Propuesta de monitoreo de las condiciones físicas de funcionamiento en tiempo real de una máquina extrusora de PVC mediante la implementación de tecnología 4.0

Jaime Eduardo Barahona-Martínez; Wilson Santiago Olovacha-Toapanta

Autores/as

Barahona-Martínez Jaime Eduardo Universidad Técnica de Cotopaxi. Latacunga, Ecuador. https://orcid.org/0009-0008-3606-4408
Olovacha-Toapanta Wilson Santiago Universidad Técnica de Cotopaxi. Latacunga, Ecuador. https://orcid.org/0000-0002-3377-7049

Palabras clave:

Industria 4.0, Monitoreo en Tiempo Real, Extrusora de PVC, Simulación, Aplicación Móvil, Internet de las Cosas (IoT), Mantenimiento Predictivo, Kotlin

Resumen

DOI: https://doi.org/10.46296/ig.v9i17.0345

Resumen

El presente proyecto aborda la necesidad de optimizar los procesos en la industria manufacturera de polímeros mediante la propuesta de un sistema de monitoreo en tiempo real para una máquina extrusora de PVC, basado en tecnologías de la Industria 4.0. La revisión de la literatura académica confirma la tendencia global hacia la digitalización, destacando la importancia del Internet de las Cosas (IoT), el análisis de Big Data y la Inteligencia Artificial (IA) para mejorar la eficiencia operativa, predecir fallos y asegurar la calidad del producto. La propuesta busca integrar estos elementos para crear un sistema ciberfísico robusto que ofrezca una supervisión continua de las condiciones físicas de la maquinaria. Como validación conceptual de la propuesta, se desarrolló una aplicación móvil para Android utilizando Kotlin. Este prototipo simula el funcionamiento del motor de la extrusora, generando y mostrando datos en tiempo real de parámetros eléctricos (voltaje, potencia) y térmicos (temperaturas por zonas). La aplicación incluye una interfaz de usuario moderna con indicadores visuales, alertas automáticas ante condiciones anómalas (sobrecorriente, sobrecalentamiento) y una función para exportar un historial completo de los datos en formato CSV. Este desarrollo demuestra la viabilidad de la arquitectura de software propuesta y sirve como una prueba de concepto efectiva, materializando los principios de monitoreo, alerta y registro de datos que son fundamentales para la Industria 4.0. La solución final representa un paso tangible hacia la implementación de sistemas de mantenimiento predictivo y optimización de procesos en el sector.

Palabras clave: Industria 4.0, Monitoreo en Tiempo Real, Extrusora de PVC, Simulación, Aplicación Móvil, Internet de las Cosas (IoT), Mantenimiento Predictivo, Kotlin.

Abstract

This project addresses the need to optimize processes in the polymer manufacturing industry by proposing a real-time monitoring system for a PVC extruder, based on Industry 4.0 technologies. A review of the academic literature confirms the global trend toward digitalization, highlighting the importance of the Internet of Things (IoT), Big Data analytics, and Artificial Intelligence (AI) to improve operational efficiency, predict failures, and ensure product quality. The proposal seeks to integrate these elements to create a robust cyber-physical system that offers continuous monitoring of the machinery's physical conditions. As conceptual validation of the proposal, a mobile application for Android was developed using Kotlin. This prototype simulates the operation of the extruder motor, generating and displaying real-time data on electrical parameters (voltage, power) and thermal parameters (zone temperatures). The application includes a modern user interface with visual indicators, automatic alerts for abnormal conditions (overcurrent, overheating), and a function to export a complete data history in CSV format. This development demonstrates the viability of the proposed software architecture and serves as an effective proof of concept, materializing the monitoring, alerting, and data logging principles that are fundamental to Industry 4.0. The final solution represents a tangible step toward the implementation of predictive maintenance systems and process optimization in the sector.

Keywords: Industry 4.0, Real-Time Monitoring, PVC Extruder, Simulation, Mobile Application, Internet of Things (IoT), Predictive Maintenance, Kotlin.

Información del manuscrito:
Fecha de recepción: 06 de febrero de 2026.
Fecha de aceptación: 20 de abril de 2026.
Fecha de publicación: 20 de mayo de 2026.

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Citas

Alsafran, A. S., Alwabari, M., & Al-Bahrani, M. (2025). Challenges in Implementing IoT for Enhanced Reliability and Effectiveness in Smart Grids: Literature Review. International Transactions on Electrical Energy Systems, 2025(1), 5514628. https://doi.org/10.1155/etep/5514628

Archenti, A., Gao, W., Donmez, A., Savio, E., & Irino, N. (2024). Integrated metrology for advanced manufacturing. CIRP Annals, 73(2), 639–665. https://doi.org/10.1016/j.cirp.2024.05.003

Arefin, N., Moni, H.-E., Espinosa, D., Cong, W., & Zeng, M. (2025). Multi-material additive manufacturing of energy storage and conversion devices: Recent progress and future prospects. Applied Physics Reviews, 12(1). https://pubs.aip.org/aip/apr/article/12/1/011330/3339482

