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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Optimization of Mechanical Properties and Evaluation of Fatigue Behavior of Selective Laser Sintered Polyamide-12 Components.

Polymers·2024
Same author

Birefringence in Injection-Molded Cyclic Olefin Copolymer Substrates and Its Impact on Integrated Photonic Structures.

Polymers·2024
Same author

Fundamental Considerations and Analysis of the Energy Distribution in Laser Turning with Ultrashort Laser Pulses.

Micromachines·2023
Same author

Preparation of Dispersed Copper(II) Oxide Nanosuspensions as Precursor for Femtosecond Reductive Laser Sintering by High-Energy Ball Milling.

Nanomaterials (Basel, Switzerland)·2023
Same author

Drilling Sequence Optimization Using Evolutionary Algorithms to Reduce Heat Accumulation for Femtosecond Laser Drilling with Multi-Spot Beam Profiles.

Materials (Basel, Switzerland)·2023
Same author

Integration of Bragg gratings in aerosol-jetted polymer optical waveguides for strain monitoring capabilities.

Optics letters·2023

Related Experiment Video

Updated: Nov 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

807

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network.

Benedikt Adelmann1, Max Schleier1, Ralf Hellmann1

  • 1Applied Laser and Photonics Group, University of Applied Sciences Aschaffenburg, Wuerzburger Straße 45, 63739 Aschaffenburg, Germany.

Sensors (Basel, Switzerland)
|January 22, 2021
PubMed
Summary

A convolutional neural network detects laser cutting interruptions from single images. This AI system achieves high accuracy with a low error rate, making it industrially applicable for quality control.

Keywords:
convolutional neural networkcut interruptionimage processinglaser cuttingremote sensing

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

1.3K

Related Experiment Videos

Last Updated: Nov 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

807
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

1.3K

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Manufacturing Engineering

Background:

  • Laser cutting is a critical industrial process.
  • Detecting cut interruptions is essential for quality control.
  • Current methods for interruption detection can be inefficient.

Purpose of the Study:

  • To develop an automated system for detecting cut interruptions during laser cutting using a convolutional neural network (CNN).
  • To evaluate the performance of the CNN in classifying images of laser-cut steel sheets.
  • To assess the industrial applicability of the developed detection system.

Main Methods:

  • A small convolutional neural network (CNN) was trained to analyze single images from a high-speed camera.
  • Images captured without additional illumination at 32 × 64 pixels resolution were used.
  • The CNN classified images into 'cuts' and 'cut interruptions' for steel sheets of varying thicknesses and laser parameters.

Main Results:

  • The CNN achieved a low error rate of 0.05% after a short learning period (five epochs) on a specific sheet thickness.
  • Color images showed a slight advantage over greyscale images, with lower error rates.
  • A single network trained on all sheet thicknesses resulted in test error rates below 0.1%.
  • The system demonstrated a short calculation time of 120 µs on a standard CPU.

Conclusions:

  • The developed CNN system effectively detects cut interruptions in laser cutting with high accuracy.
  • The system's low error rate and fast processing time indicate strong industrial applicability.
  • Utilizing color images and training on diverse sheet thicknesses enhances detection performance.