It is usual practice to optimize the various processing parameters in order to achieve well-organized and lucrative process conditions. For the creation of tungsten carbide/silicon nitride/AA2219 composites, sintering temperature, sintering pressure, dwelling time, and heating rate all must be optimized. Design of experiments and analysis of variance were employed to assess the factors’ contributions to density as well as microhardness response variables. It was decided to test the admixed powders and Vickers hardness tester, optical microscope, and Archimedes-based density testing to evaluate the sintered compacts. The optimum spark plasma sintering factors were a temperature of 500°C, a pressure of 30 MPa, a dwelling time of 8 minutes, and a heat rate of 160 °C/min, resulting in an extreme density of 2.71 g/cm3 and a maximum microhardness of 38.61 HV (0.38 GPa).
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.
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