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Fused deposition modeling (FDM) additive manufacturing has been intensively used for many industrial applications due to its attractive advantages over traditional manufacturing processes. The process parameters used in FDM have significant influence on the part quality and its properties. This process produces the plastic part through complex mechanisms and it involves complex relationships between the manufacturing conditions and the quality of the processed part. In the present study, the influence of multi-level manufacturing parameters on the temperature-dependent dynamic mechanical properties of FDM processed parts was investigated using IV-optimality response surface methodology (RSM) and multilayer feed-forward neural networks (MFNNs). The process parameters considered for optimization and investigation are slice thickness, raster to raster air gap, deposition angle, part print direction, bead width, and number of perimeters. Storage compliance and loss compliance were considered as response variables. The effect of each process parameter was investigated using developed regression models and multiple regression analysis. The surface characteristics are studied using scanning electron microscope (SEM). Furthermore, performance of optimum conditions was determined and validated by conducting confirmation experiment. The comparison between the experimental values and the predicted values by IV-Optimal RSM and MFNN was conducted for each experimental run and results indicate that the MFNN provides better predictions than IV-Optimal RSM. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) has been recognized as an effective technology to manufacture 3D dimensional parts directly from a digital computer aided design (CAD) model in a layer-by-layer style. Although it has become a significantly important manufacturing process, but it is still not well accepted additive manufacturing technology for load-carrying parts under dynamic and cyclic conditions due to many processing parameters affecting the part properties. The purpose of this study is to characterize the FDM manufactured parts by detecting how the individual and interactive FDM process parameters will influence the performance of manufactured products under dynamic and cyclic conditions. Experiments were conducted through fractional factorial design and artificial neural network (ANN). Effect of each parameter on the dynamic modulus of elasticity was investigated using analysis of variance (ANOVA) technique. Furthermore, optimal processing parameters were determined and validated by conducting verification experiment. The results showed that both ANN and fractional factorial models provided good quality predictions, yet the ANN showed the superiority of a properly trained ANN in capturing the nonlinear relationship of the system over fractional factorial for both data fitting and estimation capabilities. [ABSTRACT FROM AUTHOR]

In fused deposition modeling (FDM) additive manufacturing process, it is often difficult to determine the actual levels of process parameters in order to achieve the best dynamic mechanical properties of FDM manufactured part. This is mainly due to the large number of FDM parameters and a high degree of interaction between the parameters affecting such properties. This requires a large number of experiments to be determined. This paper presents a study on the influence of six FDM process parameters (layer thickness, air gap, raster angle, build orientation, road width, and number of contours) on the dynamic mechanical properties of the FDM manufactured parts using the fraction factorial design. The most influential parameters were statistically obtained through the analysis of variance (ANOVA) technique, and the results indicate that the layer thickness, the air gap, and the number of contours have the largest impact on dynamic mechanical properties. The optimal parameters for maximum dynamic mechanical properties were found to be layer thickness of 0.3302 mm, air gap of 0.00 mm, raster angle of 0.0°, build orientation of 0.0°, road width of 0.4572 mm, and 10 contours. Finally, a confirmation experiment was performed using optimized levels of process parameters, which showed good fit with the estimated values. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is a growing 3D printing technique widely practiced around the world in various industrial applications because of its ability to create complex 3D objects and geometries. Reduction of build time and feedstock material consumption without compromising the mechanical performance is the major concern in most industrial applications affecting the cost and the functionality of the manufactured part. One of the key issues of FDM process is how to select the correct parameters to reduce the build time and to reduce feedstock material consumption while maintaining high dynamic mechanical properties. In this study, influence of critical FDM parameters—layer thickness, air gap, raster angle, build orientation, road width, and number of contours—are studied using Q-optimal response surface methodology. Their effects on build time, feedstock material consumption and dynamic flexural modulus are critically examined. Mathematical models have been formulated to develop a functional relationship between the processing conditions and the process quality characteristics. Analysis of variance (ANOVA) technique was employed to check the adequacy and significance of mathematical models. Moreover, the optimal setting of process parameters was determined. A confirmation test was also conducted in order to verify the developed models and the optimal settings. The results show that Q-optimal design is a very promising method in FDM process parameter optimization. The results also confirm the adequacy of the developed models. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is one of the widely used additive manufacturing technologies because it has flexibility and ability to build complex parts. The accuracy of parts fabricated by FDM is greatly influenced by various process parameters. FDM process has a complex mechanism in building parts and often poses difficulty in understanding adequately how conflicting FDM parameters will determine part quality and accuracy. Sectors such as medical implant, telecommunication, electronics and aerospace require increasingly higher levels of dimensional accuracy. Thus, traditional methods of ensuring quality do not effectively address global markets and customer’s needs. This study proposes an I-optimality criterion for the optimization of FDM process parameters in order to address the limitations of the commonly used traditional designs. This study also aims to develop mathematical models in order to establish nonlinear relationship between process parameters and dimensional accuracy. The results show that I-optimality criterion is very promising technique in FDM process parameter optimization. Confirmation experiments show that the proposed method has great advantages in the aspect of both accuracy and efficiency compared with traditional methods proposed in previous studies. [ABSTRACT FROM AUTHOR]