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首页> 外文期刊>International Journal of Production Research >Designing A Multivariate-multistage Quality Control System Using Artificial Neural Networks
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Designing A Multivariate-multistage Quality Control System Using Artificial Neural Networks

机译：利用人工神经网络设计多元多阶段质量控制系统

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摘要

In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate-multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate-multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average run lengths and correct classification ratio indices have been chosen to investigate the performance of the designed network. The results of a simulation study show that the network is capable of detecting both in-control and out-of-control signals appropriately.
机译：在大多数现实世界的制造系统中，货物的生产包括几个自相关阶段，并且每个阶段的货物质量特征都是相关的随机变量。本文解决了监视多变量多阶段制造过程的问题，并诊断了信号失控的可能原因。为了使用多元时间序列模型实现此目的，首先开发了一种用于多元多阶段过程的自相关数据的模型。然后，设计，训练和采用单个神经网络来控制和分类所有阶段质量特征的均值漂移。选择了控制内和控制外平均游程长度以及正确的分类比率指数来调查设计网络的性能。仿真研究的结果表明，该网络能够适当地检测出控制中和失控信号。

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