Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Author:

Kozjek Dominik,Porter Conor,Carter III Fred M.,Mogonye Jon-Erik,Cao Jian

Funder

Argonne National Laboratory

Army Research Laboratory

U.S. Department of Energy

Government of South Australia

DEVCOM Army Research Laboratory

Office of Science

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Reference21 articles.

1. A comprehensive review on laser powder bed fusion of steels: processing, microstructure, defects and control methods, mechanical properties, current challenges and future trends;Narasimharaju;J Manuf Process,2022

2. Control of selective laser melting processes: existing efforts, challenges, and future opportunities;Al-Saadi,2021

3. Data-driven prediction of next-layer melt pool temperatures in laser powder bed fusion based on co-axial high-resolution planck thermometry measurements;Kozjek;J Manuf Process,2022

4. A meltpool prediction based scan strategy for powder bed fusion additive manufacturing;Yeung;Addit Manuf,2020

5. A residual heat compensation based scan strategy for powder bed fusion additive manufacturing;Yeung;Manuf Lett,2020

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