具与工业火过程中的实验数据的验证.
- 为重新设计和优化火过程提供计算方法.
主要方法:
- 使用有限元素方法实现双向合的热金模型和单向合的机械模型.
- 开发一种新的一般化固体到液体传热模型,考虑零件尺寸,流体特性和工艺参数.
- 实验验证使用经过批量和直接火过程的汽车螺丝.
主要成果:
- 数字工具准确预测温度演变和最终微观结构的偏差,分别低于7.5%和12%.
- 该模型有效地以降低计算成本捕获热传递机制.
- 实验验证证了该工具对于工业火场景的准确性.
结论:
- 开发的数值工具是有效的预测汽车钢轴火的结果.
- 该模型为优化工业火过程和支持数字双胞胎计划提供了宝贵的资产.
- 这种高效的计算方法有助于实现所需的材料特性,并最大限度地减少扭曲.
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