Javier Cruz-Salgado

In this study, we analyze the production process capability of sandpaper manufacturing, with an emphasis on material removal from the finished product and the identification of manufacturing variables that most influence grinding performance and final quality. To this end, the CRISP-DM methodology was applied along with linear regression, stepwise analysis, and principal component analysis (PCA) to a sample of 62 operational variables collected between 2024 and 2025. These variables were reduced to 12 critical dimensions that explain 80% of the process variability. This study highlights the interaction between chemical properties of the adhesive system (gel time, pH, and formaldehyde concentration) and fine mechanical adjustments (blade and roller clearance), showing how these variables jointly affect sanding performance. By integrating these factors into a multivariate framework, PCA allows for the identification of latent relationships, reduces process complexity, and establishes a statistical basis for standardization and continuous improvement, with the aim of supporting the transfer of technical knowledge in industrial manufacturing environments. The proposed framework is intended to support technical knowledge transfer in industrial manufacturing environments.

Three-dimensional reconstruction of anatomical models from clinical data faces challenges in maintaining geometric fidelityduring the conversion of surface models to parametric solids. Mesh processing techniques, such as smoothing, remeshing,and polygon reduction, aim to optimize mesh quality but can lead to loss of geometric information. A methodology wasdeveloped to process 3D surface meshes and convert them into solids from both faceted and organic geometries. Theimpact of different processing parameters on mesh topology and post-conversion geometry was evaluated using theTaguchi methodology with an L16 orthogonal design, analyzing three factors: smooth factor, remesh, and polygon reductionpercentage. The smooth factor had the most significant influence (p < 0.01), followed by remesh (p < 0.05), while polygonreduction was not statistically significant (p > 0.05). Conversion techniques produced significant differences (p = 0.0012),with faceted geometries yielding results closer to the target. Geometric deviation analysis using bidirectional Hausdorffdistance demonstrated sub-millimetric discrepancies in all cases. Faceted models showed RMS values between 0.067 and0.183 mm while organic models ranged from 0.076 to 0.225 mm with minimal deviation. Volumetric analysis revealedmoderate reductions averaging 4.53 ± 2.88% for faceted and 4.98 ± 3.07% for organic geometries without volumetricexpansion. Despite slightly higher sensitivity in organic models, deviations remained within acceptable tolerances foranatomical reconstruction and computational biomechanics, indicating preserved morphological fidelity after processing

The optimization of formulated products often requires simultaneous consideration of mixture composition and processing conditions, as both factors can strongly influence the final response. In this study, a mixture experimental design incorporating process variables was applied to the optimization of a prepared beverage (margarita), with the objective of maximizing sensory response. Four mixture components were evaluated under the combined influence of two process variables, and the experimental data were analyzed using regression modeling and analysis of variance. A stepwise regression procedure was employed to identify the most significant terms, revealing that linear mixture effects alone were insufficient to explain the system behavior. The final model exhibited excellent goodness of fit and predictive performance, with high coefficients of determination and a low residual error, while diagnostic analyses confirmed that the assumptions of normality, homoscedasticity, and independence were satisfied. Graphical tools, including simplex design plots, contour plots, and mixture surface plots, provided clear interpretation of the nonlinear response behavior and identified interior regions of the mixture space associated with favorable responses. Response optimization yielded a balanced formulation and specific process conditions that maximized the predicted sensory score with high desirability. Overall, the results demonstrate that integrating process variables into mixture design provides a robust and practical framework for beverage optimization, offering improved interpretability and decision‐making compared to traditional mixture‐only approaches.

