Monitoring and controlling 3D printing or Additive Manufacturing (AM) processes play a critical role in enabling the production of quality parts. The capability of in-situ measuring melt pool variations is important for assessing the AM quality. Actually, the volume of streaming data (e.g., melt pool images and temperatures) collected from in-situ metrology is too large to be timely processed by the existing architecture. To improve the computation efficiency, this research presents an intelligent in-situ metrology architecture and improves the automatic virtual metrology (AVM) system to assure the AM quality.
While adapting to various machine configurations, metal powder and process parameters, determining the thresholds of the primary control loops (System 1) of an AM process is challenging. This study also shows how to distinguish the categories of melt-pool images, derive re-melting parameters, and evaluate the control thresholds of System 1 using a secondary tuning loop (System 2) evolved from the convolutional neural networks and long short-term memory models.
Comprising the particle exhausting, powder coating, and laser printing of control loops of a laser powder bed fusion machine, the case studies validate that the intelligent in-situ metrology architecture is efficient and accurate in quality evaluation. The melt-pool image-based System 2 provides reliable thresholds of System 1 for AM process control. In addition, qualities including roughness, density, and tensile of the build parts indicate that the results are desirable for the AM industry.

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