This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

The rapid growth of the Internet of Things (IoT) in industrial environments has increased efficiency but also heightened vulnerability to sophisticated cyber-attacks. Traditional cyber security approaches are insufficient to protect critical infrastructure, creating a need for dynamic, adaptive solutions. Evolutionary algorithms (EAs), owing to their ability to explore large search spaces and optimise parameters, offer a promising route to enhancing IoT security. This review highlights the integration of EAs with deep-learning techniques to improve intrusion detection and system resilience. Building on this background, we propose an adaptive cyber-security framework that leverages evolutionary optimisation and continual learning to detect, prevent and mitigate attacks in real time. The study emphasises the importance of validating hybrid models in real-world settings and of optimising computational efficiency. Future work should investigate autonomous response mechanisms and the scalability of solutions for large-scale Industrial IoT (IIoT) deployments, ensuring robust protection against emerging threats and aligning academic advances with industry needs.

Este artículo presenta una propuesta de un sistema inteligente para el mantenimiento predictivo de un horno industrial, el objetivo del sistema es el monitoreo en tiempo real, el ajuste automático de perfiles térmicos y la predicción de fallas mediante modelos de aprendizaje automático. Se hace uso de datos simulados generados con base en condiciones reales de operación. La solución integra tecnologías del Internet de las Cosas (IoT) y algoritmos de aprendizaje automático, hace énfasis en la importancia de la Industria 4.0 e Industria 5.0. El sistema desarrollado es modular, escalable. Se reportan métricas destacadas en un entorno simulado, como una precisión del 99.5%, sensibilidad del 100% y un área bajo la curva ROC de 0.9995. Esta tecnología busca acelerar la transformación digital del sector industrial mexicano, facilitando su adopción tanto en grandes corporativos como en pequeñas y medianas empresas [5].