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首页> 外文期刊>International Journal of Production Research >Application of artificial neural network to identify non-random variation patterns on the run chart in automotive assembly process
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Application of artificial neural network to identify non-random variation patterns on the run chart in automotive assembly process

机译：人工神经网络在汽车装配过程运行图识别非随机变化模式中的应用

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摘要

A developed methodology of using an artificial neural network to identify non-random variation patterns to improve dimensional quality in automotive assembly process is presented. The proposed pattern recognition algorithm that integrates with the process knowledge basis is designed not only to detect variation patterns, but also to address the identification of unacceptable variation manifested by non-random patterns on the control chart. Once any non-random patterns occur on the control chart, the root causes of dimensional variations can be located systematically by investigating each possible cause based on the knowledge of the assembly process. This information will help to make process modifications that reduce dimensional variability for automotive body assembly process in real time. Therefore, it can be expected that the control chart with the proposed pattern recognition algorithm will play a more important role as a systematic diagnosis tool rather than only as a statistical monitoring tool.
机译：提出了一种使用人工神经网络识别非随机变化模式以提高汽车装配过程中尺寸质量的开发方法。所提出的与过程知识基础集成的模式识别算法不仅用于检测变化模式，而且还用于解决控制图上非随机模式所表现出的不可接受变化的识别。一旦在控制图上出现任何非随机模式，就可以通过基于组装过程的知识调查每个可能的原因，系统地找到尺寸变化的根本原因。该信息将有助于进行工艺修改，以减少汽车车身装配过程中的尺寸变化。因此，可以预期的是，带有所提出的模式识别算法的控制图将作为系统的诊断工具而不是仅作为统计监视工具发挥更重要的作用。

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