Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

High Piezoelectricity and Temperature Stability via Stabilized Polar Distortion.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Deciphering Core Geometry for the Rational Design of Copper(I) Iodide Cluster Scintillators Toward Computed Tomography Imaging.

Angewandte Chemie (International ed. in English)·2026

Same author

Childhood Speech Impairment and Dementia Risks Among U.S. Older Adults.

International journal of language & communication disorders·2026

Same author

Integrative Multiomics Analysis Identifies a Novel Gene Signature That Predicts Chemotherapy Resistance and Poor Survival in Osteosarcoma.

Human mutation·2026

Same author

Ecological signature on the epidemiological dynamics of severe fever with thrombocytopenia syndrome.

PLoS neglected tropical diseases·2026

Same author

Slfn4-mediated Stat3 signaling promotes suppressive bone marrow monocytes in a murine second-hit sepsis model.

Molecular medicine (Cambridge, Mass.)·2026

Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026

Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026

Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026

Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026

Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026

Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

Materials (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 30, 2025

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method.

Yingyan Chen^1,2, Hongze Wang^1,2, Yi Wu^1,2

¹State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, China.

Materials (Basel, Switzerland)

|November 13, 2020

Summary

This summary is machine-generated.

This study introduces a supervised machine learning (ML) method to intelligently detect defects and predict material printability in selective laser melting (SLM). This data-driven approach significantly improves the efficiency of finding optimal process parameters for SLM fabrication.

Keywords:

defect detection machine learning printability prediction selective laser melting

More Related Videos

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Nov 30, 2025

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Additive Manufacturing
Materials Science
Machine Learning

Background:

Selective laser melting (SLM) part quality is highly sensitive to process parameters.
Current methods for determining optimal SLM parameters are time-consuming and subjective.
Need for efficient and objective methods to define the SLM process parameter window.

Purpose of the Study:

To develop a supervised machine learning (ML) model for intelligent defect detection and printability prediction in SLM.
To classify printed tracks based on surface morphology and characteristics.
To identify defect-free process parameter combinations efficiently.

Main Methods:

Utilized a supervised machine learning approach.
Classified printed tracks into five types based on surface morphology.
Employed four quantitative indicators of track quality as input variables for the ML model.

Main Results:

Developed a data-driven ML model capable of predicting material printability in SLM.
Successfully classified track defects based on quantitative surface characteristics.
Demonstrated the model's ability to identify defect-free process parameter combinations.

Conclusions:

The proposed ML method significantly enhances the efficiency of searching for the SLM process parameter window.
This approach offers potential for application in automated and unmanned manufacturing environments.
Provides an objective and rapid alternative to traditional metallographic examination for SLM quality control.