(109c) Computational Fluid Dynamics-Based in-Situ Sensor Analytics of Direct Metal Laser Sintering Process Using Machine Learning
AIChE Annual Meeting
2020 Virtual AIChE Annual Meeting
Computing and Systems Technology Division
Big-Data for Process Applications
Monday, November 16, 2020 - 8:30am to 8:45am
To address the aforementioned problem, we develop an automated data-flow that integrates the simulation model and machine learning network for real-time process simulation and sensor data analytics. In particular, the simulation models, such as thermal history analysis, are tailored to mimic the manufacturing platform characteristic and to incorporate potential process disturbances. From the simulation result, the heat map images are reconstructed with corresponding classified disturbances categories, according to the industrial sensor technologies. Advanced machine learning algorithms like convolutional neural networks (CNN) are designed and trained with transfer learning to achieve a robust yet computationally acceptable in-situ process analysis.
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