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相关概念视频

Temperature Dependent Deformation01:12

Temperature Dependent Deformation

147
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
147
Steel Manufacturing01:26

Steel Manufacturing

469
Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
During this smelting process, limestone plays a crucial role by forming slag. Slag captures impurities within the molten iron, such...
469
Mechanical Characteristics of Steel01:18

Mechanical Characteristics of Steel

574
The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used...
574
Thermal Strain01:19

Thermal Strain

1.0K
Thermal strain is a concept that arises when we consider how temperature changes affect structures. Unlike the conventional assumption that structures remain constant under load, real-world scenarios often involve temperature fluctuations that can significantly impact these structures. Consider a homogeneous rod with a uniform cross-section resting freely on a flat horizontal surface. If the rod's temperature increases, the rod elongates. This elongation is proportional to the temperature...
1.0K
Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.2K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.2K

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相关实验视频

Updated: Jul 1, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

842

通过机器学习预测钢的相变温度.

Yupeng Zhang1, Lin Cheng1, Aonan Pan1

  • 1The State Key Laboratory of Refractories and Metallurgy, Hubei Province Key Laboratory of Systems Science on Metallurgical Processing, International Research Institute for Steel Technology, Collaborative Center on Advanced Steels, Wuhan University of Science and Technology, Wuhan 430081, China.

Materials (Basel, Switzerland)
|March 13, 2024
PubMed
概括

这项研究使用了改进的LightGBM模型来预测钢相转换温度 (Ac1,Ac3,马氏体启动 (MS),贝尼特启动 (BS)). 原子参数显著提高了预测的准确性,揭示了钢铁加工的关键影响因素.

关键词:
原子参数是一个原子参数.机器学习是机器学习.阶段转换温度转换温度的阶段转换温度.钢铁公司 钢铁公司

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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

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相关实验视频

Last Updated: Jul 1, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

842
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学

背景情况:

  • 阶段转换温度对于钢的设计,生产和热处理至关重要.
  • 了解合金元素对这些温度的影响对于材料性质控制至关重要.

研究的目的:

  • 为了研究和预测四个关键的钢阶转化温度:Ac1,Ac3,马氏体转化开始 (MS) 和贝尼特转化开始 (BS).
  • 识别和分析合金元素和原子参数对这些转变温度的影响.

主要方法:

  • 利用一个改进的梯度增强算法,LightGBM,用于预测建模.
  • 包含十八个原子参数,包括化温度,热膨胀系数,瓦伯-克罗默伪电位半径和价值电子数.
  • 采用部分依赖图 (PDP) 和沙普利增量解释 (SHAP) 来分析特征的重要性和关系.

主要成果:

  • 通过包含原子参数,LightGBM模型实现了显著提高的训练精度.
  • 化温度,线性热膨胀系数,原子伪电位半径和价值电子数被确定为四大影响力原子特征.
  • 详细的分析显示,合金元素在不同相变换温度下有不同的影响机制.

结论:

  • 原子参数对于准确预测钢阶转换温度至关重要.
  • 开发的模型和分析为通过组合和热处理控制钢材特性提供了宝贵的见解.
  • 这种方法为了解钢的复杂材料行为提供了一个强大的方法.