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相关实验视频

Updated: Jun 7, 2025

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

基于图像处理的模型用于对基合金的表面粗度评估,使用双树复杂波纹转换和辐射基函数神经网络.

J S Vishwanatha¹, P Srinivasa Pai¹, Grynal D'Mello¹

¹Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India.

Scientific reports

|November 16, 2024

概括

此摘要是机器生成的。

这项研究开发了一种计算机视觉系统,用于评估Ti 6Al 4V.中的表面粗度. 最好的模型使用双树复杂波形变换图像融合和粒子群集优化功能实现了99.13%的准确性.

关键词:

计算机视觉计算机视觉计算机视觉在DTCWTT上在GLCM中,GLCM是指GLCM.在PCA中,PCA是PCA.公共服务人员 (PSO)在RBFNNNNNNNNNNNN

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Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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Published on: December 13, 2016

相关实验视频

Last Updated: Jun 7, 2025

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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Published on: February 27, 2013

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

科学领域:

材料科学与工程材料科学与工程
计算机视觉和图像处理
制造业制造技术制造技术

背景情况:

表面粗度是评估加工元件性能和质量的关键参数.
传统的表面粗度评估方法可能耗时且主观.
开发自动化,准确和高效的评估方法对于先进制造至关重要.

研究的目的:

调查计算机视觉系统在转向的Ti 6Al 4V表面上评估表面粗性的有效性.
为了比较不同的特征提取和选择方法进行表面粗度分析.
使用先进的信号处理和机器学习技术,开发出高度准确的表面粗度预测模型.

主要方法:

利用双树复杂波纹变换 (DTCWT) 进行图像分解和特征提取.
对比了三个特征生成方法:GLCM,基于DTCWT的统计特征,以及DTCWT图像与PSO优化的GLCM特征的融合.
采用主要组件分析 (PCA) 进行特征选择和辐射基函数神经网络 (RBFNN) 进行模型开发.

主要成果:

根据特征生成和选择方法评估了六种不同的RBFNN模型.
该模型结合了DTCWT图像融合,PSO优化的GLCM功能和PCA选择,在训练数据上实现了100%的准确性.
这种优化的模型在测试数据上显示出高预测准确率99.13%.

结论:

拟议的计算机视觉系统,通过DTCWT图像融合和PSO优化的功能来增强,提供了一种非常准确的方法来评估Ti 6Al 4V的表面粗度.
使用PCA进行特征选择可以显著提高模型效率和预测性能.
这种方法为精密制造中的质量控制提供了强大而自动化的解决方案.