The Application of Monte Carlo Simulation for Statistical Process Control

被引：0
|
作者：
Zhang, Lin [1 ]
机构：
[1] Zhengzhou Inst Aeronaut Ind Management, Zhengzhou 450015, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE OF MANAGEMENT ENGINEERING AND INFORMATION TECHNOLOGY, VOLS 1 AND 2 | 2009年
关键词：
Monte Carlo simulation; SPC (Statistical Process Control); random number;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Statistical process control (SPC) is a powerful tool for process quality improvement. The data collection and analysis is important during the research of SPC. Analytic method is widely used to find the mass data of experiment, which is sometimes hardly to find the result. In this paper, Monte Carlo simulation is introduced as a effective method of analysis and solution for the statistical process control. The estimate of probability for criterions is an examples to explain how to use Monte Carlo simulation in the research of process quality improvement.
引用
收藏
页码：853 / 856
页数：4
相关论文
共 50 条
  • [1] Application Research on Monte Carlo Simulation in Machining Process at Quality Control
    Li Xiaolei
    MANAGEMENT ENGINEERING AND APPLICATIONS, 2010, : 241 - 245
  • [2] Statistical ensembles for Monte Carlo simulation
    Wilding, NB
    COMPUTER SIMULATIONS OF SURFACES AND INTERFACES, 2003, 114 : 95 - 108
  • [3] MONTE-CARLO SIMULATION OF STATISTICAL POWER
    BORENSTEIN, M
    KANE, J
    BUCHBINDER, J
    PSYCHOPHARMACOLOGY BULLETIN, 1987, 23 (02) : 300 - 302
  • [4] Monte Carlo simulation of the enantioseparation process
    Bustos, V. A.
    Acosta, G.
    Gomez, M. R.
    Pereyra, V. D.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2012, 391 (18) : 4389 - 4396
  • [5] NONLINEAR MONTE CARLO MISSION SIMULATION AND STATISTICAL ANALYSIS
    Potts, Christopher L.
    Kelly, Richard M.
    Goodson, Troy D.
    ASTRODYNAMICS 2009, VOL 135, PTS 1-3, 2010, 135 : 1725 - 1740
  • [6] Backward Simulation Methods for Monte Carlo Statistical Inference
    Lindsten, Fredrik
    Schon, Thomas B.
    FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2013, 6 (01): : 1 - 143
  • [7] Efficient Monte Carlo simulation methods in statistical physics
    Wang, JS
    MONTE CARLO AND QUASI-MONTE CARLO METHODS 2000, 2002, : 141 - 157
  • [8] Monte Carlo Simulation for Statistical Decay of Compound Nucleus
    Kawano, T.
    Talou, P.
    Chadwick, M. B.
    CNR*11 - THIRD INTERNATIONAL WORKSHOP ON COMPOUND NUCLEAR REACTIONS AND RELATED TOPICS, 2012, 21
  • [9] Exponent Monte Carlo for Quick Statistical Circuit Simulation
    Zuber, Paul
    Matvejev, Vladimir
    Roussel, Philippe
    Dobrovolny, Petr
    Miranda, Miguel
    INTEGRATED CIRCUIT AND SYSTEM DESIGN: POWER AND TIMING MODELING, OPTIMIZATION AND SIMULATION, 2010, 5953 : 36 - +
  • [10] The Using of Monte Carlo Simulation in the Teaching Process
    Sediva, Blanka
    INTERNATIONAL JOURNAL FOR TECHNOLOGY IN MATHEMATICS EDUCATION, 2019, 26 (03): : 145 - 150
12345