Classifying and Optimizing Spiral Seed Self-Servo Writer Parameters in Manufacturing Process Using Artificial Intelligence Techniques

Abstract

This paper presents a technique for evaluating the performance of high-precision machines and classifying machine conditions in terms of test capability, such as hard disk drive (HDD) signal writing machines. In general, position errors generated during the signal writing process must be minimized to ensure high-quality writing. Position errors refer to deviations in the signal writing process and can be caused by several factors, such as deviations in the performance of the positioner that result in a position error signal exceeding its control limit. The proportion-al-integral-derivative (PID) controller must be optimized to minimize position errors. In model-based controller tuning, an accurate mathematical model is essential. The first step utilizes system identification methods, including adaptive weight least squares and peak detection, to create a partition resonance frequency model. This mathematical model is used to determine the open-loop stability, which involves achieving gain and phase margin at a specific crossover frequency, and the closed-loop dynamic response, which involves minimizing the discrete Fourier transform (DFT) of the position error signal. The DFT of the position error signal in each harmonic can be represented as a resonance peak in the transfer function model. The DFT and other combinations of operating parameters are analyzed and used as machine learning features. The ANN classifier was also effective in categorizing the performance of signal writing machines into four classes: 0 (healthy machine), 1 (sensor fault), 2 (loose pushpin), and 3 (tunable machine). The results showed that the classification performance was sufficient to separate class 1 and 2 for the maintenance process and class 3 for further optimization achieved using the mathematical model.