期刊文献+

高质量过程控制技术的比较研究与分析 被引量:1

Comparison and Analysis for High Quality Processes Monitoring Techniques
下载PDF
导出
摘要 随着质量改进活动的不断开展,现代制造过程中的不合格项在逐渐降低。在这种情况下,常规的休哈特型计数控制图往往是失效的。为了监控高质量的过程运行,一种方法是采用累计合格品计数(CCC)图;另一种方法是采用几何Q图,本文首先分析了这两种控制图的基本原理;进而以平均链长(ARL)和探测过程发生漂移的概率为准则,系统分析和比较了这两种控制图的性能,仿真结果表明,在大多数情况下,这两种控制图具有相似的性能;最后,通过实例说明了这两种控制图的应用,并给出了若干建议。 With the development of quality improvement programs, many production processes today .are producing a very low level of nonconforming items. In this case, conventional Shewhart type attribute control charts are not applied effectively in practice. In order to monitor high quality processes, two alternative techniques are proposed: one is a cumulative count of conformance (CCC)chart, the other is geometric Q chart. In this paper, we first analyze basic principal of both CCC chart and geometric Q chart, which is based on the geometric distribution. Then we compare the performance by using average run length (ARL) and the probability of detecting a shift in the observations after a shift occurs. Simulation results show that in most cases CCC chart and geometric Q chart are of the similar performance. After that an example from practice is presented. And finally, some suggestions are given in conclusion.
作者 马义中 刘阳
出处 《数理统计与管理》 CSSCI 北大核心 2008年第3期473-479,共7页 Journal of Applied Statistics and Management
基金 国家自然科学基金(70672088)
关键词 高质量过程控制 CCC图 几何Q图 控制性能 high quality process control, CCC chart, geometric Q chart, control performance
  • 相关文献

参考文献8

  • 1Goh, T.N., Xie, M. Statistical Control of a Six Sigma Process [J]. Quality Engineering, 2003, 15 (4): 587-592.
  • 2Calvin, T.W. Quality Control Techniques for 'Zero Defects' [J]. IEEE Transactions on Components, Hybrids and Manufacturing Technology, 1983, CHMT-6: 323-328.
  • 3Goh. T.N. A Control Chart for Very High Yield Process [J]. Quality Assurance, 1987, 13: 18-22.
  • 4Tang, L.C., Cheong, W.T. CCC Chart with Sequentially Estimated Parameter [J]. IIE Transactions, 2004, 36: 841-853.
  • 5Tang, L.C., Cheong, W.T. A Control Scheme for High-Yield Correlated Production Under Group Inspection [J]. Journal of Quality Technology, 2006, 38 (1): 45-54.
  • 6Quesenberry, C.P. Geometric Q Charts for High Quality Processes [J]. Journal of Quality Technology, 1995, 27 (4): 304-314.
  • 7Yang, Z., Xie, M., Kuralmani, V., Tsui, K. L. On the Performance of Geometric Charts with Estimated Control Limits [J]. Journal of Quality Technology, 2002, 34 (4): 448-458.
  • 8Xie, M., Goh, T. N., Tang, X.Y. Data Transformation for Geometrically Distributed Quality Characteristics [J]. Quality and Reliability Engineering International, 2000, 16: 9-15.

同被引文献8

  • 1王海宇,徐济超,贺金凤,杨剑锋.基于多点报警的多元统计过程控制[J].数理统计与管理,2006,25(6):709-715. 被引量:3
  • 2GOH T N, XIE M. Statistical control of a six sigma process[J]. Quality Engineering, 2003, 15(4):587-592.
  • 3CHAN L Y, XIE M, GOH T N. Cumulative quantity control charts for monitoring production processes[J]. International Journal of Production Research, 2000, 38(2):397-408.
  • 4GOH T N. A control chart for very high yield processes[J]. Quality Assurance, 1987,13(1):18-22.
  • 5LAI C D, XIE M, GOVINDARAJU K. Study of a markov model of a highquality dependent process[J]. Journal of Applied Statistics, 2000, 27(4):461-473.
  • 6TANG L C, CHEONG W T. CCC chart with sequentially estimated parameter[J]. IIE Transactions, 2004, 36(9):841-853.
  • 7TANG L C, CHEONG W T. A control scheme for high-yield correlated production under group inspection[J]. Journal of Quality Technology, 2006, 38(1):45-54.
  • 8宋宗伟,宋向东.高产过程的CCC-r图控制限优化[J].辽宁工程技术大学学报(自然科学版),2014,33(4):535-538. 被引量:2

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部