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相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Residual Stresses in Circular Shafts01:10

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In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
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相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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使用基于振动信号的高斯过程回归来预测复杂结构工件的表面粗度.

Jianyong Chen1, Jiayao Lin2, Ming Zhang3

  • 1College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括

这项研究使用高斯过程回归 (GPR) 和振动信号预测复杂工件转中的表面粗度. 开发的模型准确地预测了表面粗度,提高了制造质量和效率.

关键词:
杜比奇斯波段数据包转换变形高斯过程回归法 高斯过程回归法具有复杂结构的工件.表面的粗度 表面的粗度振动信号分析 振动信号分析

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科学领域:

  • 制造业 工程 制造工程
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 表面粗度对于产品质量和制造过程中的流程优化至关重要.
  • 在复杂结构工件制中预测表面粗性仍然是一个挑战.
  • 振动信号为加工过程提供了宝贵的见解.

研究的目的:

  • 开发一个预测模型,用于复杂结构的工件转的表面粗度.
  • 使用高斯过程回归 (GPR) 根据振动信号进行准确的预测.
  • 通过可靠的表面粗度预测,提高制造质量和流程优化.

主要方法:

  • 从时间和频率领域 (平均值,中位数,STD,RMS) 的振动信号中提取特征.
  • 使用Welch的频域分析方法进行信号处理.
  • 时间频域分析采用三级的杜比希波段数据包转换 (WPT).
  • 用高斯过程回归 (GPR) 模型实现表面粗度预测.

主要成果:

  • GPR模型准确地预测了复杂结构工件的表面粗度.
  • 振动信号特征有效地告知预测模型.
  • 时间和频率域分析的整合提高了预测准确度.

结论:

  • 开发的GPR模型为转操作中的表面粗度预测提供了强大的解决方案.
  • 这种预测策略可以显著提高产品质量和简化制造流程.
  • 该方法提供了减少废物和提高工业效率的潜力.