Search research articles

Related Concept Videos

Gas Chromatography: Types of Detectors-II

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...

You might also read

Related Articles

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

Sort by

Same author

Evaluation of the implementation of a mobility plan in a geriatric clinic in Switzerland - a quality improvement study.

BMC geriatrics·2026

Same author

Carbon-14 release and speciation during corrosion of irradiated steel under radioactive waste disposal conditions.

The Science of the total environment·2021

Same author

Processes leading to reduced and oxidised carbon compounds during corrosion of zero-valent iron in alkaline anoxic conditions.

Chemosphere·2020

Same author

An Inflammatory Nucleus Pulposus Tissue Culture Model to Test Molecular Regenerative Therapies: Validation with Epigallocatechin 3-Gallate.

International journal of molecular sciences·2016

Same author

Strontium uptake by cementitious materials.

Environmental science & technology·2008

Same author

EXAFS study of Sn(IV) immobilization by hardened cement paste and calcium silicate hydrates.

Environmental science & technology·2003

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

Search research articles

Related Experiment Video

Updated: Jul 21, 2026

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Published on: December 29, 2015

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring.

Imran Ali Khan¹, Hannes Birkhofer¹, Dominik Kunz²

¹Airbus Endowed Chair for Integrative Simulation and Engineering of Materials and Processes (ISEMP), University of Bremen, Am Fallturm 1, 28359 Bremen, Germany.

Materials (Basel, Switzerland)

|October 14, 2023

Summary

This study developed a machine learning model using optical tomography to detect defects in metal additive manufacturing (AM) with 99.98% accuracy. The model identifies anomalies layer-by-layer, correlating with defects like gas pores and lack of fusion.

Keywords:

computerized tomography gas pores lack of fusion laser powder bed fusion machine learning optical tomography process monitoring quality inspection random forest

More Related Videos

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

Related Experiment Videos

Last Updated: Jul 21, 2026

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Published on: December 29, 2015

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

Area of Science:

Manufacturing Engineering
Materials Science
Computer Science

Background:

Metal additive manufacturing (AM) is revolutionizing industries with its ability to create complex, high-value components.
Quality assurance in AM is challenging due to geometry-dependent process conditions and layer-by-layer construction.
In-process monitoring is crucial for cost reduction and defect detection in metal AM.

Purpose of the Study:

To develop machine learning (ML) models for enhanced in-process monitoring and control in metal AM.
To utilize layer-wise optical tomography (OT) imaging for detecting anomalies indicative of defects.
To validate ML-based anomaly detection against computerized tomography (CT) data for critical defect identification.

Main Methods:

Layer-wise in-process sensing using optical tomography (OT) imaging as the primary data source.
Application of a Random Forest Classifier ML algorithm for anomaly segmentation in OT images.
Validation of detected anomalies by correlating with defects identified through computerized tomography (CT) data.
3D defect mapping using affine transformation and performance evaluation with metrics like accuracy, precision, recall, and IOU.

Main Results:

An anomaly detection model achieved a best detection accuracy of 99.98%.
The model successfully correlated 79.40% of defects identified by CT data with anomalies detected from OT data.
Gas pores and lack of fusion defects were the primary defects analyzed and detected.

Conclusions:

Layer-wise monitoring with ML-enhanced OT imaging is a viable strategy for in-process quality control in metal AM.
The developed anomaly detection system demonstrates high accuracy and potential for reducing defects and costs in AM.
Integration of OT imaging and ML offers a promising pathway for real-time defect identification and process control in additive manufacturing.