Multi-Physics Sensing and Real-time Quality Control in Metal Additive Manufacturing

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Virginia Tech

Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from deformation, delamination, dimensional error, and porosities. One of the most significant issues is poor printing accuracy, especially for small features such as turbine blade tips. The main reason for the shape inaccuracy is the heat accumulation caused by using constant laser power regardless of the shape variations. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives. For example, the lack of understanding of multi-physical melt pool dynamics fundamentally impedes the research progress. The scarcity of a customizable laser powder bed platform further restricts the possibility of testing the improvement strategies. Additionally, most state-of-the-art quality inspection techniques suitable for laser powder bed fusion are costly in economic and time aspects. Lastly, the rapid and chaotic printing process is hard to monitor and control.
This dissertation proposes a complete research scheme including a fundamental physics study, process signature and quality correlation, smart additive manufacturing platform development, high-performance sensor development, and a robust real-time closed-loop control system design to address all these issues. The entire research flow of this dissertation is as follows:

  1. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the melt pool dynamics, keyhole, porosity formation, vapor plume, and thermal evolution in Ti-64 and 410 stainless steel. The study discovers a strong correlation between the thermal and X-ray data, enabling the feasibility of using relatively cheap IR cameras to predict features that can only be captured using costly synchrotron X-ray imaging. Such correlation is essential for thermal-based melt pool control.
  2. A highly customizable smart laser powder bed fusion platform is designed and constructed. This platform integrates numerous sensors, including but not limited to co-axial cameras, IR cameras, oxygen sensors, photodiodes, etc. The platform allows in-process parameter adjusting, which opens the boundary to test various control theories based on multi-sensing and data correlations.
  3. To fulfill the quality assessment need of laser powder bed fusion, this dissertation proposes a novel structured light 3D scanner with extraordinary high spatial resolution. The spatial resolution and accuracy are improved by establishing hardware selection criteria, integrating the proper hardware, designing a scale-appropriate calibration target, and developing noise reduction procedures during calibration. Compared to the commercial scanner, the proposed scanner improves the spatial resolution from 48 µm to 5 µm and the accuracy from 108.5 µm to 0.5 µm.
  4. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on the monitoring result from a novel thermal sensor. The desired printing temperature is found by correlating the laser power, the dimensional accuracy, and the thermal signatures from a set of thin-wall structure printing trails. An innovative high-speed data acquisition and communication software can operate the whole system with a graphic user interface. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in small feature printing accuracy has been observed.
Additive manufacturing, smart manufacturing, sensing, data analytics, in-situ control, 3D scanning, X-ray, IR imaging