PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads
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 perf...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11050437/ |
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| author | Chaiwat Punyammaree Somyot Kaitwanidvilai |
| author_facet | Chaiwat Punyammaree Somyot Kaitwanidvilai |
| author_sort | Chaiwat Punyammaree |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b977fa504de643fc8326ea2fc18e2736 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b977fa504de643fc8326ea2fc18e27362025-08-20T02:38:29ZengIEEEIEEE Access2169-35362025-01-011311062311063510.1109/ACCESS.2025.358260211050437PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write HeadsChaiwat Punyammaree0https://orcid.org/0009-0008-7209-2246Somyot Kaitwanidvilai1https://orcid.org/0009-0005-3255-3851School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, ThailandSchool of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, ThailandThis 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.https://ieeexplore.ieee.org/document/11050437/Hard disk drivecorrosion pitsparticle swarm optimizationimage segmentationU-Net |
| spellingShingle | Chaiwat Punyammaree Somyot Kaitwanidvilai PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads IEEE Access Hard disk drive corrosion pits particle swarm optimization image segmentation U-Net |
| title | PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads |
| title_full | PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads |
| title_fullStr | PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads |
| title_full_unstemmed | PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads |
| title_short | PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads |
| title_sort | pso optimized deep learning for ultra precise corrosion detection on hdd read write heads |
| topic | Hard disk drive corrosion pits particle swarm optimization image segmentation U-Net |
| url | https://ieeexplore.ieee.org/document/11050437/ |
| work_keys_str_mv | AT chaiwatpunyammaree psooptimizeddeeplearningforultraprecisecorrosiondetectiononhddreadwriteheads AT somyotkaitwanidvilai psooptimizeddeeplearningforultraprecisecorrosiondetectiononhddreadwriteheads |