By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.

By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.