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

Manufacturing of Bioinspired SS316L-Based Multimaterials: Processing, Mechanical Properties and Modeling.

Micromachines·2026
Same author

Metals are key to the global economy - but three challenges threaten supply chains.

Nature·2025
Same author

Metal-Free Catalytic <i>N</i>-Methylation of <i>N</i>H-Sulfoximines Using CO<sub>2</sub>.

Organic letters·2025
Same author

Recent Progress in Computational and Data Sciences for Additive Manufacturing.

Materials (Basel, Switzerland)·2025
Same author

Bioreducible Amphiphilic Hyperbranched Polymer-Drug Conjugate for Intracellular Drug Delivery.

Bioconjugate chemistry·2024
Same author

Vapor Pressure versus Temperature Relations of Common Elements.

Materials (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jun 28, 2025

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

914

Mitigation of Gas Porosity in Additive Manufacturing Using Experimental Data Analysis and Mechanistic Modeling.

Satyaki Sinha1, Tuhin Mukherjee1

  • 1Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.

Materials (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

A new dimensionless gas porosity index accurately predicts and mitigates pores in laser powder bed fusion additive manufacturing. This tool helps select process variables to avoid costly post-processing for alloys like stainless steel 316 and Ti-6Al-4V.

Keywords:
3D printingAlSi10MgInconel 718Stokes lawTi-6Al-4Vbuoyancyconvective flowgas porosity indexlaser powder bed fusionstainless steel 316

More Related Videos

Negative Additive Manufacturing of Complex Shaped Boron Carbides
06:45

Negative Additive Manufacturing of Complex Shaped Boron Carbides

Published on: September 18, 2018

8.6K
Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture
08:00

Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture

Published on: September 29, 2023

2.4K

Related Experiment Videos

Last Updated: Jun 28, 2025

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

914
Negative Additive Manufacturing of Complex Shaped Boron Carbides
06:45

Negative Additive Manufacturing of Complex Shaped Boron Carbides

Published on: September 18, 2018

8.6K
Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture
08:00

Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture

Published on: September 29, 2023

2.4K

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Computational Materials Science

Background:

  • Gas porosity in laser powder bed fusion (LPBF) components degrades mechanical properties.
  • Traditional methods to reduce porosity are time-consuming and costly.

Purpose of the Study:

  • Develop an easy-to-use, verifiable index to predict and mitigate gas porosity.
  • Reduce the need for empirical testing in LPBF.

Main Methods:

  • Combined mechanistic modeling with experimental data analysis.
  • Developed a dimensionless gas porosity index.
  • Validated the index against experimental data for multiple alloys.

Main Results:

  • The gas porosity index accurately predicts porosity occurrence with 92% accuracy for stainless steel 316, Ti-6Al-4V, Inconel 718, and AlSi10Mg.
  • Higher index values correlate with increased pore amounts.
  • AlSi10Mg is most susceptible to gas porosity, with index values 5-10 times higher than other alloys.

Conclusions:

  • The proposed gas porosity index effectively mitigates pore formation in LPBF.
  • A gas porosity map aids in selecting optimal process variables.
  • This approach reduces the reliance on empirical testing for defect prevention.