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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

286
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
286
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

178
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
178
PD Controller: Design01:26

PD Controller: Design

352
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
352

You might also read

Related Articles

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

Sort by
Same author

Genome-wide association studies and candidate gene identification under salinity stress in bread wheat (<i>Triticum aestivum</i> L.).

Frontiers in plant science·2026
Same author

Genetic dissection and stability analysis of grain protein content and thousand-grain weight in emmer wheat (Triticum turgidum subsp. dicoccum) germplasm.

Scientific reports·2026
Same author

Multivariate assessment of newly developed guava (Psidium guajava L.) hybrids for tree and fruit quality traits.

Scientific reports·2026
Same author

Conversion of elite bread wheat cultivars HD3086 and HD2932 into cytoplasmic male sterile (CMS) lines and their genetic assessment to develop CMS-based hybrids.

BMC plant biology·2025
Same author

Epigenetic Switches in the Proximal 3' UTR Reprograms TaFAR1-L Temporal Expression in Wheat During Leaf Rust Pathogenesis.

Physiologia plantarum·2025
Same author

Enhanced Electrochemical Detection for DNA Hybridization on Core-Shell Magnetic Silica Sphere Gold Nanoparticles.

Langmuir : the ACS journal of surfaces and colloids·2025

Related Experiment Video

Updated: Sep 11, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K

Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.

Vijay Kumar1, Shailendra Kumar Jha2

  • 1Mechanical Engineering, IIMT College of Engineering, Greater Noida, India.

Network (Bristol, England)
|August 11, 2025
PubMed
Summary

A new Modified Ant Lion Optimization- Artificial Neural Network (MALO-ANN) technique optimizes Electrical Discharge Diamond Grinding (EDDG) processes. This method significantly improves material removal rate and surface roughness for durable, conductive materials.

Keywords:
Artificial neural networkboron carbideelectrical discharge diamond grinding systemmaterial removal rate; surface roughness and modified ant lion optimization algorithm

More Related Videos

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis
13:09

Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis

Published on: January 6, 2016

14.9K

Related Experiment Videos

Last Updated: Sep 11, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis
13:09

Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis

Published on: January 6, 2016

14.9K

Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Electrically conductive materials are vital for many applications due to their strength and stiffness.
  • Electrical Discharge Diamond Grinding (EDDG) is a key method for producing these materials.
  • Traditional Artificial Neural Network (ANN) models often face performance issues due to suboptimal hidden layers and weights.

Purpose of the Study:

  • To introduce and evaluate the Modified Ant Lion Optimization- Artificial Neural Network (MALO-ANN) technique.
  • To enhance the performance and parametric optimization of the EDDG process.
  • To investigate the impact of input factors on Material Removal Rate (MRR) and Surface Roughness (SR).

Main Methods:

  • The study employed the Modified Ant Lion Optimization (MALO) algorithm to optimize the weights and hidden layers of an Artificial Neural Network (ANN).
  • Input parameters including grit size, pulse-on/off duration, and current were systematically analyzed.
  • The MALO-ANN model was applied to predict and optimize MRR and SR in the EDDG process.

Main Results:

  • The MALO-ANN technique demonstrated significant improvements in the parametric optimization of EDDG.
  • The optimized model achieved high accuracy, with an absolute error interval for MRR and SR ranging from 1.03% to 4.49%.
  • A convergence rate of 89% was achieved, indicating enhanced efficiency and accuracy in EDDG operations.

Conclusions:

  • The MALO-ANN approach offers a superior method for optimizing EDDG processes compared to conventional ANN models.
  • This technique shows great potential for improving the efficiency and precision of manufacturing durable, electrically conductive materials.
  • The study validates the effectiveness of MALO-ANN in achieving optimal material removal rate and surface roughness.