Control Chart Pattern Recognition in Metal-Stamping Process Using Statistical Features-Ann

Authors

  • Norasulaini Abdul Rahman Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
  • Ibrahim Masood Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
  • Mohd Nasrull Abdol Rahman Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
  • Nurul Fitriah Nasir Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.

Keywords:

Control Chart Pattern Recognition, Features-Based, Dynamic Training Pattern, Unnatural Variation,

Abstract

Identification for the sources of unnatural variation (SOV) in manufacturing process is vital in quality control. In case of metal stamping process, the SOV based on special causes has become a major contributor to poor quality product. In recent years, researchers are still debating to find an effective technique for on-line monitoring-diagnosis the SOV. Control chart pattern recognition (CCPR) method has been reported as applicable for this purpose, whereby the existing CCPR schemes were trained using the artificially statistical process control (SPC) samples. Inversely, the trained scheme using real SPC samples have not been reported since the data are limited or not economically available. In this paper, the SPC samples were taken directly from an actual metal stamping process to be used as the dynamic training patterns. The proposed features-based method has resulted in higher diagnosis accuracy (normal patterns = 100%, unnatural patterns = 100%) compared to the raw data-based method (normal patterns = 66.67%, unnatural patterns = 26.97%).

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Published

2017-10-15

How to Cite

Abdul Rahman, N., Masood, I., Abdol Rahman, M. N., & Nasir, N. F. (2017). Control Chart Pattern Recognition in Metal-Stamping Process Using Statistical Features-Ann. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-2), 5–9. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2803