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Author: search.filters.author.Kanjanapruthipong, WorawitSubject: search.filters.subject.AI

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    Anomaly Flying Height Prediction Based on Clustering Techniques in Hard Disk Drive Manufacturing
    (2025-01-01)
    Kanjanapruthipong, Worawit
    ;
    Konghuayrob, Poom
    In this research, we present a method for predicting anomaly flying height (FH) profiles in hard disk drive (HDD) manufacturing by analyzing FH data at the FH1 stage. Anomalies at FH1 can lead to calibration issues at FH2, disrupting the production process. We propose an AI-based approach using unsupervised clustering techniques to group FH profiles of the read/write head. We evaluated four clustering algorithms, KMeans, MiniBatchKMeans, Birch, and BisectingKMeans, along with the Elbow method to determine the optimal number of clusters. By identifying anomalous FH profiles early at FH1, the method enables proactive intervention, reducing calibration process time and improving production efficiency. Our model achieved an accuracy of 0.939 without relying on manual feature selection (e.g., pressure and temperature), which is often difficult to capture using traditional linear or rule-based models owing to the nonlinear nature of FH profiles. These results demonstrate the practical potential of clustering techniques in enhancing HDD manufacturing processes.
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    Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process
    (2024-01-01)
    Kanjanapruthipong, Worawit
    ;
    Prasitmeeboon, Pitcha
    ;
    Konghuayrob, Poom
    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.