Surface roughness prediction in fused deposition modelling by neural networks

2013 ◽  
Vol 67 (9-12) ◽  
pp. 2727-2742 ◽  
Author(s):  
A. Boschetto ◽  
V. Giordano ◽  
F. Veniali
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fused Deposition Modelling ◽  
Surface Roughness Prediction ◽  
Fused Deposition ◽  
Roughness Prediction

Surface Roughness Prediction in Additive Manufacturing Using Machine Learning

2018 ◽  
Author(s):  
Dazhong Wu ◽  
Yupeng Wei ◽  
Janis Terpenny
Keyword(s):  
Surface Roughness ◽  
Additive Manufacturing ◽  
Predictive Model ◽  
Real Time ◽  
Fused Deposition Modeling ◽  
Real Time Monitoring ◽  
Modeling Tools ◽  
Surface Roughness Prediction ◽  
Fused Deposition ◽  
Roughness Prediction

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.

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