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相关概念视频

Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

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In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
The Maximum Shearing Stress Criterion, also known as...
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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...
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Stress-Strain Diagram - Ductile Materials01:24

Stress-Strain Diagram - Ductile Materials

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The stress-strain relationship in ductile materials such as structural steel or aluminium is intricate and progresses through several stages. When a specimen is loaded, it initially exhibits a linear length increase, depicted by a steep straight line on the stress-strain diagram. It indicates the material is elastically deforming and will return to its original shape once unloaded. However, when a critical stress value is reached, plastic deformation begins. This stage sees substantial...
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Hooke's Law01:26

Hooke's Law

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Hooke's law, a pivotal principle in material science, establishes that the strain a material undergoes is directly proportional to the applied stress, defined by a factor called the modulus of elasticity or Young's modulus.
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Residual Stresses01:26

Residual Stresses

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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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Temperature Dependent Deformation01:12

Temperature Dependent Deformation

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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...
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相关实验视频

Updated: Feb 28, 2026

Micromechanical Tension Testing of Additively Manufactured 17-4 PH Stainless Steel Specimens
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通过增材制造工艺,通过机器学习来预测316L不钢的拉力强度.

Qing Gao1,2,3, Congyu Wang2,3,4, Jiayan Hu1

  • 1State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310030, China.

Micromachines
|February 27, 2026
PubMed
概括

这项研究提出了一个新的模型,将CNN和RF集成在一起,以预测SLM制造的316L不钢部件的抗拉强度. 与单独使用CNN相比,综合模型显著提高了预测准确性.

关键词:
添加剂制造 添加剂制造 添加剂制造卷积神经网络是一种卷积神经网络.机器学习是机器学习.随机的森林随机的森林拉力强度 拉力强度 拉力强度

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科学领域:

  • 材料科学 材料科学 材料科学
  • 机械工程 机械工程
  • 添加剂制造 添加剂制造 添加剂制造

背景情况:

  • 预测增材制造组件的抗拉强度对于工程应用至关重要.
  • 增材制造 (AM) 中的工艺参数使得拉伸强度预测具有挑战性.

研究的目的:

  • 开发一个集结卷积神经网络 (CNN) 和随机森林 (RF) 的协同模型,用于预测通过选择性激光化 (SLM) 制造的316L不钢部件的抗拉强度.
  • 对实验数据评估模型的性能,并与现有方法进行比较.

主要方法:

  • 使用CNN和RF算法的组合开发了一个预测模型.
  • 在42个数据集上训练模型,并使用12个实验数据集进行验证.
  • 使用平均平方误差 (MSE) 和平均绝对误差 (MAE) 量化预测性能.

主要成果:

  • 集成的CNN-RF模型实现了0.00295的MSE和0.0344.0的MAE.
  • 这比单独的CNN显著改善,MSE (3.28%) 和MAE (31.88%) 的减少.
  • 达到0.9576的高相关系数,表明准确的预测,即使是小数据集.

结论:

  • 协同效应的CNN-RF模型为SLM制造的316L不钢提供了高精度的抗拉强度预测,单独优于CNN.
  • 该模型即使在有限的数据中也证明了有效性,为预测各种材料的机械性质提供了一个框架.
  • 添加相对密度和维克尔硬度并没有提高相同样本大小的预测准确性.