Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Classification of Initial Stripe Height Patterns using Radial Basis Function Neural Network for Proportional Gain Prediction

Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish


This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.

Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1559


[1] Y. Mei and K. A. Stelson, "Lapping Control of Hard Disk Drive Heads," Journal of Dynamic Systems Measurement and Control, vol. 123, pp. 439-448, Sep. 2001.
[2] K. H. Ang and G. Chong, "PID Control System Analysis, Design, and Technology," IEEE Trans. Control Systems Technology, vol. 13, pp. 559-576, July 2005.
[3] K. J. Astrom and T. Hagglund, PID Controllers : Theory, Design, and Tuning. Research Triangle Park, NC: Instrument Soc. Amer., 1995.
[4] P. Cominos and N. Munro, "PID controllers: recent tuning methods and design to specification," IEE Proc. Control Theory Appl., vol. 149, pp. 46-53, Jan. 2002.
[5] Q. G. Wang, T. H. Lee, H. W. Fung, Q. Bi, and Y. Zhang, " PID Tuning for Improved Performance," IEEE Trans. Control Systems Technology, vol. 7 pp. 457-465, July 1999.
[6] J. C. Shen, " New tuning method for PID controller," ISA Trans. The Instrumentation Systems and Automation Society, vol. 41, pp. 474-484, 2002.
[7] W. Navidi, Statistics for Engineers and Scientists (3rd ed.). McGraw- Hill, NY: New York, 2008.
[8] L. Fu, Neural Networks in Computer Intelligence. McGraw-Hill, Amer., 1994.
[9] C. M. Bishop, Neural Networks for Pattern Recognition. Oxford University Press, Amer., 1995.
[10] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Morgan Kaufmann, CA: San Francisco, 2005.
[11] B. Ozcelik and T. Erzurumlu, "Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm," Journal of Materials Processing Technology, vol. 171, pp. 437-445, 2006.
[12] A.D. Mishra, V. D. Bhagile, G. B. Janvale, and S. C. Mehrotra , "A High Speed Design of Rectangular and Square Shape MSA for Higher Accuracy through RBF of ANN," IEEE Int. Conf. Microwave, pp. 671- 675, 2008.