Resumen: La determinación de los parámetros eléctricos y funcionales para aplicación de soldadura requiere realizar pruebas preliminares tanto en la industria como en la investigación. Sin embargo, este proceso resulta ser muy costoso, lento y, para objeto de investigación, de baja confiabilidad, por tal motivo, el desarrollo de una metodología de experimentación que permita obtener adecuadamente dichos parámetros es muy importante. El presente trabajo muestra el análisis estadístico para la aplicación de soldadura de arco metálico con electrodo de tungsteno y gas inerte (GTAW) utilizada para determinar la velocidad de avance de la antorcha y la corriente de soldadura óptimos para lograr soldadura libre de defectos. Con los resultados obtenidos en el diagrama causa-efecto (Ishikawa) se permite la interpretación efectiva de la causa raíz de los defectos, reduciendo el número de experimentos, posteriormente el diagrama de árbol da pauta para definir la matriz experimental. La validación de la experimentación se hace con el diseño de experimento factorial completo, la efectividad proporcionada por el cálculo del coeficiente de determinación, también conocido como nivel de correlación, de 94,24% en penetración del cordón y 85,03% en ancho del cordón. La gráfica de residuales y valores predichos valida la distribución del error en las probetas.

This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and DC buses, where voltage gain is one of the essential issues to consider. The NC-MFT inside the DC-DC converter is designed with a new approach that not only provides isolation but also contributes to achieving high efficiency and a higher step-up ratio. The high efficiency of the converters contributes to the integration of PV systems into DC microgrids. The converter yields a high voltage conversion ratio of 16.17. The experimental results obtained at 41.8 V/676 V and 275 W for the prototype revealed high efficiency (95.63% at full load). The experimental results validate the theoretical analysis and simulation, confirming that the converter achieves the main objective of high voltage conversion and high efficiency. These results will contribute to the interest in the use of this type of energy and its impact on the reduction in CO₂ emissions.

The presence of contaminants influences braking efficiency in the railway system because it alters the adhesion at the wheel–rail interface. It is essential to study this phenomenon, as contaminants reduce the friction between wheels and rails, which impacts braking and transport safety. In addition, these contaminants increase the risk of derailments. The objective of the research was to determine the impact of different contaminants and operating speeds on the critical braking system’s responses. Using the 3^k full factorial experimental design methodology, with analysis of variance (ANOVA) and linear and quadratic regressions, visualized using surface graphs, the effects of three operating conditions were studied: clean rails, with sand and sawdust, and driving the train at three operating speeds. These conditions gave rise to variations in braking distances, maximum creep, wheel slip times, and maximum peaks of electric current when braking in each experiment. The tests were carried out on the straight section of a β-shaped track and a railway vehicle, designed at a scale of 1:20. The analysis reveals that the braking distance increases significantly with surface roughness (clean track < sawdust < sand). At 0.75 m/s, the sawdust track reduces braking distance by 21% compared with the clean track; at 1.00 m/s, the reduction is 19%; and at 1.30 m/s, it is 35%.

Railway braking efficiency hinges on the thermomechanical conditions at the wheel-rail interface. Frictional heating during operation causes significant temperature fluctuations, directly impacting braking performance in rail vehicles. Evaluating these effects is important for developing infrastructure and components adapted to environmental conditions. Several studies have explored the influence of temperature on components such as the brake disc or the wheel; little attention has been paid to the thermal conditions of the rail itself. This paper examines the effect of rail temperature on the braking behavior and energy consumption of a railway vehicle. Using a 1:20 railway track, rail segments were subjected to four temperatures (28.5 °C, 40.0 °C, 49.9 °C, 71.0 °C) by heating with Nichrome wire, and tests were performed at three speeds (0.75, 1.00, and 1.30 m/s). The results show that higher rail temperatures improve wheel-rail adhesion up to an optimum point (40.0 °C), beyond which performance deteriorates. In contrast, tests at 71.0 °C showed reduced braking efficiency, despite lower electrical current peaks, indicating a non-linear thermal response.

Condition monitoring of railway track surfaces is crucial for ensuring the safety, operational efficiency, and effective maintenance of railway systems. This work presents a data-driven modelling and an experimental methodology for identifying and classifying contaminants on railway tracks using vibration analysis and artificial intelligence techniques. In this study, the railway dynamics were physically simulated using a 1:20 scaled test rig, where the rails were treated with various contaminants (oil, water, and sand), and the resulting vehicle vibrations were recorded by on-board accelerometers and gyroscopes. To construct the predictive model, a hybrid architecture was designed integrating Short-Time Fourier Transform (STFT) for time-frequency feature extraction and a multi-channel Convolutional Neural Network (CNN) for pattern recognition. Initial results indicate that accelerometer data, particularly from longitudinal and lateral vibrations, are more effective than gyroscope data for classifying certain contaminants. To enhance classification robustness, this work introduces a multi-channel CNN that simultaneously processes the most informative signals, leading to a significant improvement in detection accuracy across all tested contaminants. This study validates the effectiveness of the proposed methodology as a robust and reliable solution for contaminant detection, while also confirming the utility of the scaled testbed as a valuable platform for future research in railway dynamics.