工业工程

工业工程 ›› 2021, Vol. 24 ›› Issue (5): 101-107.doi: 10.3969/j.issn.1007-7375.2021.05.013

• 专题论述 • 上一篇    下一篇

基于密度的轮廓控制参数识别方法研究

张阳1,2, 秦幸幸1   

  1. 1. 天津商业大学 管理学院;
    2. 管理创新与评价研究中心，天津 300134
  • 收稿日期:2020-03-27 发布日期:2021-11-02
  • 作者简介:张阳(1986—)，男，河北省人，副教授，博士，主要研究方向为质量工程与质量管理、统计过程控制
  • 基金资助:
    国家自然科学基金资助项目(71401123)

A Density-based Profile Monitoring Parameter Identification Method

ZHANG Yang1,2, QIN Xingxing1   

  1. 1. School of Management;
    2. Management Innovation and Evaluation Research Center, Tianjin University of Commerce, Tianjin 300134, China
  • Received:2020-03-27 Published:2021-11-02

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摘要： 正确识别受控轮廓集并确定轮廓控制参数是轮廓控制的基础。当轮廓内部存在相关关系时，异常轮廓对目前轮廓控制参数识别方法的干扰较大。因此，为降低异常轮廓对轮廓控制参数识别方法的影响，提出一种基于密度的受控轮廓集识别方法。该方法包括基于线性混合模型的轮廓建模、基于密度的初始受控轮廓集确定、基于逐次迭代方法的受控轮廓集识别和轮廓控制参数确定等。基于蒙特卡洛模拟分析所提方法中初始受控轮廓数目和密度参数对识别性能的影响。此外，比较分析所提方法与已有方法的识别性能。模拟仿真显示，基于密度的轮廓控制参数识别方法的识别性能要优于其他方法。

关键词: 轮廓控制, 相关性, 数据密度, 统计过程控制

Abstract: Correctly identifying the in-control profile set and determining the profile control parameters are the basis of profile monitoring. When the within-profile data is correlated, the existing profile parameters identification method is greatly affected by abnormal profiles. Then, a density-based identification method is developed, including profile modeling based on the linear mixed model, determining the initial in-control profile set based on the data density, identifying the in-control profile set based on successive iteration method, and calculating the in-control parameters. Then, based on the Monte Carlo simulation, the influence of density parameter and the number of profiles in initial in-control profile set is analyzed. Finally, the identification performance of the proposed method is compared with the existing methods. Simulation results show that the identification performance of the proposed density-based method is better than the existing methods.

Key words: profile monitoring, correlation, data density, statistical process control

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