Session: 03-10 Metals: Residual Stress Studies and Process Monitoring

Paper Number: 96740

96740 - A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing 

Data fusion techniques aim to improve inference results or decision making by combining multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate data at different scales and sampling rates during a build process. Data analysis based on an individual data source may not be accurate enough to monitor the process state, or be limited by the relevancy of the observations, signal-to-noise ratio, or other measurement uncertainties. This work proposes a decision-level, multimodal data fusion method that combines multiple in-situ AM monitoring data sources to improve overall process monitoring performance. A powder bed fusion experiment that was conducted to create overhang surfaces throughout a 3D part is used to illustrate and validate the proposed method. The overhang features are designed with different shapes and angles, and at various build locations. They are formed using constant laser power and scan speed. A high-frequency coaxial melt pool imaging system and a low-frequency layerwise staring camera are the two in-situ monitoring data sources used in the case study. The Naïve Bayes and k-nearest neighbors algorithms are first applied to each data set for overhang feature detection. Then both hard voting and soft voting are adopted in fusing the classification outcomes. The results show that while none of the individual classifiers are perfect in detecting overhang features, the fused decision of the 324 test samples achieved 100 % detection accuracy.

Presenting Author: Yan Lu NIST

Presenting Author Biography: Dr. Yan Lu is a member of the System Integration Division. Her research interests at NIST include smart manufacturing system reference architecture design, production operation and optimization, and additive manufacturing modeling and design optimization. <br/>Before joining NIST, Dr. Lu was the head of Grid Automation and Production Operation and Optimization Research Group at Siemens Corporation, Corporate Technology. With Siemens, she has led and successfully delivered tens of million dollars of corporate funded and government funded research projects in the areas of survivable control systems, energy automation and building energy management systems. She has published more than 30 peer reviewed journal and conference papers and was granted more than 10 patents in industry and building automation technology. Dr. Lu also worked for Seagate Research Center for two years on developing hard disk drive servo control.

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