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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.

Sensors (Basel, Switzerland)

|October 27, 2022

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 obsolescence diminishing manufacturing sources and material shortages forecasting machine learning unsupervised 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.