scholarly journals A data-driven approach for predicting printability in metal additive manufacturing processes

2020 ◽  
Vol 31 (7) ◽  
pp. 1769-1781 ◽  
Author(s):  
William Mycroft ◽  
Mordechai Katzman ◽  
Samuel Tammas-Williams ◽  
Everth Hernandez-Nava ◽  
George Panoutsos ◽  
...  
2022 ◽  
Vol 62 ◽  
pp. 145-163
Author(s):  
Shenghan Guo ◽  
Mohit Agarwal ◽  
Clayton Cooper ◽  
Qi Tian ◽  
Robert X. Gao ◽  
...  

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 4929
Author(s):  
Teng Yang ◽  
Sangram Mazumder ◽  
Yuqi Jin ◽  
Brian Squires ◽  
Mathew Sofield ◽  
...  

Additive manufacturing technologies based on metal are evolving into an essential advanced manufacturing tool for constructing prototypes and parts that can lead to complex structures, dissimilar metal-based structures that cannot be constructed using conventional metallurgical techniques. Unlike traditional manufacturing processes, the metal AM processes are unreliable due to variable process parameters and a lack of conventionally acceptable evaluation methods. A thorough understanding of various diagnostic techniques is essential to improve the quality of additively manufactured products and provide reliable feedback on the manufacturing processes for improving the quality of the products. This review summarizes and discusses various ex-situ inspections and in-situ monitoring methods, including electron-based methods, thermal methods, acoustic methods, laser breakdown, and mechanical methods, for metal additive manufacturing.


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