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PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads
(2025-01-01)
Punyammaree, Chaiwat
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Kaitwanidvilai, Somyot
This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. A U-Net model optimized via Particle Swarm Optimization (PSO) is developed to enhance segmentation performance by automatically tuning hyperparameters. The methodology includes optimized SEM image acquisition, preprocessing (patch-based subdivision and expert annotation), PSO-driven hyperparameter selection, and post-processing with thresholding and connected component analysis for pit counting. Experimental results demonstrate that the PSO-optimized U-Net significantly outperforms standard U-Net, SegNet, and LinkNet models, achieving an F1-score of 79.60%, an IoU of 86.51%, and an accuracy of 99.77%. Additionally, the proposed method achieves 86.9% counting accuracy, surpassing human experts (72.7%) while processing images 15 times faster (180 seconds vs. 2700 seconds per image). These findings highlight the potential of PSO-optimized deep learning for improving HDD quality control by providing an accurate, efficient, and standardized solution for corrosion pit detection, ultimately reducing the risk of HDD failure and data loss.
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Safety path planning with obstacle avoidance using particle swarm optimization for agv in manufacturing layout
(2019-02-01)
Praserttaweelap, Rawinun
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Kaitwanidvilai, Somyot
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Aoyama, Hisayuki
In robotic systems, path planning is the one of important processes for robot motion. The best path planning is required for shortest path searching that can make fast movement of robot. However, the real environment is not only the path from point to point but it has obstacles which are the one of constraints for best path searching. The obstacle avoidance is concerned to avoid the crashing between robot and obstacle under environment. In Hard Disk Drive manufacturing, the first priority is safety constraint for non-collision and second priority is shortest path for processing time saving. This research designed the algorithm for path planning and obstacle avoidance for AGV in Hard Disk Drive Manufacturing of Seagate Technology (Thailand) Ltd by using particle swarm optimization. The fitness function on particle swarm optimization process for particle searching has been integrated with obstacle avoidance function to find the best path for robot without collision and total distance to find the shortest path. This algorithm is applied to verifying the model performance. The simulation results of this research are done by MATLAB 2016b and illustrate the good performance on different cases with controlled parameter.
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Hybrid adaptive notch filter and fixed-structure pid H ∞ robust loop shaping control based pso for hard disk drive servo actuator
(2019-02-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
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Aoyama, Hisayuki
In order to achieve the high precision head-positioning of the voice coil motor (VCM) actuator with narrow track pitch, the adaptive notch filter based limited-search-range of particle swarm optimization (PSO), as well as the fixed-structure propor-tional-integral-derivative H <inf>∞</inf> robust loop shaping controller using the concept of four closed loop disturbance norms is proposed. Generally the conventional method, fixed-frequency notch filter (FFNF), is combined with the nominal plant to reduce the effect of the mechanical vibration resonance; however, the resonance mode of servo system can be shifted with various factors such as the ambient temperature change, and the unbalanced disk. In addition, mathematical solving in the H <inf>∞</inf> robust control problems and the suitable notch filter design are very complex and the final results of the conventional controller with notch filters are normally complicated structure and high order which is difficult to implement. Thus, the adding of intelligent system in the proposed design with the careful range of the search space is utilized to suppress the vibration caused by resonance mode shifting and also reduce the order of the final robust controller. Simulation results of six scenarios test demonstrate the effectiveness of the proposed design compared with FFNF in the commercial product.
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Low order robust ν-gap metric H∞ loop shaping controller synthesis based on particle swarm optimization
(2017-12-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
The strong demand of the data storage capacity has been increasing significantly. According to the heat-assisted magnetic recording (HAMR) technology, the demand trend of hard disk drive (HDD) is predicted that the areal density will be achieved 10 Tbit/in<sup>2</sup> before the year 2020. High areal density results in a narrow track pitch which is quite sensitive to the external disturbance including the measured noise. This point is the benchmark problem of the high precision controller design for controlling the HDD servo mechanism. Moreover, the systematic uncertainties have to be taken into consideration in controller design procedure as well. The alternative robust ν-gap metric related to H<inf>∞</inf> loop shaping is proposed in this paper to stabilize a voice coil motor in HDD under the uncertainty condition. The potential particle swarm optimization (PSO) is adopted to minimize the gap between the plant with H<inf>∞</inf> controller and the plant with specified 3 controller orders. Instead of using the conventional H<inf>∞</inf> controller with high order with a complicated structure, this paper applies the proposed lower controller order based on ν-gap which is more appropriate implement in the actual application. The performance and robustness of both controllers are compared in the simulation studies. The results confirm the similar characteristics of both controllers in terms of performance tracking and disturbance rejection. Furthermore, the system stability index called stability margin with 0.472 and system perturbations testing condition also emphasizes the robustness and effectiveness of the proposed controller.
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Specified order - H∞ loop shaping control for hard disk drive servo using pso
(2016-08-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
The demand of data storage density has been increasing continuously, but the data access time and track pitch need to be decreased. The high precision servo control system is required to achieve the predicted goal that will be crossing 10 TB/in<sup>2</sup> area, I density before 2020. Due to the fact that the precision of read/write head is sensitive to disturbance and noise, it is the challenging problem for the practical control. To overcome this problem, this paper proposes a new servo controller designed, specified order - H<inf>∞</inf> loop shaping (SOHLS) which adapts the particle swarm optimization (PSO) for searching the optimal controller parameters. The proposed technique can solve the problem of high order that was caused from the conventional H<inf>∞</inf> loop shaping (HLS), in addition, it can guarantee the robustness of the servo system,. Based on simulation results, the performances of the proposed SOHLS approach are investigated in comparison with that of the conventional H<inf>∞</inf> robust loop shaping, fixed-structure H<inf>∞</inf> control based on nonsmooth algorithm including reduced order controller by Hangkel norm model reduction technique. The proper repeatable runout (RRO) and non-repea table runout (NRRO) were applied, to the system to verify the robustness of all designed, controllers. The results demonstrate the advantages of the proposed SOHLS which gains better performance and more robustness than the other conventional controllers.
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Automatic visual inspection of bump in hard disk drive component using neural network and image processing
(2008-12-01)
Kaitwanidvilai, Somyot
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Seanton, Anakkapon
This paper presents the progressive development of the automatic visual inspection system for hard disk drive manufacturing. The developed system is applied to inspect the flip-chip solder joints called 'bump' on the flexible PCB. In the designed system, conventional image processing is utilized to find the interesting features from the Flip-Chip image. Such features are adopted as the inputs of the trained artificial neural network (ANN). The designed ANN is used as the expert system for classifying the completeness of bump. To compare performance, the measured value of the bump's length from the proposed inspection system is investigated in comparison with the value from the destructive inspection using micro-electroscope. Experimental results show that our developed system is effectively used to automatically inspect the bump. Inspection time of the proposed system is much faster than that of the human inspection.

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