Persona: Camacho López, Ana María
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Camacho López
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Ana María
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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 An Experimental and Numerical Analysis of the Compression of Bimetallic Cylinders(MDPI, 2019-12-07) Herrero, José Manuel; Aragón, Ana María; Lorenzo Martín, Cinta; Yanguas Gil, Ángel; Martins, Paulo A. F.; Camacho López, Ana María; Rodríguez Prieto, Álvaro; Bernal Guerrero, ClaudioThis paper investigates the upsetting of bimetallic cylinders with an aluminum alloy center and a brass ring. The influence of the center-ring shape factor and type of assembly fit (interference and clearance), and the effect of friction on the compression force and ductile damage are comprehensively analyzed by means of a combined numerical-experimental approach. Results showed that the higher the shape factor, the lower the forces required, whereas the effect of friction is especially important for cylinders with the lowest shape factors. The type of assembly fit does not influence the compression force. The accumulated ductile damage in the compression of bimetallic cylinders is higher than in single-material cylinders, and the higher the shape factor, the lower the damage for the same amount of stroke. The highest values of damaged were found to occur at the middle plane, and typically in the ring. Results also showed that an interference fit was more favorable for preventing fracture of the ring than a clearance fit. Microstructural analysis by scanning electron microscopy revealed a good agreement with the finite element predicted distribution of ductile damage.Publicación Selection of die material and its impact on the multi-material extrusion of bimetallic AZ31B–Ti6Al4V components for aeronautical applications(MDPI, 2021-12-09) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaThis paper investigates the effect that the selection of the die material generates on the extrusion process of bimetallic cylindrical billets combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model is developed to analyze the variation in the extrusion force, damage distribution, and wear using different die materials. The results show that die material is a key factor to be taken into account in multi-material extrusion processes. The die material selection can cause variations in the extrusion force from 8% up to 15%, changing the effect of the extrusion parameters, for example, optimum die semi-angle. Damage distribution in the extrudate is also affected by die material, mainly in the core. Lastly, die wear is the most affected parameter due to the different hardness of the materials, as well as due to the variations in the normal pressure and sliding velocity, finding critical values in the friction coefficient for which the die cannot be used for more than one forming stage because of the heavy wear suffered. These 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 Selection of die material and its impact on the multi-material extrusion of bimetallic AZ31B–Ti6Al4V components for aeronautical applications(MDPI, 2021-12-09) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana MaríaThis paper investigates the effect that the selection of the die material generates on the extrusion process of bimetallic cylindrical billets combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model is developed to analyze the variation in the extrusion force, damage distribution, and wear using different die materials. The results show that die material is a key factor to be taken into account in multi-material extrusion processes. The die material selection can cause variations in the extrusion force from 8% up to 15%, changing the effect of the extrusion parameters, for example, optimum die semi-angle. Damage distribution in the extrudate is also affected by die material, mainly in the core. Lastly, die wear is the most affected parameter due to the different hardness of the materials, as well as due to the variations in the normal pressure and sliding velocity, finding critical values in the friction coefficient for which the die cannot be used for more than one forming stage because of the heavy wear suffered. These 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 An Experimental and Numerical Analysis of the Compression of Bimetallic Cylinders(MDPI, 2019-12-07) Herrero, José Manuel; Aragón, Ana María; Lorenzo Martín, Cinta; Yanguas Gil, Ángel; Martins, Paulo A. F.; Camacho López, Ana María; Rodríguez Prieto, Álvaro; Bernal Guerrero, ClaudioThis paper investigates the upsetting of bimetallic cylinders with an aluminum alloy center and a brass ring. The influence of the center-ring shape factor and type of assembly fit (interference and clearance), and the effect of friction on the compression force and ductile damage are comprehensively analyzed by means of a combined numerical-experimental approach. Results showed that the higher the shape factor, the lower the forces required, whereas the effect of friction is especially important for cylinders with the lowest shape factors. The type of assembly fit does not influence the compression force. The accumulated ductile damage in the compression of bimetallic cylinders is higher than in single-material cylinders, and the higher the shape factor, the lower the damage for the same amount of stroke. The highest values of damaged were found to occur at the middle plane, and typically in the ring. Results also showed that an interference fit was more favorable for preventing fracture of the ring than a clearance fit. Microstructural analysis by scanning electron microscopy revealed a good agreement with the finite element predicted distribution of ductile damage.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 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 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 Influence of the Main Blown Film Extrusion Process Parameters on the Mechanical Properties of a High-Density Polyethylene Hexene Copolymer and Linear Low-Density Polyethylene Butene Copolymer Blend Used for Plastic Bags(MDPI, 2023-11-09) Cuesta, Francisco; Camacho López, Ana María; Rubio Alvir, Eva MaríaPolyethylene plastic bags manufactured via blown film extrusion have different quality specifications depending on their intended use. It is known that the mechanical properties of a film depend on the process parameters established, but little is known concerning how they affect one another, even more so due to the variety of polyethylene materials and processing techniques. This study focuses on establishing a proper correspondence of important mechanical properties like the dart impact, tensile strength at break, and elongation at break with commonly used process parameters like the blow-up ratio, take-up ratio, thickness reduction, and neck height, for a high-density polyethylene hexene copolymer and a linear low-density polyethylene butene copolymer blend film. Because this polyethylene mixture is an anisotropic material, interesting R2 values equal to or higher than 0.90 were found: a BUR with elongation at break and tensile strength at break in the MD and TD, a TUR with elongation at break in the MD and tensile strength at break in the MD and TD, and a TR with elongation at break and tensile strength at break in the MD. Also, a relationship between the dart impact and both the neck height and thickness were found.