TY - GEN
T1 - Feature-Based Transfer Learning for High-Value Component Recovery in Electric Vehicles
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
AU - Luo, Yang
AU - Xu, Yifeng
AU - Zhang, Fan
AU - Lo, Ying Tuan
AU - Lyu, Geng
AU - Tan, Andrew
AU - Majeed, Anwar P.P.Abdul
AU - Zhu, Yuyi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The worldwide transition to electric vehicles (EVs) has resulted in a substantial 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 features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed 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%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization 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 recovery 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.
AB - The worldwide transition to electric vehicles (EVs) has resulted in a substantial 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 features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed 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%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization 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 recovery 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.
KW - Deep Learning
KW - Electric Vehicles (EVs)
KW - End-of-Life (EoL)
KW - Feature-Based Transfer Learning
KW - Machine Learning
KW - Sustainable Manufacturing
UR - http://www.scopus.com/inward/record.url?scp=105002732335&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_29
DO - 10.1007/978-981-96-3949-6_29
M3 - Conference Proceeding
AN - SCOPUS:105002732335
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 368
EP - 375
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2024 through 23 August 2024
ER -