基于边界可行方案的控制过程连续采样方案系统选择新方法

Li Chun-zhi, Tong Shu-rong, Wang Ke-qin
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引用次数: 0

摘要

连续抽样计划(csp)在制造系统中广泛采用,以提高出厂质量并降低检验成本。现有的csp在制定以平均出厂质量限值(AOQL)轮廓值为最大值的检验方案时,忽略了一些可行的控制过程抽样方案。提出了一种新的系统方法——动态连续抽样计划(DCSP),用于建立边界可行的检验方案,并为从业人员提供所有可行的检验方案。在csp中,根据产品批量大小和AOQL选择检验方案给从业者带来了复杂性和困惑。DCSP通过证明检验方案选择的抽样频率越大越好来解决这一问题。在所有的csp中,哪个间隔的csp可以有效地工作以及何时应该停止csp是两个悬而未决的问题。DCSP通过建立有效的工作间隔和停车规则,成功地解决了这两个问题。与文献中的部分优化不同,DCSP可以利用有效工作间隔、停止规则等特征,将所有过程控制工具整合为一个整体,实现过程的定量和定性控制。同时利用不合格概率的自然估计量实现闭环控制和过程控制。由于具有相同输出质量的过程的接受概率值不同,csp中原有的接受概率定义不适用于DCSP。在DCSP中,接受的概率被重新定义为根据平均输出质量(AOQ)接受或拒绝输出产品流。讨论了各参数对DCSP的影响。结果表明,DCSP与CSP-1的输出质量具有稳定性和可控性。最后给出了一个数值算例,验证了所提方法的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Systematic Method for Selecting Continuous Sampling Plan Based on the Boundary Feasible Plan for In-Control Process
Continuous sampling plans (CSPs) are extensively adopted in manufacturing systems to improve outgoing quality as well as reduce inspection costs. Existing CSPs formulating inspection scheme with the maximum value of average outgoing quality limit (AOQL) contour neglect some feasible sampling plans for in-control process. A new systematic method, designated as dynamic continuous sampling plan (DCSP), is proposed for establishing boundary feasible inspection schemes and supplying all feasible inspection schemes for the practitioners. Selecting inspection scheme according to produce lot size and AOQL in CSPs leads to the complexity and perplexity for the practitioners. DCSP solves the problem by demonstrating the-bigger-the-better rule on sampling frequency for inspection scheme selection. In all CSPs, which interval CSPs can work effectively and when CSPs should be stopped are two pendent issues. DCSP successfully solves the two problems by the foundation of effective working interval and stopping rule. Unlike partial optimization in the literature, DCSP can incorporate all process control tools into a whole integer by these characteristics, such as effective working interval, stopping rule, et al to realize process quantitative and qualitative control. Simultaneously DCSP realizes closed-loop and in-process control by the natural estimator of the probability of non-conformance. The original definition of the probability of acceptance in CSPs is unsuitable for DCSP due to the different value of the probability of acceptance for the processes with same outgoing quality. The probability of acceptance in DCSP is redefined as accepting or rejecting the outgoing product flow according to average outgoing quality (AOQ). The effects of the parameters in DCSP are discussed. The results comparing DCSP with CSP-1 show the stability and controllability in outgoing quality for DCSP. A numerical example is given at last to verify the proposed method.
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