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%).

References

Ebrahimzadeh, A., Addeh, J. and Ranaee, V. 2013. Recognition of Control Chart Patterns Using an Intelligent Technique. Applied Soft Computing Journal. 2970-2980.

Wang, J., Kochhar, A. and Hannam, R. 1998. Pattern Recognition for Statistical Process Control Chart. The International Journal of Advanced Manufacturing Technology. 99-109.

Masood, I. and Hassan, A. 2010. Issue in Development Artificial Neural Network-Based Control Chart Pattern Recognition Schemes. European Journal of Scientific Research. 336-355.

Bag, M. and Gauri, S.K 2012. An Expert System for Control Chart Pattern Recognition. International Journal Advanced Manufacturing Technology. 291-301.

Pacella, M, Semeraro, Q. and Anglani, A. 2014. Adaptive Resonance Theory-Based Neural Algorithms for Manufacturing Process Quality Control. International Journal of Production Research. 4581-4607.

Wani, M.A. and Rashid, S. 2005. Parallel Algorithm for Control Chart Pattern Recognition. Proceedings of the 4th International Conference on Machine Learning. 89-96.

Gauri, S.K. and Chakraborty, S. 2009. Recognition of Control Chart PPatterns Using Improved Selection of Features. Computer and Industrial Engineering. 1577-1588.

Ebrahimzadeh, A. and Ranaee, V. 2010. Contol Chart Pattern Recognition Using an Optimized Neural Network and Efficient Features. ISA Transaction. 387-393.

Ranaee, V. and Ebrahimzadeh, A. 2011. Control Chart Pattern Recognition Using a Novel Hybrid Intelligent Method. Applied Software Computing. 2676-2686.

Hassan, A., Nabi Baksh, M.S., Shaharoun, A.M. and Jamaluddin, H. 2003. SPC Chart Pattern Recognition Using Statistical Features. International Journal of Production Research. 1587-1603.

Rumelhart, D.E., Hinton, G.E. and William, R.J. 1986. Learning Internal Representations by Error Propagation. Parallel Distributed Processing. Vol.1. 318-362.

Mahdiani, M., Judi, H.M. and Ashaari, N.S. 2014. Abnormal Control Chart Pattern Classification Optimisation Using Multilayered Perceptrons. Research Journal of Applied Sciences, Engineering and Technology. 4690-4695.

Saravanan, A. and Nagarajan, P. 2013. Performance of ANN in Pattern Recognition for Process Improvement Using Lavenberg-Marquardt and Quasi-Newton Algorithm. IOSR Journal Engineering. 8-13.

Hassan, A., Nabi Baksh, M.S., Shaharoun, A.M. and Jamaluddin, H. 2006. Feature Selection for SPC Chart Pattern Recognition Using Fractional Factorial Experimental Design. Virtual International Conference on Innovative Production Machine and Systems.

Nelson, L.S. 1989. Standardization of Shewhart Control Chart. Journal of Quality Technology. 287-289.

Demuth, H. and Beale, M. 1998. Neural Network Toolbox, User’s Guide. The MathWorks Natick.

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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

Issue

Vol. 9 No. 3-2: EMPOWERING SCIENCE, ENGINEERING AND TECHNOLOGY TOWARDS GLOBAL MANUFACTURING SUSTAINABILITY

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Articles

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