The increasing incorporation of biodegradable plastics into conventional recycling streams has raised concerns regarding their influence on the performance of recycled polymers. In particular, the presence of polylactic acid (PLA) in recycled high‐density polyethylene (rHDPE) may significantly affect mechanical stability during reprocessing and environmental exposure. This study systematically evaluates the combined effects of PLA content (0–50 wt%), recycling cycles, exposure time, and aging conditions (natural and accelerated) on the mechanical performance of rHDPE/PLA blends using a multilevel full factorial experimental design comprising 96 experimental runs. Tensile stress, elongation at break, and impact resistance were measured according to ASTM standards. Statistical analyses including ANOVA, regression modeling, and interaction analysis were applied to identify significant factors and combined effects, followed by multiresponse optimization using a desirability‐function approach. PLA incorporation produced a drastic reduction in ductility and toughness, with elongation at break decreasing by more than 95% and impact resistance by approximately 80%–85% as PLA content increased from 0 to 50 wt%. Accelerated aging generated the most pronounced reduction in tensile strength, with decreases on the order of 20%–30% compared with natural aging conditions. Recycling cycles further contributed to mechanical degradation, although to a lesser extent. Significant interaction effects among composition, recycling cycle, and aging environment demonstrated that mechanical degradation results from synergistic thermo‐oxidative, processing‐induced, and interfacial incompatibility mechanisms. The results provide a comprehensive statistical framework for understanding the degradation behavior of rHDPE systems contaminated with biodegradable polymers and support the identification of optimal operating conditions for maximizing the mechanical performance of recycled polymer blends.

This article establishes a non-parametric validation framework for photoplethysmography (PPG) signals intended for heart rate monitoring, formulated as a methodological proof-of-concept study. The study applies the two-sample Kolmogorov–Smirnov test as a robust and versatile method for comparing the distributions of PPG signals. By integrating the two-sample Kolmogorov–Smirnov test into the validation process of cardiac pulse measurement devices, the work demonstrates its effectiveness in enhancing the accuracy and reliability of biomedical signal analysis. Results show that, when comparing signals against calibrated reference devices and visualizing cumulative distribution functions, the two-sample Kolmogorov–Smirnov test is capable of detecting subtle differences in signal behavior. This innovative use of the two-sample Kolmogorov–Smirnov test provides valuable insights for the design and validation of biomedical signal processing systems and contributes to the advancement of non-invasive health monitoring technologies. The proposed framework is evaluated under controlled experimental conditions using data acquired from a single subject. Consequently, the results should be interpreted as a methodological validation of the proposed statistical approach rather than as a definitive clinical validation, and are intended to demonstrate feasibility and analytical relevance rather than population-level generalizability.

The inclusion of process variables in mixture experimental design is crucial for optimizing final products with precision. Unlike standard response surface designs, which are limited by the requirement that proportions must sum to 100%, mixture-process experiments enable a thorough evaluation of how operational factors, such as particle size and mixing time, interact with mixture components. This approach enhances the understanding of how processing conditions affect product properties and leads to more accurate predictive models, thereby improving production consistency and reliability. Regression analysis reveals that interactions between PET and both particle size and mixing time significantly impact the response variable. The model demonstrates strong predictive accuracy, with R-squared and adjusted R-squared values of 92% and 86%, respectively, and a low root mean square error (S) of 0.2818. The PRESS value of 3.38 confirms the model’s ability to accurately predict new data. The absence of high multicollinearity, as indicated by variance inflation factor (VIF) values below 5, further supports the model's stability and interpretability. Contour plots illustrate the effect of varying mixture proportions on the response, such as tensile strength, showing a positive impact of both particle size and mixing time. The highest tensile strength is achieved at maximum levels of these variables, indicating a synergistic effect. Response variable optimization identifies the optimal mixture composition as 90% PET, 10% wood, and no coupling agent. To maximize tensile strength, the largest particle size and longest mixing time should be used, though extrapolating beyond the studied parameters should be done with caution.

A mixture experiment is one where the response depends only on the relative proportions of the ingredients present in the mixture. Different regression models are used to analyze mixture experiments, such as the Scheffé model, the Slack Variable model, and models with inverse terms. Models with inverse terms are worthy of consideration in certain applications. These models have been analyzed considering their fit quality, but not their numerical stability. This article analyzes the numerical stability of the model with inverse terms and the use of pseudo components. Likewise, a criterion is defined for the selection of the regression model with inverse terms, based on the quality of fit and numerical stability.

Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems.