Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Celastrol Attenuates Fgf23 Expression in Osteoblasts by Inhibiting STAT3 Activation.

Endocrine, metabolic & immune disorders drug targets·2026
Same author

Gelsolin amyloidosis presenting with nephrotic syndrome: a case report and molecular insights.

Frontiers in medicine·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

Unified Online Adaptation Framework for Correlation Analysis-based Spatial Filtering Methods in SSVEP-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Gender Differences in Neurobehavioural Signatures of Interpersonal Negotiation Revealed by EEG Hyperscanning.

International journal of neural systems·2026
Same author

MRI-Based Habitat Analysis for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.1K

Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts.

Li Zeng1, Feng Wan2, Baiyun Zhang3

  • 1School of Mechanical and Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

An automated machine vision system accurately detects and classifies surface defects on Cubic Boron Nitride (CBN) inserts. This enhances quality control in precision manufacturing with over 90% accuracy.

Keywords:
CBN insertdeep learningdefect detectionvisual inspection technology

More Related Videos

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
07:42

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material

Published on: December 20, 2024

311
Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive
06:40

Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive

Published on: September 27, 2013

14.7K

Related Experiment Videos

Last Updated: Jun 4, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.1K
Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
07:42

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material

Published on: December 20, 2024

311
Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive
06:40

Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive

Published on: September 27, 2013

14.7K

Area of Science:

  • Materials Science and Engineering
  • Manufacturing Technology
  • Computer Vision and Image Processing

Background:

  • Cubic Boron Nitride (CBN) inserts are critical in precision manufacturing due to their exceptional hardness.
  • Surface defects on CBN inserts can significantly degrade product integrity and performance.
  • Existing defect detection methods may lack the speed and accuracy required for automated production lines.

Purpose of the Study:

  • To develop and validate an automated machine vision system for detecting and classifying surface defects on CBN inserts.
  • To evaluate the performance of various defect detection algorithms for CBN insert inspection.
  • To create a robust and efficient system for enhancing quality control in high-speed manufacturing.

Main Methods:

  • Integration of an optical bracket, high-resolution industrial camera, precise lighting, and an advanced development board for image acquisition.
  • Application of digital image processing techniques for defect identification and categorization.
  • Comparative analysis of multiple defect detection algorithms, considering parameter tuning and dataset diversity.

Main Results:

  • The developed system achieves a detection accuracy exceeding 90% for multiple defect types.
  • The system demonstrates a tooth surface recognition efficiency of three frames per second.
  • The front and side cutting surfaces of the tool are effectively captured and analyzed within each frame.

Conclusions:

  • The proposed machine vision system offers a scalable and reliable solution for automated surface defect detection on CBN inserts.
  • This technology significantly improves quality control in automated, high-speed precision manufacturing environments.
  • The system's high accuracy and efficiency pave the way for enhanced production line monitoring and defect management.