北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (6): 98-104.doi: 10.13190/j.jbupt.2019-172

• 论文 • 上一篇    下一篇

一种多变量制造过程中的关键变量检测算法

余凯祥1, 陈振豪2, 张四海1   

  1. 1. 中国科学技术大学 信息学院, 合肥 230022;
    2. 长鑫存储技术有限公司, 合肥 230000
  • 收稿日期:2019-08-21 出版日期:2019-12-28 发布日期:2019-11-15
  • 作者简介:余凯祥(1995-),男,硕士生,E-mail:ykx@mail.ustc.edu.cn;张四海(1974-),男,副教授,硕士生导师.
  • 基金资助:
    国家自然科学基金重点项目(61631018);长鑫存储技术专项研究项目(CT201809030002)

A Key Variable Detection Algorithm in Multivariate Manufacturing Process

YU Kai-xiang1, CHEN Zhen-hao2, ZHANG Si-hai1   

  1. 1. School of Information, University of Science and Technology of China, Hefei 230022, China;
    2. Chang Xin Memory Technologies, Inc., Hefei 230000, China
  • Received:2019-08-21 Online:2019-12-28 Published:2019-11-15

摘要: 提出了一种基于机器学习的多变量制造过程中的关键变量检测算法.该算法利用机器学习分类器对多变量制造过程进行数学建模,以随机打乱过程变量后分类器的性能变化作为评价指标,检测导致产品质量相对异常的关键变量.设计并生成了多变量制造过程的仿真数据集,在仿真数据集和基于中国某工厂的2个实际生产案例数据集上对算法的检测性能进行了性能验证,2次验证结果均表明算法检测性能良好.

关键词: 多变量制造过程, 机器学习, 关键变量检测

Abstract: A key variable detection algorithm based on machine learning in multivariate manufacturing process was proposed. It uses the machine learning classifier to mathematically model the multivariate manufacturing process. And the performance change of the classifier after the process variable shuffled randomly is used as an evaluation index to detect the key variables that lead to relatively abnormal product quality. Simulation data of the multivariate manufacturing process is designed and generated. The performance of the algorithm was verified by simulation data set and two actual production case data sets based on a factory in China. Both verifications show that the algorithm has good detection performance.

Key words: multivariate manufacturing process, machine learning, key variable detection

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