Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

Manufacturing processes have become highly accurate and precise in recent years, particularly the chemical, aerospace, electronics industries. This has attracted researchers to investigate improved procedures for monitoring detection of small process variations remain line with such advances. Among these techniques, statistical controls (SPC), particular control chart pattern (CCP), a popular choice variance, being utilized numerous industrial manufacturing applications. study provides an recognition (CCPR) method focusing on X-bar patterns using ensemble classifier comprised five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian k-nearest neighbours. Before advancing classification step, Nelson’s Rus Rules were as rule distinguish between stable unstable processes. The study’s findings indicate that proposed improves performance mean changes less than 1.5 sigma, confirm is superior individual classifier. can types accuracy 99.55% ARL1 11.94.