Classification of Initial Stripe Height Patterns using Radial Basis Function Neural Network for Proportional Gain Prediction

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.

Authors:

• Prasit Wonglersak
• Prakarnkiat Youngkong
• Ittipon Cheowanish

Keywords:

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

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