Ashebir, D. A., Hendlmeier, A., Dunn, M., Arablouei, R., Lomov, S. V., Di Pietro, A., & Nikzad, M. (2024). Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques. Polymers, 16(21), 2986. https://doi.org/10.3390/polym16212986

Cavallucci, D., Brad, S., & Livotov, P. (Eds.). (2025). World Conference of AI-Powered Innovation and Inventive Design: 24th IFIP WG 5.4 International TRIZ Future Conference, TFC 2024, Cluj-Napoca, Romania, November 6–8, 2024, Proceedings, Part II (Vol. 736). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-75923-9

Chirumalla, K., Oghazi, P., Nnewuku, R. E., Tuncay, H., & Yahyapour, N. (2025). Critical factors affecting digital transformation in manufacturing companies. International Entrepreneurship and Management Journal, 21(1), 54. https://doi.org/10.1007/s11365-024-01056-3

Dostatni, E., Osiński, F., Mikołajewski, D., Sapietová, A., & Rojek, I. (2024). Neural Networks for Prediction of 3D Printing Parameters for Reducing Particulate Matter Emissions and Enhancing Sustainability. Sustainability, 16(19), 8616. https://doi.org/10.3390/su16198616

Fernández, A., García, F. E., Ortíz, S., Jiménez, M., Fernández, A. P., & Settembre-Blundo, D. (2025). AI-Driven Transformations in Manufacturing: Bridging Industry 4.0, 5.0, and 6.0 in Sustainable Value Chains. Future Internet, 17(9), 430. https://doi.org/10.3390/fi17090430

Gatell, I. S., & Avella, L. (2024). Impact of Industry 4.0 and circular economy on lean culture and leadership: Assessing digital green lean as a new concept. European Research on Management and Business Economics, 30(1). https://doi.org/10.1016/j.iedeen.2023.100232

Hornos, M. J., & Quinde, M. (2024). Development methodologies for IoT-based systems: Challenges and research directions. Journal of Reliable Intelligent Environments, 10(3), 215–244. https://doi.org/10.1007/s40860-024-00229-9

Jing, L., Matsumoto, Y., & Zhang, Z. (2024). Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments. https://www.mdpi.com/books/reprint/9829-exploring-iot-sensors-and-their-applications-advancements-challenges-and-opportunities-in-smart

Keim, A. O., Pandit, P. P., Sotelo, L. D., & McClain, M. S. (2025). Nondestructive evaluation of material properties in polymer additive manufacturing processes: Moving towards process-agnostic qualification. Virtual and Physical Prototyping, 20(1), e2569544. https://doi.org/10.1080/17452759.2025.2569544

Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Krolczyk, G. M., & Wu, C. (2023). Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. Journal of Intelligent Manufacturing, 34(1), 21–55. https://doi.org/10.1007/s10845-022-02029-5

Manjunatha, C. M., Padmanabhan, M. A., Dey, A., & Mohan Kumar, M. (Eds.). (2025). Advances in Materials and Manufacturing Processes for Composite Applications (AMMPCOM): Proceedings of the ISAMPE National Conference on Composites (INCCOM-18) (Vol. 83). Springer Nature. https://doi.org/10.1007/978-981-96-8734-3

Mizuyama, H., Morinaga, E., Nonaka, T., Kaihara, T., Von Cieminski, G., & Romero, D. (Eds.). (2026). Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond: 44th IFIP WG 5.7 International Conference, APMS 2025, Kamakura, Japan, August 31 - September 4, 2025, Proceedings, Part V (Vol. 768). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-03546-2

Mrugalska, B., Trzcielinski, S., Karwowski, W., Di Nicolantonio, M., & Rossi, E. (Eds.). (2020). Advances in Manufacturing, Production Management and Process Control: Proceedings of the AHFE 2020 Virtual Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, July 16–20, 2020, USA (Vol. 1216). Springer International Publishing. https://doi.org/10.1007/978-3-030-51981-0

Reis, J., & Melão, N. (2023). Digital transformation: A meta-review and guidelines for future research. Heliyon, 9(1), e12834. https://doi.org/10.1016/j.heliyon.2023.e12834

Singh, S., Mohite, P., & Datta, D. (Eds.). (2025). Biomaterial-based Additive Manufacturing in Tissue Engineering and Regeneration (Vol. 7). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-96070-3

Tajima, T., & Chen, P. (2023). Progress in Laser Accelerator and Future Prospects. MDPI-Multidisciplinary Digital Publishing Institute. https://www.mdpi.com/books/reprint/7438-progress-in-laser-accelerator-and-future-prospects/

Wang, Q., Li, Y., & Li, R. (2025). Integrating artificial intelligence in energy transition: A comprehensive review. Energy Strategy Reviews, 57, 101600. https://doi.org/10.1016/j.esr.2024.101600

Werheid, J., Behnen, H., Woltersmann, J.-H., He, S., Hamann, T., Abdelrazeq, A., & Schmitt, R. H. (2025). Machine vision in manufacturing SMEs: A review. Discover Applied Sciences, 7(5), 371. https://doi.org/10.1007/s42452-025-06923-4

Yaqub, M. Z., & Alsabban, A. (2023). Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies. Sustainability, 15(11), 8553. https://doi.org/10.3390/su15118553