Persona: Rodríguez Prieto, Álvaro
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Rodríguez Prieto
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Publicación Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data(MDPI, 2020-07-06) Merayo Fernández, David; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaAluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5𝑘 entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95% . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.Publicación Analytical and numerical study for selecting polymeric matrix composites intended to nuclear applications(SAGE, 2019-12) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaThis study describes a methodological proposal to select composite materials which are suitable to be employed to manufacture pipes that can properly withstand environments subjected to gamma and neutronic radiation. The methodology is used to select, among many others, the optimal composite material whose properties are used afterwards to simulate several pipe sections by finite element analysis, comparing the results with a well-known nuclear-grade steel, WWER 15Kh2MFAA. The most suitable composite material according to the defined criteria is composed of a phenolic resin matrix reinforced with long boron fibres and exhibit great properties to be used in a nuclear reactor environment: good radiation resistance and mechanical properties with a very low density at low cost. It can be concluded that, in some cases, composite material pipes can be a better option than steel ones. Extending the method to be employed in other industries or with other components could be seen as future works.Publicación Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks(IEEE, 2020-01-10) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaIn this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively.Publicación Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys(MDPI, 2020-10-02) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaIn metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.Publicación Reliability Prediction of Acrylonitrile O-Ring for Nuclear Power Applications Based on Shore Hardness Measurements(MDPI, 2021-03-19) Primera, Ernesto; Frigione, Maríaenrica; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaThe degradation of polymeric components is of considerable interest to the nuclear industry and its regulatory bodies. The objective of this work was the development of a methodology to determine the useful life—based on the storage temperature—of acrylonitrile O-rings used as mechanical sealing elements to prevent leakages in nuclear equipment. To this aim, a reliability-based approach that allows prediction of the use-suitability of different storage scenarios (that involve different storage times and temperatures) considering the further required in-service performance, is presented. Thus, experimental measurements of Shore A hardness have been correlated with storage variables (temperature and storage time). The storage (and its associated hardening) was proved to have a direct effect on in-service durability, reducing this by up to 60.40%. Based on this model, the in-service performance was predicted; after the first three years of operation the increase in probability of failure (POF) was practically insignificant. Nevertheless, from this point on, and especially, from 5 years of operation, the POF increased from 10% to 20% at approximately 6 years (for new and stored). From the study, it was verified that for any of the analysis scenarios, the limit established criterion was above that of the storage time premise considered in usual nuclear industry practices. The novelty of this work is that from a non-destructive test, like a Shore A hardness measurement, the useful life and reliability of O-rings can be estimated and be, accordingly, a decision tool that allows for improvement in the management of maintenance of safety-related equipment. Finally, it was proved that the storage strategies of our nuclear power plants are successful, perfectly meeting the expectations of suitability and functionality of the components when they are installed after storage.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.Publicación Effect of Process Parameters and Definition of Favorable Conditions in Multi-Material Extrusion of Bimetallic AZ31B–Ti6Al4V Billets(MDPI) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaThis paper investigates the extrusion process to manufacture bimetallic cylinders combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model has been developed to determine the most influential parameters and to study the effect of them on the extrusion force and damage induced by means of Design of Experiments (DOE) and Taguchi method. The results show that the most influential parameters in the extrusion forces are the friction between sleeve and container/die and the height of the cylinder; and the less influential ones are the process temperature and ram speed. Moreover, minimum values of forces along with low damage can be reached by favorable interface contact conditions, minimizing the friction at the core-container/die interface, as the main influencing factor; followed by the geometrical dimensions of the billet, being the billet height more important when paying attention to the minimum forces, and being the core diameter when considering the minimum damage as the most important criterion. The results can potentially be used to improve the efficiency of this kind of extrusion process and the quality of the extruded part that, along with the use of lightweight materials, can contribute to sustainable production approaches.Publicación Analysis of Favourable Process Conditions for the 2 Manufacturing of Thin-Wall Pieces of Mild Steel 3 Obtained by Wire and Arc Additive Manufacturing 4 (WAAM)(2018) Prado Cerqueira, José Luis; Diéguez, José Luis; Aragón, Ana María; Lorenzo Martín, Cinta; Yanguas Gil, Ángel; Rodríguez Prieto, ÁlvaroOne of the challenges in additive manufacturing (AM) of metallic materials is to obtain workpieces free of defects with excellent physical, mechanical, and metallurgical properties. In wire and arc additive manufacturing (WAAM) the influences of process conditions on thermal history, microstructure and resultant mechanical and surface properties of parts must be analyzed. In this work, 3D metallic parts of mild steel wire (American Welding Society-AWS ER70S-6) are built with a WAAM process by depositing layers of material on a substrate of a S235 JR steel sheet of 3 mm thickness under different process conditions, using as welding process the gas metal arc welding (GMAW) with cold metal transfer (CMT) technology, combined with a positioning system such as a computer numerical controlled (CNC) milling machine. Considering the hardness profiles, the estimated ultimate tensile strengths (UTS) derived from the hardness measurements and the microstructure findings, it can be concluded that the most favorable process conditions are the ones provided by CMT, with homogeneous hardness profiles, good mechanical strengths in accordance to conditions defined by standard, and without formation of a decohesionated external layer; CMT Continuous is the optimal option as the mechanical properties are better than single CMT.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 Optimal Parameters Selection in Advanced Multi-Metallic Co-Extrusion Based on Independent MCDM Analytical Approaches and Numerical Simulation(MDPI, 2022-11-28) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaMulti-material co-extrusion is a complex thermo-mechanical forming process used to obtain bimetallic billets. Its complexity is due to the combination of diffusion phenomena in the interface of both materials together with the high temperature and pressure generated and the different flow stress characteristics created by the joining of dissimilar materials. Accordingly, the selection of optimal process parameters becomes key to ensure process feasibility. In this work, a comparison among different multi-criteria decision making (MCDM) methodologies, together with different eighting methods, were applied to the simulation results by using DEFORM3D© software to select the optimal combination of process parameters to fulfil the criteria of minimum damage, extrusion force, and tool wear, together with the maximum reduction in the average grain size.