Ball Screw Drive Surface Defect Model Based on Transfer Learning Approach

Yifeng Xu, Yang Luo, Anwar P.P.Abdul Majeed*, Xiaoyan Liu, Yuyi Zhu, Wei Chen

*Corresponding author for this work

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

Abstract

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.

Original languageEnglish
Title of host publicationSelected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
EditorsWei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages451-457
Number of pages7
ISBN (Print)9789819639489
DOIs
Publication statusPublished - 2025
Event2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China
Duration: 22 Aug 202423 Aug 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1316 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/TerritoryChina
CitySuzhou
Period22/08/2423/08/24

Keywords

  • Convolutional Neural Networks (CNN)
  • Intelligent Manufacturing
  • Machine Learning
  • Transfer Learning

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