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相关概念视频

Bonding in Metals02:32

Bonding in Metals

47.4K
Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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Metal-Ligand Bonds02:51

Metal-Ligand Bonds

20.8K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
20.8K
Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

200
The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
200
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

463
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
463
Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

1.0K
Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.
1.0K
Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

261
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
261

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相关实验视频

Updated: Jul 12, 2025

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
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基于Xgboost机器学习算法进行的金属-金属结合过程研究.

Jingpeng Feng1,2, Lihua Zhan1,2, Bolin Ma1,2

  • 1State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

Polymers
|October 28, 2023
PubMed
概括

优化金属-金属结合使用机器学习和有限元素模型. 这种方法通过优化过程参数,如粗度和圈长度,提高了14%的拉力剪切强度.

关键词:
机器学习算法Xgboost机器学习算法有限元素模型的模型.解释工具包 SHAP 解释工具包过程参数优化过程参数优化单关节 单关节 单关节 单关节

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相关实验视频

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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
  • 计算科学 计算科学

背景情况:

  • 优化粘合过程参数通常涉及广泛的实验和复杂的数据分析.
  • 新技术对于理解过程参数和粘合性能之间的复杂关系至关重要.

研究的目的:

  • 通过先进的计算和机器学习技术,提高金属对金属的粘合性能.
  • 识别和优化影响粘合强度的关键过程参数.

主要方法:

  • 使用单关节 (SLJ) 实验来生成数据.
  • 采用有限元模型 (FEMs) 来模拟和验证结合行为.
  • 应用了Xgboost机器学习 (ML) 算法进行参数优化和分析.
  • 使用SHAP (Shapley添加式解释) 来解释参数的重要性.

主要成果:

  • 在70次运行中训练的Xgboost模型展示了有效的预测能力.
  • 拉力剪切强度 (TSS) 的参数重要性被排列为:粗度,粘合层厚度和膝盖长度.
  • 优化的参数包括粗度 (0.89微米),粘合层厚度 (0.1毫米) 和膝盖长度 (27毫米).
  • 实验验证显示,在优化参数的情况下,TSS增加了14%.

结论:

  • 机器学习方法可以更准确,更直观地理解过程参数对TSS的影响.
  • SLJ实验,FEM和ML的综合方法为优化结合过程提供了有效的途径.