To realize high quality, additively manufactured parts, real-time process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-time monitoring system is developed to monitor the health condition of a 3D printer and FDM processes using multiple sensors. A predictive model is built by random forests (RFs). Experimental results have shown that the predictive model is capable of predicting the surface roughness of a printed part with very high accuracy.
Surface Roughness Prediction in Additive Manufacturing Using Machine Learning
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Wu, D, Wei, Y, & Terpenny, J. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference. Volume 3: Manufacturing Equipment and Systems. College Station, Texas, USA. June 18–22, 2018. V003T02A018. ASME. https://doi.org/10.1115/MSEC2018-6501
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