Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components

© 2015 John Wiley & Sons, Inc. All Rights Reserved. This chapter presents the theoretical aspect concerning the application of artificial intelligence (AI) techniques to control the quality of hard disk drive (HDD) components. It first describes general guidelines of how AI could be applied to...

Full description

Saved in:
Bibliographic Details
Main Author: Wimalin Laosiritaworn
Format: Book
Published: 2018
Subjects:
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018653346&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/54346
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
id th-cmuir.6653943832-54346
record_format dspace
spelling th-cmuir.6653943832-543462018-09-04T10:12:14Z Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components Wimalin Laosiritaworn Computer Science © 2015 John Wiley & Sons, Inc. All Rights Reserved. This chapter presents the theoretical aspect concerning the application of artificial intelligence (AI) techniques to control the quality of hard disk drive (HDD) components. It first describes general guidelines of how AI could be applied to quality control (QC) task, and includes a review of some key literatures. AI tasks in QC can be classified into three groups: classification and prediction, clustering and time series analysis. Subsequently, the chapter presents three examples, namely, the case of using artificial neural networks (ANN) for multipanel lamination process modeling, HDD actuator arm control chart pattern recognition with AI, and machine clustering with AI. From all the examples, it can be concluded that AI tools have demonstrated highly promising results in the field of QC of HDD components where the process is complex and usually nonlinear. 2018-09-04T10:12:14Z 2018-09-04T10:12:14Z 2015-03-27 Book 2-s2.0-85018653346 10.1002/9781119058755.ch6 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018653346&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54346
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Wimalin Laosiritaworn
Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
description © 2015 John Wiley & Sons, Inc. All Rights Reserved. This chapter presents the theoretical aspect concerning the application of artificial intelligence (AI) techniques to control the quality of hard disk drive (HDD) components. It first describes general guidelines of how AI could be applied to quality control (QC) task, and includes a review of some key literatures. AI tasks in QC can be classified into three groups: classification and prediction, clustering and time series analysis. Subsequently, the chapter presents three examples, namely, the case of using artificial neural networks (ANN) for multipanel lamination process modeling, HDD actuator arm control chart pattern recognition with AI, and machine clustering with AI. From all the examples, it can be concluded that AI tools have demonstrated highly promising results in the field of QC of HDD components where the process is complex and usually nonlinear.
format Book
author Wimalin Laosiritaworn
author_facet Wimalin Laosiritaworn
author_sort Wimalin Laosiritaworn
title Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
title_short Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
title_full Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
title_fullStr Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
title_full_unstemmed Artificial Intelligence Techniques for Quality Control of Hard Disk Drive Components
title_sort artificial intelligence techniques for quality control of hard disk drive components
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018653346&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/54346
_version_ 1681424303737798656