TY - JOUR
T1 - In situ process monitoring using acoustic emission and laser scanning techniques based on machine learning models
AU - Xu, Ke
AU - Lyu, Jiaqi
AU - Manoochehri, Souran
N1 - Publisher Copyright:
© 2022 The Society of Manufacturing Engineers
PY - 2022/12
Y1 - 2022/12
N2 - Fused Filament Fabrication (FFF) is a widely used additive manufacturing method for obtaining prototypes with complex structures. On the other hand, Commercial FFF machines have limits on process dependability and product quality. Numerous parameters can impact the printing process's accuracy and stability. This study created a real-time monitoring system based on acoustic emission (AE) and laser scanning technology to monitor the warpage defect in fabricated parts throughout the printing process. The AE signal generated during the printing process indicates variations in vibration caused by changes in the part's internal tension. It is capable of precisely displaying real-time part characteristics. After amplifying and filtering the AE signal, the frequency and time domain characteristics are retrieved and trained in the machine learning model. Laser scanning depth image data could be utilized to quantify the dimensional changes of the part. After noise reduction and processing, the point cloud data collected by the laser scanner can represent the specific percentage of warpage. The experimental results demonstrate that the machine learning models trained by AE sensory data can accurately identify if warpage of various sizes is formed with great accuracy in real time. This study examines the support vector machine, naïve bayes classifier, and decision tree data-driven machine learning models.
AB - Fused Filament Fabrication (FFF) is a widely used additive manufacturing method for obtaining prototypes with complex structures. On the other hand, Commercial FFF machines have limits on process dependability and product quality. Numerous parameters can impact the printing process's accuracy and stability. This study created a real-time monitoring system based on acoustic emission (AE) and laser scanning technology to monitor the warpage defect in fabricated parts throughout the printing process. The AE signal generated during the printing process indicates variations in vibration caused by changes in the part's internal tension. It is capable of precisely displaying real-time part characteristics. After amplifying and filtering the AE signal, the frequency and time domain characteristics are retrieved and trained in the machine learning model. Laser scanning depth image data could be utilized to quantify the dimensional changes of the part. After noise reduction and processing, the point cloud data collected by the laser scanner can represent the specific percentage of warpage. The experimental results demonstrate that the machine learning models trained by AE sensory data can accurately identify if warpage of various sizes is formed with great accuracy in real time. This study examines the support vector machine, naïve bayes classifier, and decision tree data-driven machine learning models.
KW - Acoustic emission (AE)
KW - Additive manufacturing (AM)
KW - Fused filament fabrication (FFF)
KW - Naïve bayes classifier (NBC)
KW - SVM (support vector machine)
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U2 - 10.1016/j.jmapro.2022.10.002
DO - 10.1016/j.jmapro.2022.10.002
M3 - Article
AN - SCOPUS:85140141626
SN - 1526-6125
VL - 84
SP - 357
EP - 374
JO - Journal of Manufacturing Processes
JF - Journal of Manufacturing Processes
ER -