Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing-Reference-Cited by-同舟云学术

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Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Published:2022-09 Issue: Volume:221 Page:110919
ISSN:0264-1275
Container-title:Materials & Design
language:en
Short-container-title:Materials & Design

Author:

Gaikwad Aniruddha,Williams Richard J.,de Winton Harry,Bevans Benjamin D.,Smoqi Ziyad,Rao Prahalada,Hooper Paul A.

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference84 articles.

1. The metallurgy and processing science of metal additive manufacturing;Sames;Int. Mater. Rev.,2016

2. Additive manufacturing: technology, applications and research needs;Guo;Frontiers of Mech. Eng.,2013

3. Process defects and in situmonitoring methods in metal powder bed fusion: a review;Grasso;Meas. Sci. Technol.,2017

4. A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes;Mani;Int. J. Prod. Res.,2017

5. A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing;Tapia;J. Manuf. Sci. Eng.,2014

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1. A novel data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing;Computers in Industry;2024-12

2. Photodiode-based porosity prediction in laser powder bed fusion considering inter-hatch and inter-layer effects;Journal of Materials Processing Technology;2024-11

3. Evaluating and correlating multimodal process dynamics, microstructure features, and mechanical properties in laser powder bed fusion;Journal of Manufacturing Processes;2024-10

4. Melt pool control-assisted additive manufacturing of thin-walled parts;International Journal of Mechanical Sciences;2024-10

5. Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning;Materials & Design;2024-09

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