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 Experiment Video

Updated: Jan 7, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

719

Machine Learning-Driven Multi-Objective Optimization of Bead Geometry and Energy Efficiency in Laser-Arc Hybrid

Chunyang Xia1,2, Kui Zeng3, Jiawei Ning3

  • 1College of Engineering, Ocean University of China, Qingdao 266100, China.

Materials (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

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

Scale-Aware Prompting With Optimal Transport for Remote Sensing Image Captioning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Monitoring and Control of the Direct Energy Deposition (DED) Additive Manufacturing Process Using Deep Learning Techniques: A Review.

Materials (Basel, Switzerland)·2026
Same author

An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning.

Biomimetics (Basel, Switzerland)·2025
Same author

Ready-to-Use OECT Biosensor toward Rapid and Real-Time Protein Detection in Complex Biological Environments.

ACS sensors·2025
Same author

Characterization of RufT Thioesterase Domain Reveals Insights into Rufomycin Cyclization and the Biosynthetic Origin of Rufomyazine.

ACS chemical biology·2025
Same author

The stabilization mechanism of the pea protein and rutin complex at the gas/liquid interface and its application in low-fat cream.

Food chemistry: X·2025
This summary is machine-generated.

This study introduces a machine learning framework for laser-arc hybrid additive manufacturing (LAHAM) to predict and optimize bead geometry and energy efficiency. The developed models effectively balance deposition accuracy with energy utilization for improved manufacturing processes.

Area of Science:

  • Materials Science and Engineering
  • Manufacturing Technology
  • Computational Science

Background:

  • Laser-arc hybrid additive manufacturing (LAHAM) presents challenges in predicting and controlling bead geometry and energy consumption due to complex nonlinear process interactions.
  • Accurate prediction and control are crucial for optimizing deposition accuracy and energy utilization in LAHAM.

Purpose of the Study:

  • To develop a machine learning (ML) framework for predicting bead width, height, and deposition volume per unit energy (DVUE) in LAHAM.
  • To optimize process parameters for enhanced deposition accuracy and energy efficiency.

Main Methods:

  • Experimental data was used to train and evaluate multiple regression models, including Support Vector Regression, Gaussian Process Regression (GPR), Neural Networks, and XGBoost.
Keywords:
additive manufacturingbead geometryenergy efficiencylaser–arc hybridmulti-objective optimization

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K
Laser Micromachining for Polymer Surface Topography Design
05:49

Laser Micromachining for Polymer Surface Topography Design

Published on: September 19, 2025

402

Related Experiment Videos

Last Updated: Jan 7, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

719
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K
Laser Micromachining for Polymer Surface Topography Design
05:49

Laser Micromachining for Polymer Surface Topography Design

Published on: September 19, 2025

402
  • Gaussian Process Regression (GPR) was selected for its superior performance in capturing nonlinear relationships and further optimized using Bayesian Optimization and Particle Swarm Optimization.
  • Optimized GPR models were integrated with the NSGA-II multi-objective optimization algorithm.

    • Main Results:

    • The proposed ML framework successfully predicted bead geometry and DVUE in LAHAM.
    • Optimized GPR models achieved superior performance in capturing complex nonlinear process dynamics.
    • The NSGA-II algorithm identified Pareto-optimal process parameters, balancing geometric deviations and DVUE.

    Conclusions:

    • The developed ML framework provides a reliable and intelligent strategy for optimizing process parameters in hybrid additive manufacturing.
    • The approach effectively balances deposition accuracy and energy utilization rate.
    • This research contributes to advancing the control and efficiency of LAHAM processes.