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IJML 2023 Vol.13(4): 181-184
DOI: 10.18178/ijml.2023.13.4.1148

Data Balancing and Aggregation Strategy to Predict Yield in Hard Disk Drive Manufacturing

Nittaya Kerdprasop*, Anusara Hirunyawanakul, Paradee Chuaybamroong, and Kittisak Kerdprasop

Manuscript received January 10, 2023; revised February 20, 2023; accepted April 27, 2023.

Abstract—Hard disk drive manufacturing is complicated and involves several steps of assembling and testing. Poor yield in one step can result in fail product of the whole lot. Accurate yield prediction is thus important to product monitoring and management. This paper presents a novel idea of data preparation and modeling to predict yield in the process of hard disk drive production. Data balancing technique based on clustering and re-sampling is introduced to make the proportion of the pass and fail products comparable. Then, we propose a strategy to aggregate manufacturing data to be in a reasonable group size and efficient for the subsequent step of yield predictive model creation. Experimental results reveal that grouping data into a constant size of 10,000 records can lead to the more accurate yield prediction as compared to the intuitive idea of weekly grouping.

Index Terms—Data balancing, data aggregation, yield prediction, hard disk drive manufacturing, machine learning

Nittaya Kerdprasop and Kittisak Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Thailand.
Anusara Hirunyawanakul is with the Data Science and Computation School, King Mongkut's University of Technology North Bangkok, Rayong Campus, Thailand.
Paradee Chuaybamroong is with the Department of Environmental Science, Thammasat University, Thailand.
*Correspondence: nittaya@sut.ac.th (N.K.)


Cite: Nittaya Kerdprasop*, Anusara Hirunyawanakul, Paradee Chuaybamroong, and Kittisak Kerdprasop, "Data Balancing and Aggregation Strategy to Predict Yield in Hard Disk Drive Manufacturing," International Journal of Machine Learning vol. 13, no. 4, pp. 181-184, 2023.

Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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