An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning

Abstract

This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performance when used individually and when using an ensemble of them is carried out, obtaining an improvement of 7% in accuracy in the ensemble. The results of the test set demonstrate the successful performance of the system in terms of component classification.

BIB_text

@Article {
title = {An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning},
journal = {Information},
pages = {489},
volume = {12},
keywds = {
quality inspection; deep learning; ensemble learning; component remanufacturing; automotive industry
}
abstract = {

This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performance when used individually and when using an ensemble of them is carried out, obtaining an improvement of 7% in accuracy in the ensemble. The results of the test set demonstrate the successful performance of the system in terms of component classification.


}
doi = {10.3390/info12120489},
date = {2021-12-01},
}
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