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相关概念视频

Residual Stresses01:26

Residual Stresses

186
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...
186

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相关实验视频

Updated: May 10, 2025

Surrogate Model Development for Digital Experiments in Welding
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通过人工智能技术实现电阻接质量

Luis Alonso Domínguez-Molina1, Edgar Rivas-Araiza1, Juan Carlos Jauregui-Correa1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, México.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种非破坏性计算机视觉方法,用于评估电阻点 (RSW) 质量. 可见图像在分类接质量和预测机械强度方面比热图像更有效.

关键词:
卷积神经网络是一种卷积神经网络.电极强力是指电极的强力.电阻点接 电阻点接 电阻点接 电阻点接热图像是一种热图像.可见的图像可见的图像可见的图像接电流的接电流是什么接时间 接时间

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科学领域:

  • 材料科学 材料科学 材料科学
  • 制造业 工程 制造工程
  • 计算机视觉 计算机视觉

背景情况:

  • 阻力点 (RSW) 质量评估在制造业中至关重要.
  • 非破坏性评估 (NDE) 方法对于保持材料完整性越来越重要.
  • 现有的方法可能会改变关节的特性,需要采用其他方法.

研究的目的:

  • 利用计算机视觉开发一个具有成本效益的,非破坏性的RSW质量评估方法.
  • 为了将接过程参数与视觉和机械质量结果相关联.
  • 评估在视觉和热接数据上训练的机器学习模型的性能.

主要方法:

  • 使用手动RSW机器收集过程参数 (电流,时间,压力) 和视觉/热图像.
  • 在图像数据上训练了六个机器学习模型,以对接质量进行分类.
  • 用交叉验证评估模型性能,将图像数据与机械性能 (拉力,接直径) 相关联.

主要成果:

  • 接时间和电极角度显著影响关节的机械强度.
  • 使用可见图像的计算机视觉模型优于使用热图像的模型.
  • 拟议的方法有效地通过视觉数据将工艺参数与接质量联系起来.

结论:

  • 基于计算机视觉的非破坏性方法为RSW质量评估提供了可行的解决方案.
  • 可见图像分析是预测接质量和机械性能的一种有前途的技术.
  • 这种方法可以提高RSW应用中的制造质量控制.