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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 36, Number 4(2) (2024)
Copyright(C) MYU K.K.
pp. 1377-1387
S&M3605 Research Paper of Special Issue
https://doi.org/10.18494/SAM4825
Published: April 19 , 2024

Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process [PDF]

Worawit Kanjanapruthipong, Pitcha Prasitmeeboon, and Poom Konghuayrob

(Received December 26, 2023; Accepted March 13, 2024)

Keywords: flying height, deep neural network, particle swarm optimization, hard disk drive, DNNpso, AI

In contemporary hard disk drive (HDD) manufacturing processes, after the assembly of the HDD from the production line, a series of diverse calibration procedures are necessary to ensure standardization. These include capacity calibration, which determines the storage space in terabytes (TB) presently available, and flying height (FH) calibration, which evaluates the distance between the head and the disk by applying electric current to the heater coil element to achieve the desired FH, thus optimizing the writing and reading performance and tailoring it to each HDD. Additionally, electric current is saved in a digital-to-analog converter (DAC) unit for the utilization of a read/write head, while a preamp collaborates with the drive firmware to convert the electric current in the DAC unit to milliwatts. In the present scenario, multiple calibrations of flying heights (FHs), specifically flying height 1 (FH1) and flying height 2 (FH2), are performed. Each FH calibration requires a testing time of approximately 5 h owing to the separation of measurement points into 240 locations across the disk surface, referred to as test zones, with a total of 20 heads. The primary objective of this study is to reduce the testing time by using a combination of deep neural network (DNN) and particle swarm optimization techniques to predict the DAC profiles of FH2 as it approaches FH1, where FH1 is the input for the DNN model.

Corresponding author: Poom Konghuayrob


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Worawit Kanjanapruthipong, Pitcha Prasitmeeboon, and Poom Konghuayrob, Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process, Sens. Mater., Vol. 36, No. 4, 2024, p. 1377-1387.



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