Experimental investigation and ANN prediction for part quality improvement of fused deposition modeling parts

Abstract

Abstract 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.