.png){kind=icon}
.png){kind=icon}
Return
Control chart pattern recognition using back propagation artificial neural networks
.png){kind=icon}
PRE
AI
DOI：10.1080/00207540110061616.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). Online automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. Two back-propagation artificial neural networks are used to model traditional Shewhart SPC charts and identify out-of-control situations as specified by the Western Electric Statistical Quality Control Handbook, including instability patterns, trends, cycles, mixtures and systematic variation. Using back propagation, patterns are presented to the network, and training results in a suitable model for the process. The implication of this research is that out-of-control situations can be detected automatically and corrected within a closed-loop environment. This research is the first step in an automated process monitoring and control system based on control chart methods. Results indicate that the performance of the back propagation neural networks is very accurate in identifying control chart patterns.
Keywords:
STATISTICAL PROCESS-CONTROL
IDENTIFICATION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
1.1W
Citations:
3.7W
Organization
No organization information available