An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning
Fátima A. Saiz, Garazi Alfaro, Íñigo Barandiarán
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.
Topics & Concepts
RemanufacturingAutomotive industryComponent (thermodynamics)Ensemble learningComputer scienceArtificial intelligenceEnsemble forecastingSet (abstract data type)Machine learningPattern recognition (psychology)EngineeringManufacturing engineeringPhysicsProgramming languageThermodynamicsAerospace engineeringIndustrial Vision Systems and Defect DetectionRecycling and Waste Management TechniquesManufacturing Process and Optimization