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相关概念视频

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Metallic Solids02:37

Metallic Solids

Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability. Many...
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

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...
Valence Bond Theory02:42

Valence Bond Theory

Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The semiconductor's...

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使用深度神经网络预测金属添加制造的珠子几何.

Min Seop So1, Mohammad Mahruf Mahdi2, Duck Bong Kim3

  • 1Department of Industrial Engineering, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
概括

深度神经网络 (DNN) 准确地预测了线弧增材制造 (WAAM) 中的珠子几何形状,提高了大型金属部件的结构完整性. 这种先进的机器学习方法提高了航空航天领域的精度.

关键词:
珠子的几何结构深度神经网络 (DNN) 是一个深度神经网络.气体金属弧接 (GMAW) 是一种电缆弧增材制造 (WAAM) 是一种

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科学领域:

  • 制造业 工程 制造工程
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 增材制造 (AM),特别是电弧增材制造 (WAAM),使大型金属组件的生产成为可能,这对航空航天至关重要.
  • 对珠子几何形状 (宽度和高度) 的精确控制对于WAAM部分完整性至关重要.
  • 现有的方法与管理WAAM珠子形成的复杂,非线性关系作斗争.

研究的目的:

  • 开发和验证深度神经网络 (DNN) 模型,以准确预测气体金属弧接-冷金属转移 (GMAW-CMT) WAAM中的珠子几何形状.
  • 将DNN的预测性能与传统的回归和机器学习模型进行比较.
  • 展示深度学习的潜力,以提高WAAM过程控制和效率.

主要方法:

  • 使用坐标测量机 (CMM) 收集精确的工艺参数数据 (电线速度,料速度) 和珠子尺寸.
  • 训练并验证了多个回归模型,包括线性回归,,多项式,随机森林,以及自定义的DNN.
  • 设计了一个具有多个隐藏层的DNN,通过反向传播进行训练,并使用Adam优化器进行优化.

主要成果:

  • 与所有其他测试模型相比,DNN模型在预测珠子的宽度和高度方面表现出卓越的准确性.
  • 实现了非常低的误差指标:宽度为0.014%的MAPE,高度为0.012%的MAPE,宽度为0.122 RMSE,高度为0.153 RMSE.
  • 随机森林也显示出有效性,但DNN因其捕捉复杂非线性的能力而脱而出.

结论:

  • 深度神经网络为预测WAAM过程中的珠子几何学提供了一个高度准确和强大的方法.
  • 开发的DNN模型可以显著提高WAAM制造组件的精度和可靠性.
  • 这项研究强调了人工智能和深度学习在推进增材制造技术方面的变革潜力.