Abstract
Generally, components tend to fail due to cyclic loading; therefore study of fatigue life of products is a critical issue. The purpose of this study is to develop a predictive model relating the process parameters with fatigue life of fused filament fabrication (FFF) technology-based 3D-printed parts. Test specimens are printed using a desktop FFF 3D printer to suit a laboratory-based fatigue testing machine. Response surface methodology-based central composite design method was used for the design of experiments to obtain a regression model for prediction of response. Lastly, genetic algorithm solver was used to optimise the predictive model results. Number of contours, layer thickness and raster width were found to have a significant effect while raster angle had very little effect on the fatigue life of FFF-based 3D printed parts. In addition, it was observed that there is a high significant interaction between layer thickness and raster width of the FFF specimens.
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