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.

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.

Artificial intelligence plays a revolutionary role in modern transport systems. The article discusses the role of artificial intelligence in railway transport and its potential impact on the sector. This article presents different types of artificial intelligence technologies used in this sector, explores the advantages of artificial intelligence in this field, and discusses the challenges associated with using artificial intelligence in rail transport. Artificial Intelligence is revolutionary in rail transport systems by enhancing efficiency, safety, and overall performance. There are several ways in which AI influences rail transport and its impact on the cognitive load of human resources. These factors are examined in this article.

Fused Deposition Modeling (FDM) is a widely used 3D printing technology that can create a diverse range of objects. However, achieving the desired mechanical properties of printed parts can be challenging due to various printing parameters. Residual stress is a critical issue in FDM, which can significantly impact the performance of printed parts. In this study, we used Digimat-AM software to conduct numerical simulations and predict residual stress in Acrylonitrile Butadiene Styrene (ABS) material printed using FDM. We varied six printing parameters, including printing temperature, printing speed, and infill percentage, with four values for each parameter. Our results showed that residual stress was positively correlated with printing temperature, printing speed, and infill percentage, and negatively correlated with layer thickness. Bed temperature did not have a significant effect on residual stress. Finally, using a concentric infill pattern produced the lowest residual stress. The methodology used in this study involved conducting numerical simulations with Digimat-AM software, which allowed us to accurately predict residual stress in FDM-printed ABS parts. The simulations were conducted by systematically varying six printing parameters, with four values for each parameter. The resulting data allowed us to identify correlations between residual stress and printing parameters, and to determine the optimal printing conditions for minimizing residual stress. Our findings contribute to the existing literature by providing insight into the relationship between residual stress and printing parameters in FDM. This information is important for designers and manufacturers who wish to optimize their FDM printing processes for improved part performance. Overall, our study highlights the importance of considering residual stress in FDM printing, and provides valuable information for optimizing the printing process to reduce residual stress in ABS parts.

This study explores the optimization of ironing parameters in Material Extrusion (MEX) technology, specifically targeting improvements in the mechanical properties of polylactic acid (PLA) printed objects, with a focus on enhancing Ultimate Tensile Strength (UTS) and minimizing printing time. To achieve this, we employed a systematic experimental approach that varied flow rate, path speed, and spacing between passes, followed by a comprehensive statistical analysis using Analysis of Variance (ANOVA). Our findings indicate that path speed and spacing between passes significantly influence both UTS and printing time, while flow rate has a minor effect within the tested range. Optimal parameters were determined to be a flow rate of 14.34%, path speed of 30 mm/s, and spacing between passes of 0.3 mm, achieving a composite desirability of 0.970537. Validation using the response optimizer feature in Minitab software confirmed a strong correlation between predicted and experimental results. This research provides practical insights for optimizing MEX parameters, contributing to enhanced mechanical properties and efficiency in additive manufacturing processes.

It is well known that the surface quality obtained in additive manufacturing processes is highly variable. There are several reasons for this, of which the most prominent is the staircase effect, which results from the fact that 3D printing can be actually considered as a 2.5 machining process, as we build the part layer by layer. However, this staircase effect can be very different on surfaces that are arranged in different ways. By measuring the values that characterise the surfaces (Ra, Rz), however, we can observe that they are direction dependent, i.e. it does not matter how we measure them. This phenomenon is called surface anisotropy. It is clear that the surface roughness also has an effect on the tribological behaviour. In the case of a component where it is in contact with another component and relative displacement occurs between them, frictional properties may play a prominent role, which may thus also become direction dependent. Surface roughness also has a clear effect on fatigue properties. Consequently, for parts undergoing periodic dynamic stresses, it may be important to choose the right manufacturing orientation. The present study aims to demonstrate the extent of variation in surface roughness on different surfaces of a part produced by FDM. For this purpose, surface quality factors are investigated and evaluated on a self-designed model produced with given manufacturing parameters.