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Real-Time Composition Monitoring Using Support Vector Regression of Laser-Induced Plasma for Laser Additive Manufacturing | IEEE Journals & Magazine | IEEE Xplore

Real-Time Composition Monitoring Using Support Vector Regression of Laser-Induced Plasma for Laser Additive Manufacturing


Abstract:

Laser additive manufacturing has gained widespread adoption in recent years. However, process diagnosis and process control lag behind the progresses of other key technol...Show More

Abstract:

Laser additive manufacturing has gained widespread adoption in recent years. However, process diagnosis and process control lag behind the progresses of other key technologies, which make the product quality control a challenging problem. This work proposes an operating parameter conditioned support vector regression (SVR) method that uses processing parameter conditioned kernel function to achieve a processing parameter independent in-situ composition prediction. Two different features of laser-induced plasma, spectral line-intensity-ratio, and both spectral line-intensity-ratio and spectral integrated intensity were used to train the SVR. Composition measurements using a calibration curve method, partial least square regression, and artificial neural networks are also performed for comparison. The results show that the SVR with both spectral line-intensity-ratio and spectral integrated intensity as inputs has the best performance due to linearly separable point clusters in the high-dimensional space. Laser power independent composition prediction is achieved and real-time composition predictions are validated. It is proved that the operating parameter conditioned SVR provides a more accurate, a more universal, and an operating parameter independent prediction. Moreover, operating parameter conditioned SVR provides a potential data-driven-based approach for real-time composition monitoring of the laser additive manufacturing process.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 64, Issue: 1, January 2017)
Page(s): 633 - 642
Date of Publication: 12 September 2016

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