The Uncertainty Valuation and Reduction in Part Curve Fitting

WEI Jinwen

doi:10.7652/xjtuxb202110008

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The Uncertainty Valuation and Reduction in Part Curve Fitting

更新时间：2025-07-09

- The Uncertainty Valuation and Reduction in Part Curve Fitting
- Vol. 55, Issue 10, Pages: 68-77(2021)
- 作者机构：
  
  广西大学机械工程学院,南宁,530004
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202110008
  CLC： TH39
- Online First：10 October 2021，
  
  Published：2021
- 稿件说明：
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WEI Jinwen. The Uncertainty Valuation and Reduction in Part Curve Fitting[J]. 2021, 55(10): 68-77.
DOI：

WEI Jinwen. The Uncertainty Valuation and Reduction in Part Curve Fitting[J]. 2021, 55(10): 68-77. DOI： 10.7652/xjtuxb202110008.

摘要

针对零件曲线拟合中的数据噪声与误差、数据分割误差以及用错几何元素等引起的不确定度进行评估并提出其降低方法。首先

基于蒙特卡罗法

在拟合数据中叠加噪声、用零均值高斯分布作为分割误差模型、用圆弧拟合Bezier曲线

以数字仿真研究拟合参数的不确定度。结果表明

对不确定度影响最大的是分割误差与用错几何元素

远大于数据噪声的影响; 其次

为了降低拟合参数的不确定度

采用高维向量映射测试元素特征在局部数据中的恒定性

找出其所在的数据段范围

从而准确辨识出各数据段的几何元素。仿真与实物实验均表明

提出的方法能够正确识别数据序列的几何元素、准确分割数据

从而降低零件曲线拟合不确定度。

Abstract

The uncertainties from data noise and error

data segmentation error and wrong fitting geometric elements

are evaluated respectively

and then a method to decrease the uncertainties is proposed for the curve fitting of parts. Firstly

based on Monte Carlo method

the uncertainty of fitting parameters is studied by adding noise to fitting data

using 0-means Gaussian distribution as segmentation error model

and using circular arc to fit Bezier curve. The results show that segmentation error and misuse of geometric elements have a great influence on the uncertainty

which is much greater than the influence of data noise. Secondly

in order to segment the data sequence accurately

high-dimensional vector mapping is proposed to test the constancy of local features of elements

identify the geometric elements in the data sequence

and find out the range of the data segment. Simulation and experiment results show that the proposed method can obtain more accurate data segmentation and fitting than traditional methods.

关键词

Keywords

references

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