TY - GEN
T1 - On-Line Condition Monitoring of Additive Manufacturing Based on Friction Induced Acoustic Emissions
AU - Li, Zhen
AU - Zou, Xinfeng
AU - Zhang, Xianzhi
AU - Gu, Fengshou
AU - Ball, Andrew D.
N1 - Funding Information:
Acknowledgement. We are very grateful for the AE experimental equipment support from Qingcheng Acoustic Emission Research (Guangzhou) Co., Ltd. And the valuable suggestions from their engineers are appreciated. The study is also supported by Characteristic Innovation Program of Universities in Guangdong Province: “Research on key technologies of the error prediction and quality mapping of 3D printing based on multi-parameter information coupling (2022KTSCX200)”.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/3/4
Y1 - 2023/3/4
N2 - Layer-by-layer superpositions are the primary way of additive manufacturing (AM). The layer can be formed by point-forming, line-forming, or surface forming. Each layer is integrated with the other to complete construction. The quality of each layer has an essential impact on the quality of the whole AM model. In particular, the excessive surface bulges in the construction process will affect the coupling quality of the next layer, which leads to the failure of part construction. However, due to the different construction parameters, materials, and equipment, the quality of print layer forming is not very uniform but changes greatly. This paper proposes a new method to monitor changes based on acoustic emission analysis. In this work, the main concern is detecting the bulge at different layers. The material bulges on the completed layer will increase the friction effects between the nozzle and the layer, forming random impulsive dynamic excitations and producing more significant acoustic emission responses. The time-frequency analysis shows that the AE waveform due to the bulges exhibit wideband with high amplitudes. It means moving waveform parameters such as moving root mean square (MRMS) value, energy, and so on can be a useful indicator to show the behaviors of the friction effect. Experimental studies show that the proposed MRMS values give accurate indications of the simulated part bulges at different layers, allowing the printing quality to be assessed based on the number of occurrences of the high MRMS value at each layer. In addition, this real-time information will provide feedbacks to terminate or adjust the printing process to ensure part quality.
AB - Layer-by-layer superpositions are the primary way of additive manufacturing (AM). The layer can be formed by point-forming, line-forming, or surface forming. Each layer is integrated with the other to complete construction. The quality of each layer has an essential impact on the quality of the whole AM model. In particular, the excessive surface bulges in the construction process will affect the coupling quality of the next layer, which leads to the failure of part construction. However, due to the different construction parameters, materials, and equipment, the quality of print layer forming is not very uniform but changes greatly. This paper proposes a new method to monitor changes based on acoustic emission analysis. In this work, the main concern is detecting the bulge at different layers. The material bulges on the completed layer will increase the friction effects between the nozzle and the layer, forming random impulsive dynamic excitations and producing more significant acoustic emission responses. The time-frequency analysis shows that the AE waveform due to the bulges exhibit wideband with high amplitudes. It means moving waveform parameters such as moving root mean square (MRMS) value, energy, and so on can be a useful indicator to show the behaviors of the friction effect. Experimental studies show that the proposed MRMS values give accurate indications of the simulated part bulges at different layers, allowing the printing quality to be assessed based on the number of occurrences of the high MRMS value at each layer. In addition, this real-time information will provide feedbacks to terminate or adjust the printing process to ensure part quality.
KW - Acoustic emission (AE)
KW - Additive manufactory (AM)
KW - Moving root mean square
KW - Signal processing
KW - Surface friction
UR - http://www.scopus.com/inward/record.url?scp=85151138075&partnerID=8YFLogxK
UR - https://link.springer.com/book/10.1007/978-3-031-26193-0
U2 - 10.1007/978-3-031-26193-0_22
DO - 10.1007/978-3-031-26193-0_22
M3 - Conference contribution
AN - SCOPUS:85151138075
SN - 9783031261923
SN - 9783031261954
VL - 129
T3 - Mechanisms and Machine Science
SP - 255
EP - 267
BT - Proceedings of TEPEN 2022
A2 - Zhang, Hao
A2 - Ji, Yongjian
A2 - Liu, Tongtong
A2 - Sun, Xiuquan
A2 - Ball, Andrew David
PB - Springer, Cham
T2 - International Conference of The Efficiency and Performance Engineering Network 2022
Y2 - 18 August 2022 through 21 August 2022
ER -