A surface roughness based visual and analysis system for surface quality improvement in fused deposition modeling rapid prototype machine

Abstract

In rapid prototyping (RP), part deposition orientation and surface finish are two significant concerns, but they are contradicting with each other. In model building in RP, a concession is commonly made between these two features to get good quality surface roughness at a short build time. A concession among these two contradicting concerns can be achieved via an adaptive slicing method; on the other hand, selection of an appropriate part deposition orientation will further provide an improved solution. In this thesis, an effort towards determining an optimum part deposition orientation and adaptive slicing method for Fused Deposition Modeling (FDM) process for enhancing part surface finish, and hence, reducing build time (repeating process in RP cycle) is proposed. The quality of the surface roughness is determined by using visual and analysis. This Surface Roughness Based Visual and Analysis (SRVA) system is obtained based on the calculation of surface roughness (Ra). In this present work, the Region Based Adaptive Slicing method is applied in building the model in FDM. The proposed methodology allows the RP user to observe and analyze the prototype model before fabricating the prototype model in the FDM. A program based on fuzzy logic is also used to verify the input and output parameters obtained from the proposed method. The developed SRVA system has successfully improved the surface finish and minimized the build time in fabricating the prototype model in FDM. The result showed that increasing part deposition orientation would decrease the Ra value of the model. For 00 and 900 part deposition orientation, the Ra from measurement are closed to the Ra output from fuzzy logic with percentage differences 1.78% and 1.52% respectively. Therefore, the Ra values calculated from the SRVA system are acceptable for these orientations. However, for 450 part deposition orientation, it is 2.26% higher than the Ra output from fuzzy logic because during fabrication process, the surrounding support model affects the surface finish of the prototype model. However, this value is also acceptable because the effect of surrounding support model to the surface finish has not been the focus of the present work. The result also shows that the adaptive slicing method has improved the surface roughness of the prototype model. The inspected Ra obtained by this method is 1.22% lower than that obtained without adaptive slicing method, but 0.56% higher than that obtained by fuzzy logic. This result is obtained without the necessity to repeatedly fabricate the model or piecework in FDM for good quality surface roughness as the proposed method in this thesis successfully managed to optimize RP cycle; hence the build time in RP is reduced.