质量控制图在线智能诊断分析系统

在计算机集成制造系统环境下，为了有效实现工序质量控制，提出了质量控制图的在线智能诊断分析系统框架，它由控制图模式识别、参数估计、专家诊断分析系统和加工参数调整系统四个模块组成。在该系统中，采用了一种适用于模式识别与分类的新型神经网络模型——局部有监督特征映射网络，将其应用于该系统的控制图模式识别和参数估计中。仿真实验和应用实例表明，识别和分类结果与实际相符，并可以保证实时性。

On-line intelligent diagnosis and analysis system for quality control charts

To control the machining process quality more effectively under the Contemporary Integrated Manufacturing System (CIMS) environment, an on-line intelligent diagnosis and analysis system for quality control charts was proposed, which consisted of four modules, namely, pattern recognition module, parameter estimation module for abnormal patterns, diagnosis and analysis expert system and process adjustment system. To recognize the control chart patterns and estimate the parameters of abnormal patterns effectively in this intelligent diagnosis and analysis system, a new neural network model named regional supervised feature mapping (RSFM) network was proposed. The topology structure and training algorithm of this network were represented, and its basic performance was also studied. Numerical simulation and an engineering application example are provided to demonstrate that RSFM network is fit for pattern recognition and parameter estimation of control charts in a real time SPC system.