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Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence.

Kyoung-Sook Moon1, Hee Won Lee2, Hongjoong Kim2

  • 1Department of Mathematical Finance, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea.

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Summary
This summary is machine-generated.

This study introduces a new machine learning algorithm to predict electronic component obsolescence. The method uses unsupervised clustering to improve prediction accuracy and speed up training for manufacturing industries.

Keywords:
component obsolescencediminishing manufacturing sources and material shortagesforecastingmachine learningunsupervised clustering

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Computer Science

Background:

  • Product obsolescence is a significant challenge in manufacturing, driven by innovation and cost-efficiency.
  • Proactive obsolescence prediction is crucial for minimizing manufacturing losses and enhancing customer satisfaction.

Purpose of the Study:

  • To develop a machine learning algorithm for proactive component obsolescence forecasting.
  • To improve the accuracy and efficiency of obsolescence prediction in the manufacturing sector.

Main Methods:

  • Proposed a novel machine learning algorithm utilizing an adaptive data selection method.
  • Employed unsupervised clustering to partition the dataset into multiple covers.
  • Constructed and trained individual models for each data cover, selecting optimal models for regression on test data.

Main Results:

  • The proposed algorithm demonstrated improved obsolescence prediction accuracy compared to traditional methods.
  • Empirical experiments confirmed accelerated training procedures.
  • The study validated the effectiveness of unsupervised clustering in enhancing supervised regression algorithms.

Conclusions:

  • Unsupervised clustering significantly enhances supervised regression for obsolescence prediction.
  • The developed algorithm offers a proactive strategy to mitigate manufacturing losses due to product obsolescence.
  • This approach contributes to improved efficiency and accuracy in forecasting electronic component lifecycles.