Abrasive water jet machining has become an indispensable process for cutting Kevlar fiber-reinforced polymers used in applications such as ballistics protection, race cars, and protective gloves. The complex and diffuse action of a large number of input parameters leads to the need to evaluate the quality characteristics of the technological transformation as a result of the deployment of experimental studies adapted to the specific processing conditions. Thus, the paper focuses on identifying the influence of different factors and modeling their action on the characteristics that define the quality of the cut parts, such as the kerf taper angle and the Ra roughness parameter, by applying statistical methods of design and analysis of experiments.

Abrasive waterjet cutting (AWJC) is widely used for processing Kevlar-reinforced polymer composites; however, surface quality is typically assessed using amplitude-based roughness parameters like Ra and Rz, which offer limited insight into the overall morphology of the machined surface. This study investigates the use of complementary roughness descriptors—focusing on profile height, distribution, spacing, and slope—to characterize the kerf wall morphology generated during the cutting process. Measurements were performed across different zones of the kerf wall under varied cutting conditions, enabling the examination of how specific process parameters influence these descriptors. All investigated parameters significantly influenced the four roughness descriptors, which proved to be strongly inter-correlated, reflecting a unified material removal mechanism. Furthermore, a clear distinction was highlighted between the smooth, uniform top zone and the rougher bottom zone featuring diagonal striations, confirming the sensitivity of these descriptors to local variations in cutting conditions.

The present paper aims to develop a systematic, prescriptive, and exclusively statistical problem-solving methodology that integrates scientific experimental design methods with the Six Sigma philosophy. This methodology was used for the study and continuous improvement of a direct dyeing process for textile materials. In the first stages of the methodology, the process was systematically analyzed; color difference was identified, using rank correlation as the main quality requirement of the customer; and the influence of the electrolyte concentration in the dye bath on this quality characteristic was tested, using analysis of variance. In the subsequent stages, a full factorial experiment was carried out to obtain a mathematical model describing the action of the main selected influence factors on the color difference; response surfaces and constant level curves were plotted to find the optimal settings of these influence factors. It was concluded that cotton fabric provides a more uniform chromatic reproduction, i.e., a lower color difference, compared to linen, and the electrolyte concentration of 20 g/L yielded the most stable chromatic performance for both fiber types.

Aligned with Six Sigma principles and continuous improvement, this study applies the structured DISMO methodology, supported by VBA software tools, to identify suitable operating conditions for abrasive waterjet machining (AWJC) of Kevlar fiber composites. Starting from ten machining output parameters evaluating process performance and productivity, rank correlation analysis prioritized the kerf taper angle (ka) and material removal rate (MRR). A screening phase using the random balance method reduced the input factors from six to three key factors: traverse speed (v), focusing tube diameter (D), and abrasive flow rate (q). First-order polynomial empirical models were established to quantify parameter effects. Multi-response optimization using a composite desirability function identified a balanced operating regime (v = 500mm/min, D = 0.76 mm și q = 200 g/min) with a desirability value of Ds = 0.70. This moderate value highlights the inherent trade-off between maximizing material removal rate and minimizing taper angle. Due to its general character, the developed methodological framework can be easily adapted for the analysis and control of other manufacturing processes.

The present paper aimed to develop a systematic, prescriptive and exclusively statistical problem-solving methodology that integrates scientific experimental design methods with the Six Sigma philosophy. This was used for the study and continuous improvement of a direct dyeing process for textile materials. In the first stages of the methodology, the process was systematically analyzed, the color difference was identified, using rank correlation, as the main quality requirement of the customer, and the influence of the electrolyte concentration in the dye bath on this quality characteristic was tested, using analysis of variance. In the subsequent stages, a full factorial experiment was carried out to obtain a mathematical model describing the action of the main selected influence factors on the color difference, and response surfaces and constant level curves were plotted to find the optimal settings of these influence factors. It was concluded that cotton fabric provides a more uniform chromatic reproduction, i.e. a lower color difference, compared to linen, and the electrolyte concentration of 20 g/L yielded the most stable chromatic performance for both fiber types.

Abrasive waterjet cutting (AWJC) is widely used for processing Kevlar-reinforced polymer composites; however, surface quality is typically assessed using amplitude-based roughness parameters like Ra and Rz, which offer limited insight into the overall morphology of the machined surface. This study investigates four complementary roughness descriptors—profile peak height, distribution, spacing, and slope—to characterize kerf wall morphology. Measurements were performed in two zones of the kerf wall under varied cutting conditions: traverse speed (100; 500 mm/min), focusing tube diameter (0.76; 1.00 mm), and abrasive grain size (100; 200 mesh). All investigated process parameters significantly influenced the descriptors. The bottom zone was rougher than the smooth top zone, with the maximum profile peak height (Rp), skewness (Rsk), and mean width of profile elements (RSm) increasing on average by 42.13%, 57.85%, and 45.80%, respectively, while the mean profile slope (Rda) showed a moderate average increase of 6.33%. Pearson correlation analysis showed strong relationships between the complementary descriptors and the conventional amplitude parameters (Ra, Rz, Rz1max, Rt) in the top zone (r ≥ 0.790), whereas in the bottom zone the correlations were lower (r = 0.421–0.798). This suggests that the behavior of the complementary descriptors changes with kerf depth.