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

Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

203
Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
203

You might also read

Related Articles

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

Sort by
Same author

Stagnation and Progression: How Glymphatic Failure Promotes Meningioma Malignancy in Aging.

Aging and disease·2026
Same author

Microsurgery of a rare case of abducens schwannoma: surgical techniques.

Chinese clinical oncology·2026
Same author

Cisternal segment trochlear nerve schwannoma.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2026
Same author

Diversified synthesis with causal-intervened separation for glioblastoma progression diagnosis.

Computers in biology and medicine·2025
Same author

Research on motion tracking and impact force detection of flying objects.

Scientific reports·2025
Same author

A Hybrid CBiGRUPE Model for Accurate Grinding Wheel Wear Prediction.

Sensors (Basel, Switzerland)·2025
Same journal

Serum vitamin D level and its association with vertigo frequency and severity in Meniere disease.

Scientific reports·2026
Same journal

PFA-Net: a physics-informed feature enhancement and attention network for interpretable bearing fault diagnosis under strong noise.

Scientific reports·2026
Same journal

Circulating inflammatory, redox, and apoptosis-related alterations in drug-naive idiopathic pulmonary fibrosis: an exploratory case-control study.

Scientific reports·2026
Same journal

A baseline-oriented dynamic aggregation approach for demand-side heterogeneous controllable resources.

Scientific reports·2026
Same journal

Temporal precision and accuracy in schizophrenia: an exploratory study.

Scientific reports·2026
Same journal

Prefrontal EEG spectral and nonlinear signatures of subthreshold depression during resting state and affectively valenced picture/video viewing: a participant-level analysis.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry
08:47

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Published on: February 2, 2018

12.3K

Grinding wheel wear evaluation with the PMSCNN model.

Sumei Si1,2, Zekai Si1,3, Deqiang Mu4

  • 1College of Electromechanical Engineering, Changchun University of Technology, Changchun, 130012, China.

Scientific Reports
|August 6, 2025
PubMed
Summary
This summary is machine-generated.

A new model, PMSCNN, accurately assesses grinding wheel wear using motor current signals and machine learning. This method enhances machining efficiency and quality by predicting wear trends effectively.

Keywords:
CNNGradient boosting regressorGrinding wearMulti-head self-attention mechanismPosition encoding

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

3.1K

Related Experiment Videos

Last Updated: Sep 12, 2025

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry
08:47

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Published on: February 2, 2018

12.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

3.1K

Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Grinding wheel wear critically impacts machining efficiency and product quality.
  • Accurate assessment of grinding wheel wear is essential for optimizing manufacturing processes.

Purpose of the Study:

  • To develop and validate a novel grinding wheel wear assessment model named PMSCNN.
  • To improve the accuracy and reliability of predicting grinding wheel wear using machine learning.

Main Methods:

  • A Convolutional Neural Network (CNN) and Transformer model (PMSCNN) were developed for wear assessment.
  • Grinding wheel spindle motor current signals were measured and processed using median filtering.
  • Feature importance was analyzed using a gradient boosting regressor, selecting the top four features.
  • The PMSCNN model's predictive accuracy was validated using these selected features.

Main Results:

  • The PMSCNN model demonstrated good similarity between predicted and real wear trends.
  • Cross-validated results showed an average Mean Absolute Error (MAE) of 3.028, Root Mean Square Error (RMSE) of 3.938, and R-squared (R²) of 0.919.
  • Modular analysis confirmed each component's contribution to the model's performance.

Conclusions:

  • The PMSCNN model effectively extracts wear-related patterns from current signals.
  • The developed model achieves high prediction accuracy for grinding wheel wear.
  • This approach offers a promising solution for real-time monitoring and assessment of grinding wheel wear.