Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method.

The aim of this study is to investigate the environmental risk of long-term metallurgical waste disposal. The investigated site was used for the open storage of lead and zinc waste materials originating from a lead smelter and refinery. Even after remediation was performed, the soil in the close vicinity of the metallurgical waste deposit was heavily loaded with heavy metals and arsenic. The pollutants were bound in various compounds in the form of sulfides, oxides, and chlorides, as well as complex minerals, impacting the pH values of the investigated soil, such that they varied between 2.8 for sample 6 and 7.34 for sample 8. In order to assess the environmental risk, some eight soil samples were analyzed by determining the total metal concentration by acid digestion and chemical fractionation of heavy metals using the BCR sequential extraction method. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine six elements (As, Cd, Cu, Pb, Zn, and Ni). Total concentrations of the elements in the tested soil samples were in the range of 3870.4–52,306.18 mg/kg for As, 2.19–49.84 mg/kg for Cd, 268.03–986.66 mg/kg for Cu, 7.34–114.67 mg/kg for Ni, 1223.13–30,339.74 mg/kg for Pb, and 58.21–8212.99 mg/kg for Zn. The ratio between the mean concentrations of the tested metals was determined in this order: As > Pb > Zn > Cu > Ni > Cd. The BCR results showed that Pb (50.7%), Zn (49.2%), and Cd (34.7%) had the highest concentrations in mobile fractions in the soil compared to the other metals. The contamination factor was very high for Pb (0.09–33.54), As (0.004–195.8), and Zn (0.14–16.06). According to the calculated index of potential environmental risk, it was confirmed that the mobility of Pb and As have a great impact on the environment.

3D printing represents one of the newer production technologies which are becoming more and more important. The widening range of materials used in 3D printing leads to the need to examine their mechanical properties. 3D printing parameters such as i...

This paper investigates the influence of layer height, one of the key parameters in Fused Deposition Modeling (FDM) 3D printing, on the surface roughness of parts manufactured using PETG (Polyethylene Terephthalate Glycol) material. Specimens were printed with different layer heights (0.1 mm, 0.2 mm, and 0.3 mm) under identical printing conditions to evaluate how this parameter affects surface quality. Surface roughness (Ra) was measured using a profilometer, and the results indicate that increased layer height leads to higher surface roughness values, resulting in a rougher surface finish. The findings highlight the importance of selecting an appropriate layer height to achieve the desired surface quality, particularly in applications where aesthetics or functional surface characteristics are critical. This study contributes to the optimization of PETG 3D printing processes in contexts where surface finish is of particular importance.