Feature-Based Transfer Learning for High-Value Com-ponent Recovery in Electric Vehicles: An InceptionV3 Model Evaluation

Yang Luo, Yifeng Xu, Fan Zhang, Ying Tuan Lo, Geng Lyu, Andrew Huey Ping Tan, Anwar P.P. Abdul Majeed*, Yuyi Zhu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

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Abstract

The worldwide transition to electric vehicles (EVs) has resulted in a substan-tial rise in the quantity of end-of-life (EoL) EVs that need effective recycling methods. This study examined a method of transfer learning that uses fea-tures to classify valuable electrical components from end-of-life electric ve-hicles. The study made use of a dataset consisting of high-resolution photo-graphs of different electronic control units (ECUs). The photos were pro-cessed using pre-trained InceptionV3 convolutional neural network (CNN) models to identify distinctive features. The performance of four classifiers, namely the Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Naive Bayes (NB), was tested using the collected features. The dataset was partitioned into training, validation, and test sets using a 70:15:15 stratified split to guarantee an equitable distribution of all classes. The InceptionV3-SVM pipeline achieved the highest performance, with training, validation, and test accuracies of 100%, 97%, and 97%, re-spectively. Other classifiers also demonstrated strong performance, with val-idation and test accuracies exceeding 94%. The high accuracy and generali-zation capabilities of the InceptionV3-SVM pipeline indicate its potential for practical deployment in sustainable manufacturing processes. This study provides a foundation for further research in the automated sorting and re-covery of high-value electronic components from EVs, potentially extending to a broader range of electronic components and applications. The findings highlight the effectiveness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry.
Original languageEnglish
Title of host publicationInternational Conference on Intelligent Manufacturing and Robotics 2024
Subtitle of host publicationICiMR 2024
Publication statusAccepted/In press - 2024
Event2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR) - Taicang, China
Duration: 22 Aug 202423 Aug 2024

Conference

Conference2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR)
Country/TerritoryChina
Period22/08/2423/08/24

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