Persona: Fernández Bermejo, Daniel
Cargando...
Dirección de correo electrónico
ORCID
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Fernández Bermejo
Nombre de pila
Daniel
Nombre
2 resultados
Resultados de la búsqueda
Mostrando 1 - 2 de 2
Publicación Data analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloys(MDPI, 2024-03-10) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaAbstract: Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) meth- odologies, specifically Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the prediction model and the time for data preprocessing is less than the time needed in SVR. This new methodology is proven to be effective as alternative to the traditional approaches and aligned with the new trends in the industry based on simulation and data analytics.Publicación Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure(MDPI, 2024-06-17) Primera, Ernesto; Cacereño, Andrés; Fernández Bermejo, Daniel; Rodríguez Prieto, ÁlvaroRoller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.