Cataphoresis varnishing enables an organic coating to form on an aluminum substrate, thus increasing its corrosion resistance and durability. Cataphoresis varnishing is known to ensure a high adhesion of the created cataphoresis layer and a good homogeneity of this layer, even on surfaces with complex geometry. This paper aimed to optimize the deposition process and to analyze and evaluate the thickness of a cataphoresis layer formed on an aluminum substrate from AW 1050—H24 material. In total, 30 separate samples were created in accordance with the Design of Experiments methodology, using a central composite plan. The independent input factors in the study were: the electrical voltage (U) and deposition time in the cataphoresis varnishing process (t_KTL) at the polymerization times of 15 min, 20 min, and 25 min, respectively. The results of the statistical analysis showed that the voltage accounted for 33.82% of the change in the thickness of the created layer and the deposition time contributed 28.67% to thi change. At the same time, the interaction of the voltage and deposition time (p < 0.0001) accounted for 20.25% of the change in the thickness of the layer under formation. The regression model that was constructed showed a high degree of prediction accuracy (85.8775%) and its use as a function for nonlinear optimization provided a maximum layer thickness t_h of max = 26.114 µm, at U = 240 V and t_KTL = 6.0 min, as was proven under experimental conditions.

This research examines the inter-relationship between the deposition time, degreasing temperature, and applied voltage in the cataphoretic painting process, focusing on their cumulative effects on the thickness of the formed layers. A series of experiments was conducted, systematically varying deposition time effects through voltage levels (200 V to 300 V) and degreasing temperatures (40 °C to 80 °C). The results demonstrate that the maximum layer thickness is achieved at longer cataphoretic times, with significant thickness increments observed at optimal voltage levels. Conversely, the study reveals that lower degreasing temperatures lead to increased layer thickness, while elevated temperatures tend to diminish it. Notably, the thickness variations are consistent across different voltage applications, with a discernible threshold at which the layer thickness stabilizes. Additionally, energy-dispersive X-ray spectroscopy (EDX) was utilized to characterize the elemental composition of the cataphoretic layer, providing deeper insights into the coating structure and its relationship to process parameters. This work provides valuable insights into the optimization of cataphoretic processes, offering a framework for enhancing the quality and uniformity of coatings in industrial applications. The findings underscore the importance of the precise control over process parameters to achieve the desired material characteristics, thereby advancing the field of surface engineering and coating technologies.

The objective of the experimental analysis was to assess the impact of the reuse of technological waste (recyclate) on the selected performance properties of the fibrous polymer composite used to produce components for the automotive industry by injection molding technology. Polyphthalamide (PPA), which belongs to a group of high-tech polymers, was chosen as the analyzed material. In accordance with the set goals, the rheological, mechanical, and structural properties of the material were evaluated using ANOVA analysis in the experimental part of the work, depending on the mass ratio of the recycled material added to the virgin material. The influence of the proportion of recycled material on the lifetime of moldings by the method of their exposure at an elevated temperature for a defined time was also assessed. During the research, it was found that at a concentration of up to 40 wt. % of recyclate, its mechanical properties do not change significantly. At a concentration of 50 wt. %, there is a rapid decrease in mechanical properties. In the long term, it can also be said that the addition of recyclate significantly affects the service life of the components. No significant changes in morphology were observed during the analysis of structural properties.

This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η² ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X^¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ², Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control.