Fused deposition modeling (FDM) is the most prevalent thermoplastic additive manufacturing technology. Many input parameters and their settings have a significant impact on the quality and functionality of FDM parts produced. To enhance the quality of parts, it is critical to be able to predict surface roughness distribution in advance. The development of artificial neural network (ANN) models to forecast the impact of main FDM process factors on the part quality in terms of surface roughness while utilizing ABS (Acrylonitrile butadiene styrene) material is described in this work. Taguchi L9 orthogonal array was used to plan the experiments. Different printing input parameters such as layer thickness, orientation angle, and infill angle are used in the experiments. In terms of controllable input parameters, ANN is used to construct a predictive mathematical model. The effects of various printing settings on surface roughness were investigated using analysis of variance (ANOVA), main effect plots, and contour plots. Experiment findings and regression value are used to validate the models. The model has shown to be capable of adequately predicting responses within a maximum percentage error of 4.664 percent of arithmetic roughness average (Ra), which is a good agreement.
Fused deposition modeling (FDM) technology is production devices that use plastic material in the semi-molten state to harvest the products directly from the CAD model. This study describes the development of mathematical models to predict the effects of significant process parameters of the FDM on the surface roughness of ABS material. Experiments were planned as per Taguchi orthogonal array. Experiments were conducted under different printing input parameters of layer thickness, orientation angle, and infill angle. Response surface methods (RSM) have been employed to develop a predictive mathematical model in terms of controllable input parameters. Analysis of Variance (ANOVA), main effect and interaction plot, 3D surface, and contour plot were used to investigate the influence of various printing parameters on surface roughness. Finally, Taguchi methodology and RSM approaches have been applied successfully for the optimization of surface roughness (Ra) in FDM printing parts. It was observed that the models can adequately describe the responses within the ranges considered as the maximum error percent in the prediction of mean Ra and S/N ratio of Ra are 14.61% and 18.83% respectively, which is in good agreement. The optimal combination of printing process parameters obtained indicates that optimum surface quality is layer thickness at 0.1mm, orientation angle at 0º, and infill angle at 0º.
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