TY - GEN
T1 - Ball Screw Drive Surface Defect Model Based on Transfer Learning Approach
AU - Xu, Yifeng
AU - Luo, Yang
AU - Majeed, Anwar P.P.Abdul
AU - Liu, Xiaoyan
AU - Zhu, Yuyi
AU - Chen, Wei
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This study proposes a transfer learning (TL) pipeline for detecting surface defects in ball screw drives, which are vital in industries like robotics and aerospace. The pipeline uses pre-trained CNN models—InceptionV3, VGG16 and VGG19—to extract features from defect images, followed by classification with SVM (Support Vector Machine) and kNN (k-Nearest Neighbors). The InceptionV3 + SVM combination excels in accuracy, recall, precision, and F1 score, highlighting its effectiveness in defect detection. The study underscores the importance of selecting appropriate CNN architectures and classifiers for specific defect detection tasks. The dataset from the Karlsruhe Institute of Technology, consisting of 2000 images, is used to evaluate the TL pipeline's performance. The findings suggest that the InceptionV3 + SVM model offers a reliable method for identifying ball screw drive defects, with potential for further optimization through expanded datasets and hyperparameter tuning.
AB - This study proposes a transfer learning (TL) pipeline for detecting surface defects in ball screw drives, which are vital in industries like robotics and aerospace. The pipeline uses pre-trained CNN models—InceptionV3, VGG16 and VGG19—to extract features from defect images, followed by classification with SVM (Support Vector Machine) and kNN (k-Nearest Neighbors). The InceptionV3 + SVM combination excels in accuracy, recall, precision, and F1 score, highlighting its effectiveness in defect detection. The study underscores the importance of selecting appropriate CNN architectures and classifiers for specific defect detection tasks. The dataset from the Karlsruhe Institute of Technology, consisting of 2000 images, is used to evaluate the TL pipeline's performance. The findings suggest that the InceptionV3 + SVM model offers a reliable method for identifying ball screw drive defects, with potential for further optimization through expanded datasets and hyperparameter tuning.
KW - Convolutional Neural Networks (CNN)
KW - Intelligent Manufacturing
KW - Machine Learning
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=105002717090&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_37
DO - 10.1007/978-981-96-3949-6_37
M3 - Conference Proceeding
AN - SCOPUS:105002717090
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 451
EP - 457
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
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Y2 - 22 August 2024 through 23 August 2024
ER -