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- W2892439871 abstract "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." @default.
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- W2892439871 date "2018-06-18" @default.
- W2892439871 modified "2023-10-10" @default.
- W2892439871 title "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning" @default.
- W2892439871 doi "https://doi.org/10.1115/msec2018-6501" @default.
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