Optimization of Design for Air Gap Sensor Using the Response Surface Methodology

In Hard Disk Drive (HDD) manufacturing, there is always a concern about the cutting defects that are caused by residual cutting chips. Only a small amount of 10 μm chips (act as the air gap) can cause the workpiece to tilt and shift from the correct position, and thus affect the dimension of the workpiece (mainly the Base HDD). For this reason, researchers adapted the adjustable micrometer as a simulation device that resembles the air gap for the design of the Air Gap Sensor Module. The design of experiments using response surface methodology will be studied to confirm the appropriate factors of the prototype. This study reports the optimization of the main factors that affect Air Gap Sensor Module condition: Air Nozzle Diameter 2.303 mm, Air Pressure 0.1 MPa, and Sampling Time 645 ms, which has a high square of the coefficient correlation (R-squared = 99.0%) with a close relationship between gap distance and air pressure. The relationship between these variables is mostly linear. The R-squared error percentage of actual value is less than 0.93% compared to predicted value. The mathematical model results and experimental values were consistent and able to predict response variables. The Air Gap Sensor Module can provide the measurement results in micron ccuracy and displays light and beep to confirm as acceptable or reject gap conditions with the uncertainty of measurement ± 0.001 mm.

FluidSMR: Adaptive Management for Hybrid SMR Drives

Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR.

Hard Disk Drive Failure Detection Using Hybrid Algorithm

The data is the most valuable thing in this modern world of Information Technology. As we can see the day to day the data is increasing as each and every people using the World Wide Web. This all system generated data or may be the personal or informative data will get generated in a huge amount of size. That data will get stored at the data centers or on cloud. But those will get stored on the Hard Disk Drives in data centers. So in some situation if the HDD got crashed then we will have lost our data. This work proposes to develop the failure prediction of Hard disk drive. We have chosen the accuracy and review measurements, generally important to the issue, and tried a few learning strategies, Adaboost, Naive Bayes, Logistic Regression and Voting. Our investigation shows that while we can't accomplish close to 100% forecast precision utilizing ML with the present information we have accessible for HDDs, we can improve our expectation exactness over the standard methodology Keywords: Machine learning, Adaboost, Naive Bayes, Voting, Logistic Regression

A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes

This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms).

Sliding repetitive control with adaptive bound estimation for motion systems liable to periodic exogenous signals

Periodic exogenous signals often exist in motion systems, especially those involving one or more rotating elements. These periodic exogenous signals deteriorate the performance of motion systems, and these adverse effects cannot be practically eliminated by straightforwardly increasing feedback control gains due to sensor noise, actuator saturation, and unmodeled plant dynamics. This paper describes a sliding repetitive controller for motion systems subject to periodic exogenous signals. Moreover, an adaptive law for bound estimation is devised to ensure the presence of a sliding motion for both repetitive learning and disturbance observation. The tracking motion system of a disk drive is considered in practice, and a traditional repetitive controller is also implemented for performance comparisons with the proposed scheme. Experimental results are reported in this paper, showing the efficacy of the proposed scheme.

Numerical simulations of flow induced noise from a dual rotor cooling fan used in electronic cooling systems

Hard Disk Drive (HDD) system enclosures in a data center require effective cooling systems to avoid HDD overheating. These systems often rely on air cooling because of their cost effciency and maintainability. Air cooling systems typically consist of an array of axial fans which push or pull the air through the system. These fans emit high level tonal noise particularly at high tip speed ratios. High-capacity HDDs, on the other hand, are sensitive to high acoustic noise, which consequently increases the risk of read/write error and deteriorates drive performance. Therefore, cooling fan noise adversely affects the function of the HDD enclosure systems which emphasizes the need to understand the noise sources and develop methods to mitigate HDD noise exposure.