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

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...

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Bibliographic Details
Published in:Information (Basel) 2021-12, Vol.12 (12), p.489
Main Authors: Saiz, Fátima A., Alfaro, Garazi, Barandiaran, Iñigo
Format: Article
Language:English
Subjects:
Automation
automotive industry
Automotive parts
Case studies
Classification
Climate change
component remanufacturing
Deep learning
Ensemble learning
Inspection
Machine learning
Manufacturers
Manufacturing
Pipelines
Product development
Quality control
quality inspection
Remanufacturing
Sustainable development
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12120489