Additive manufacturing (AM), commonly known as 3D printing, has gained significant popularity for its ability to produce intricate parts with high precision. However, the presence of residual stresses and warpage deformation are common issues affecting the quality and functionality of 3D-printed parts. This study conducts a comprehensive finite element analysis (FEA) to investigate the material-dependent impact of key printing parameters on residual stress and warpage deformation in 3D printing. The research focuses on three distinct materials: polyetherimide (PEI), acrylonitrile butadiene styrene (ABS), and polyamide 6 (PA6). Various printing parameters are systematically varied, including printing temperature, printing speed, bed temperature, infill density, layer thickness, and infill pattern. The study employs the Taguchi L27 orthogonal array and employs the analysis of variance (ANOVA) statistical technique to assess the significance of the input parameters. The obtained results reveal that certain parameters exhibit a greater sensitivity to material differences, whereas the layer thickness parameter demonstrates a relatively lower sensitivity. Notably, infill density and printing temperature play a crucial role in reducing residual stress for PA6, while the infill pattern parameter proves to be a significant contributor to minimizing warpage deformation across all three materials. These findings underscore the importance of conducting material-specific analyses to optimize 3D printing parameters and achieve the desired quality outcomes while mitigating residual stress and warpage deformation.

Fused Filament Fabrication (FFF) is an Additive Manufacturing (AM) technique implemented in widespread applications and several components. Despite its benefits, the physics behind the FFF process is quite complicated and requires fast heating and cooling rate of the extruded material. Consequently, the component experiences extremely non-uniform internal stresses that might lead to warpage deformation. It is necessary to optimize the printing parameters as they are associated with the warpage deformation of printed components. One method for achieving this target is conducting physical tests that offer precise findings, but it is an expensive strategy. Another approach is to simulate the printing parameters with special software. In this work, Digimat-AM was employed to develop a thermomechanical Finite Element Model of the FFF to simulate parts made of Polyamide12 (PA12). An L27 orthogonal array, a tool of the Taguchi orthogonal array, and an analysis of variance (ANOVA) were used to estimate the impact of five printing parameters and their ultimate levels to improve the dimension's quality by minimizing the warpage deformation. Results showed a significant impact of the bed temperature on the warpage deformation values. The infill density contributed 2.84% in reducing the warpage deformation, and the rest of the parameters' contribution was less than 1% for each.

Material Extrusion (MEX) technology, a prominent process in the field of additive manufacturing (AM), has witnessed significant growth in recent years. The continuous quest for enhanced material properties and refined surface quality has led to the exploration of post-processing techniques. In this study, we delve into the ironing process as a vital processing step, focusing on the optimization of its parameters through the application of Design of Experiments (DoE), specifically the Box–Behnken Design (BBD). Through a systematic examination of ironing process parameters, we identified optimal conditions that resulted in a substantial reduction in surface roughness (Ra) by approximately 69%. Moreover, the integration of optimized ironing process parameters led to remarkable improvements in mechanical properties. For instance, the Ultimate Tensile Strength (UTS) saw a substantial improvement of approximately 29%, while the compressive strength (CS) showed an increase of about 25%. The flexural strength (FS) witnessed a notable enhancement of around 35%, and the impact strength (IS) experienced a significant boost of about 162%. The introduction of ironing minimizes voids, enhances layer bonding, and reduces surface irregularities, resulting in components that not only exhibit exceptional mechanical performance but also possess refined aesthetics. This research sheds light on the transformative potential of precision experimentation, post-processing techniques, and statistical methodologies in advancing Material Extrusion technology. The findings offer practical implications for industries requiring high-performance components with structural integrity and aesthetic appeal.

Additive manufacturing using fused deposition modelling (FDM) is an affordable and efficient way to produce functional parts and prototypes. However, the FDM-manufactured parts, particularly those made of polylactic acid (PLA), has some drawbacks regarding accuracy and surface finish. To address this, post-processing techniques such as ironing can be applied. However, little is known about the effect of ironing process parameters on surface roughness, and there is a lack of comparative studies on contact and non-contact measurement methods. This study aims to address these gaps by investigating the impact of path speed, ironing spacing, and flow rate on the surface roughness of FDM-produced PLA samples using both contact and non-contact measurement methods. The results show that the ironing process can lead to a significant reduction in surface roughness values, and careful optimization of process parameters can further minimize roughness. The study highlights the advantages and limitations of contact and non-contact methods for surface roughness analysis and provides guidance on selecting the appropriate method for a given application and material.