TY - GEN
T1 - Acoustic-Based In-Situ Monitoring of Additive Manufacturing Fabrication
T2 - 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
AU - Fatoba, Olawale Samuel
AU - Jen, Tien Chien
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In-situ monitoring involves assessing the state of an additively built item while it is being used to look for cracks or pores. The capacity to monitor and regulate the additive manufacturing (AM) process in real-time for in-process measurements determines the quality of the finished products. The manufacturing sector has not yet experienced a significant amount of AM's effects. This is because there are a number of technological obstacles to be solved first, such as a lack of process comprehension and in-situ process monitoring and control, particularly in metal AM systems. Since they can have a significant impact on the printed microstructure and the functionality of subsequent products, AM processing parameters can be challenging to fine-tune. Building a process-structure-property-performance (PSPP) relationship for AM using conventional numerical and analytical models is a challenging task. Using acoustic emission (AE), it may be possible to identify and correct major flaws in additively manufactured parts before they cause further damage. This could reduce material waste, improve quality, and reduce the need for repair or rework.
AB - In-situ monitoring involves assessing the state of an additively built item while it is being used to look for cracks or pores. The capacity to monitor and regulate the additive manufacturing (AM) process in real-time for in-process measurements determines the quality of the finished products. The manufacturing sector has not yet experienced a significant amount of AM's effects. This is because there are a number of technological obstacles to be solved first, such as a lack of process comprehension and in-situ process monitoring and control, particularly in metal AM systems. Since they can have a significant impact on the printed microstructure and the functionality of subsequent products, AM processing parameters can be challenging to fine-tune. Building a process-structure-property-performance (PSPP) relationship for AM using conventional numerical and analytical models is a challenging task. Using acoustic emission (AE), it may be possible to identify and correct major flaws in additively manufactured parts before they cause further damage. This could reduce material waste, improve quality, and reduce the need for repair or rework.
KW - 3D printer
KW - In-situ monitoring
KW - acoustic emission (AE)
KW - additive manufacturing
KW - defects
UR - https://www.scopus.com/pages/publications/85168773532
U2 - 10.1109/ICMIMT59138.2023.10200667
DO - 10.1109/ICMIMT59138.2023.10200667
M3 - Conference contribution
AN - SCOPUS:85168773532
T3 - 2023 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
SP - 250
EP - 256
BT - 2023 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 May 2023 through 28 May 2023
ER -