Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing

Author:

Gunasegaram D.R.,Barnard A.S.,Matthews M.J.,Jared B.H.,Andreaco A.M.,Bartsch K.,Murphy A.B.

Funder

National Nuclear Security Administration

Australian Government

U.S. Department of Energy

Commonwealth Scientific and Industrial Research Organisation

Publisher

Elsevier BV

Reference224 articles.

1. Defects in Metal Additive Manufacturing Processes;Brennan,2020

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

3. Additive manufacturing processes for metals and effects of defects on mechanical strength: a review;Bellini;Procedia Struct. Integr.,2021

4. A computationally efficient thermo-mechanical model for wire arc additive manufacturing;Yang;Addit. Manuf.,2021

5. Hot isostatic pressing in metal additive manufacturing: X-ray tomography reveals details of pore closure;du Plessis;Addit. Manuf.,2020

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