[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122424-en":3,"doc-seo-122424-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122424,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Optimization signal writing with machine learning assisted control","The paper addresses high-precision hard disk drive (HDD) signal writing failures caused when the discrete Fourier transform (DFT) of the position error signal (PES) exceeds control limits. A combined machine learning classification and controller optimization strategy identifies machine classes with high potential for improving writing quality, separating them from obviously degraded cases. Genetic algorithm-based controller tuning optimizes controller gain using objectives tied to crossover frequency, phase margin, and low-frequency PES DFT. Experiments show improved signal writing quality for class 0 and class 3, reducing the failure rate from 0.1% to 0.05%.","Optimization signal writing with machine learning assisted  \ncontrol  \nChaweng Sapapporn1,3, Soontaree Seangsri2, Sorada Khaengkarn1, Jiraphon Srisertpol1  \n1School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand 2School of Engineering and Innovation, Rajamangala University of Technology Tawan-ok, Chon Buri, Thailand 3Western Digital Storage Technologies (Thailand) Ltd., Bang Pa-in Industrial Estate, Ayutthaya, Thailand  \nArticle history:  \nReceived Apr 8, 2024 Revised Sep 2, 2024 Accepted Sep 29, 2024  \nKeywords:  \nController gain optimization Genetic algorithm  \nHDD signal write machine Machine learning  \nPosition error signal  \nCorresponding Author:  \nThe high-precision signal writing machine, experiencing a 0.1% failure rate due to discrete fourier transform (DFT) of position error signal (PES) exceeding control limits, can be improved with an appropriate controller gain. This paper combines machine learning (ML) classification and controller optimization to determine the suitable gain for the hard disk drive (HDD) signal writing process. The result from machine classification has a high potential for position error improvement, distinguishing them from those with obvious degradation. The identified machine classes with high potential for signal write quality improvement undergo controller optimization using a genetic algorithm (GA) . The objective function considers gain crossover frequency, phase margin, and PES DFT at low frequencies. Experimental results demonstrate that the new controller gain enhances signal write quality of class 0 and class 3 by 14.68% and 17.18%, respectively, leading to a reduced failure rate down to 0.05% .  \nThis is an open access article under the CC BY-SA license.  \nJiraphon Srisertpol  \nSchool of Mechanical Engineering, Suranaree University of Technology Nakhon Ratchasima, Thailand  \nEmail: [jiraphon@sut.ac.th](jiraphon@sut.ac.th)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe precise control of hard disk drive (HDD) demands high-precision movement [1], [2] . The HDD signal writer, which writes tracks to identified locations on the disk, employs a proportional integral derivative (PID) type controller that undergoes fine-tuning by expert designers using high-performance machines to handle actuator resonances, vibration rejection, and command following before deployment to manufacturing [3] . While PID controllers are commonly used, continuous and prolonged use can lead to errors in the components of the signal writer. Currently, skilled experts are employed to diagnose and identify the causes of abnormalities in the HDD signal writer in order to categorize them for repair. Some machines with improvement potential can reduce positional errors by optimizing controller gain. However, this process is time-consuming in large-scale manufacturing, as machines requiring maintenance and optimization are mixed in with production. Therefore, artificial intelligence-based classification systems and optimization systems must work together to address the problem effectively.  \nArtificial neural networks (ANN) are employed in various classification applications, including classifying HDD signal writing machine performance. This classification uses operating parameters as features and the symptom of position signal error movement as the label. Machines are classified into four groups: Groups 1 and 2 are earmarked for maintenance due to obvious internal damage, while Groups 0 and 3 are considered for controller optimization [4] . These findings align with past studies on classifying failuresin high-speed auto core adhesion mounting machines [5] and mounting head degradation classification [6] .  \nAnother important task is PID controller tuning, which can restore performance in drives that have degraded in position movement. Several well-known methods for assessing the robustness and performance of PID controllers in both the frequency and time domains are discussed","cbCaiq2CZ0fr9cm4","https://ap.wps.com/l/cbCaiq2CZ0fr9cm4","pdf",992804,1,11,"English","en",105,"# Article Info ABSTRACT\n# 1. INTRODUCTION\n## Problem of HDD signal writer precision and PID fine-tuning\n## Classification with artificial neural networks (ANN)\n## PID controller tuning methods and comparison","[{\"question\":\"Why does the HDD signal writing machine fail in this study?\",\"answer\":\"Failures occur when the DFT of the position error signal (PES) exceeds predefined control limits, producing a measurable failure rate.\"},{\"question\":\"How does the machine learning part support controller optimization?\",\"answer\":\"An ANN classification model uses operating parameters as features and PES error movement symptoms as labels, selecting machine classes with high potential for improvement (not the obviously degraded ones).\"},{\"question\":\"What does the genetic algorithm optimize in the controller tuning?\",\"answer\":\"The genetic algorithm searches for an appropriate controller gain by considering gain crossover frequency, phase margin, and low-frequency PES DFT as the objective components.\"}]","Optimization signal writing with machine learning assisted control | 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