Nowadays, FDM technology permits obtaining functional prototypes or even end parts. The process parameters, such as layer thickness, building orientation, fill density, type of support, etc., have great influence on the quality, functionality and behavior of the obtained parts during their lifetime. In this paper, we present a study concerning the possibilities of obtaining certain values for clearance in revolute joints of non-assembly mechanisms manufactured by FDM 3D Printing. To ensure the functioning of the assembly, one must know the relationship between the imposed and measured clearances by taking into account the significant input data. One way is to use the automat learning method with an artificial neuronal network (ANN). The data necessary for the training, testing, and validation of ANN were experimentally obtained, using a complete L 27 Taguchi experimental plan. A total of 27 samples were printed with different values of the following parameters: the infill density, the imposed clearance between the shaft and the hole, and the layer thickness. ANN architecture corresponds to the Hecht–Kolmogorov theorem. Genetic algorithms (GA) were used for the optimization of the output. The Neural Network Toolbox from MATLAB was used for training the network and a hybrid tool genetic algorithm artificial neural network (GA-ANN) was used to minimize the value of the absolute relative clearance (arc). The minimum value of the absolute relative clearance established by GA-ANN was 0.0385788. This value was validated experimentally, with a relative difference of 4%. We also introduced a rational function to approximate the correlation between the input and output parameters. This function fulfills some frontier conditions resulted from practice. In addition, the function may be used to establish the designed clearance in order to obtain an imposed one.

Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R² values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality.

This paper presents research on the degradation processes of the fresco painting in the cave church of Corbii de Piatră Hermitage under the influence of meteoric infiltration water and environmental factors. The medieval fresco dates from the end of the 13th century and the beginning of the 14th century, being painted on a sandstone wall. The infiltration of meteoric water through this wall, the temperature variations, the environment and the repeated wetting/drying processes determined the degradation of the fresco, resulting in its detachment from large surfaces. This research established correlations between the processes that take place, the structural transformations, the changes in composition and the adhesion of the fresco to the sandstone wall. The results have been made available to conservation and restoration specialists, in order to choose appropriate materials and technologies. This paper presents findings regarding the pictorial material and introduces new analysis techniques in research on the degradation processes of the fresco painting in the cave church of Corbii de Piatră Hermitage under the influence of meteoric infiltration water and environmental factors.

The Inconel 718 superalloy, a precipitation-hardenable material, is of particular interest for applications involving components operating under extreme conditions due to its excellent mechanical properties, high corrosion resistance at temperatures up to 700 °C, and good workability. At high temperatures, thermal transfer processes are crucial for temperature distribution across the component’s section, structural transformations, and variations in the alloy’s properties. The history of accidental overheating events is critical for the microstructure and properties of the alloy. Studies on thermal transfer in the Inconel 718 alloy available in the literature typically focus on the alloy in its as-delivered state. The experimental research presented in this paper examines the influence of repeated overheating history on the thermal diffusivity of the alloy.

The fresco of the Corbii de Piatră Cave Church, dating from the end of the 13th century and the beginning of the 14th century, is applied to the sandstone wall. The degradation of the fresco on large surfaces, with many areas of detachment, has been determined by the infiltration of meteoric water through the sandstone wall on which it is applied, as well as temperature variations, and repeated wetting/drying processes. However, there are small portions of fresco that show good adhesion to the wall. The present research, aimed at providing scientific data to restorers and historians, involves the advanced characterization of a fragment of fresco with good adhesion to the wall and is being carried out by an interdisciplinary team. The stratigraphy, microstructure, compaction defects, chemical composition, and variation of chemical composition in the fresco from the pictorial surface to the mortar-sandstone interface were determined. Correlations were established between degradation processes and wall adhesion.

The Inconel 718 alloy is a nickel-based superalloy that can be strengthened through precipitation hardening. Due to its exceptional mechanical properties, high corrosion resistance, and good workability, it is particularly suitable for applications where components operate in corrosive environments at temperatures up to 600 °C. Under these conditions, overheating frequently occurs, leading to structural transformations and changes in mechanical properties. This experimental study examined the effect of repeated overheating on the alloy’s structure, the formation of oxide layers, and hardness. The cyclic overheating process was simulated using thermal shocks induced by solar energy, with temperatures exceeding the recommended range, between 700 and 1000 °C. Morphological characterization, elemental chemical analysis, qualitative phase analysis, and microhardness measurements highlighted the transformations induced by cyclic thermal stress at high temperatures.