Persona:
Camacho López, Ana María

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amcamacho@ind.uned.es
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0000-0002-3898-6480
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Camacho López
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Ana María
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2017 - 201942020 - 202516
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Mostrando 1 - 10 de 21
  • Miniatura
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    Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning
    (MDPI, 2021-08-16) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.
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    Computational design methodology for additive manufacturing to enhance customised products and process management efficiency
    (Elsevier, 2025-05-24) García Domínguez, Amabel; Claver Gil, Juan; Camacho López, Ana María; Sebastián Pérez, Miguel Ángel
    The emergence of Additive Manufacturing (AM) provides new horizons for innovation and optimization of designs. However, the limited knowledge of Design for Additive Manufacturing (DfAM) and the constraints imposed by conventional DfAM frameworks are hindering the potential of AM. The scope of recent studies, focused on algorithmic approaches, is limited to certain aspects of the problem and does not offer a comprehensive solution to generate efficient custom designs. To overcome these limitations, the role of interdisciplinary and transversal DfAM methodologies is essential. This work presents a DfAM methodology based on computational design with a continuous dataflow for the integration of all the design phases of a knowledge-based DfAM framework in a single algorithm, from initial design stages to machine code generation, what fully exploits the potential of additive technologies in customized products. This facilitates the integration of key data relationships and connections, promoting the development of intelligent and efficient open systems. Firstly, a bibliographic review on actual knowledge-based DfAM framework is exposed. Then, the proposed methodology is detailed and discussed. Furthermore, its potential applied to customized parts with specific mechanical requirements is validated through a splint design case study. Finally, the proposed methodology advantages and limitations are discussed.
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    Data-driven automatic validation of phased-array ultrasonic-testing data acquisitions of welds in thick austenitic stainless-steel plates for the ITER vacuum vessel manufacturing
    (Elsevier, 2025-01-22) Ortiz de Zuniga, María; Dans, Andrés; Megna, Tito; Prinja, Nawal; Camacho López, Ana María; Rodríguez Prieto, Álvaro; Universidad Nacional de Educación a Distancia (UNED)
    Phased-Array Ultrasonic Testing (PAUT) is a commonly used technique for rigorous non-destructive testing (NDT) of metallic welds and composite material welds, since it is known for its sensitivity and precision, mainly used in highly demanding industrial applications. Nevertheless, its interpretation is time-consuming and requires a skilled certified inspector, according to ISO 9712. The complexity of the PAUT output interpretation depends on many factors such as the type of material, the length of the welded area and the number of beams configured to be transmitted by the probe at each location. During the weld quality check, one of the main tasks of the inspector as part of the interpretation is to ensure that the acquisition is valid. Unfortunately, validation can only be done manually during the inspection and processing phase of the weld PAUT output, which can be time consuming. In the manufacturing of large and complex components with special materials, late validation of PAUT acquisitions results in large cost overruns and delays. This paper presents an automatic early detection analysis mechanism, which differentiates valid from invalid PAUT weld acquisitions based on the analysis of acquired data. This data analysis has been translated into a successful development, which can be used live in a manufacturing workshop for mechanical components. The International Thermonuclear Experimental Reactor (ITER) Vacuum Vessel has been selected as a case study for the adequacy of this technique to welding, as well as to improve time and cost efficiency of PAUT. This paper brings a new data-driven methodology regarding PAUT acquisition validation, covering a gap in technical-scientific literature, by finding the most important and common features to valid PAUT acquisitions.
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    Guidelines for Selecting Plugs Used in Thin-Walled Tube Drawing Processes of Metallic Alloys
    (MDPI, 2017-12-18) Rubio Alvir, Eva María; Camacho López, Ana María; Pérez, Rául; Marín Martín, Marta María; MDPI
    In this paper, some practical guidelines to select the plug or set of plugs more adequate to carry out drawing processes of thin-walled tubes carried out with fixed conical inner plug are presented. For this purpose, the most relevant input parameters have been considered in this study: the tube material, the most important geometrical parameters of the process (die semiangle, a, and cross-sectional area reduction, r) and the friction conditions (Coulomb friction coefficients, m1, between the die and the tube outer surface, and m2, between the plug and the tube inner surface). Three work-hardening materials are analyzed: the annealed copper UNS C11000, the aluminum UNS A91100, and the stainless steel UNS S34000. The analysis is realized by means of the upper bound method (UBM), modelling the plastic deformation zone by triangular rigid zones (TRZ), under the validated assumption that the process occurs under plane strain conditions. The obtained results allow establishing, for each material, a group of geometrical parameters, friction conditions, a set of plugs that make possible to carry out the process under good conditions, and the optimum plug to carry out the process using the minimum amount of energy. The proposed model is validated by means of an own finite element analysis (FEA) carried out under different conditions and, in addition, by other finite element method (FEM) simulations and real experiments taken from other researchers found in the literature (called literature simulations and literature experimental results, respectively). As a main conclusion, it is possible to affirm that the plug that allows carrying out the process with minimum quantity of energy is cylindrical in most cases.
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    Mechanical performance of 3D-printed TPU auxetic structures for energy absorption applications
    (ELSEVIER, 2025-02-01) Fuentes del Toro, Sergio; Crespo Sánchez, Jorge; Ayllón Pérez, Jorge; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    The emergence of metamaterials and layered structures obtained through additive manufacturing (AM) techniques opens a new paradigm of mechanical properties for advanced applications that need to be explored. This study investigates the mechanical behavior of 3D-printed auxetic structures, fabricated from thermoplastic polyurethane (TPU), under tensile and compressive loads. Utilizing fused deposition modeling (MEX), we examined the influence of printing direction on the anisotropic mechanical properties of TPU, with a particular focus on energy absorption, stress–strain responses, and damping capabilities. The research employs the Ogden model for hyperelastic characterization, demonstrating excellent correlation with experimental data. Thus, the novelty of this work relies on an approach that – with a focus in the precision and accuracy of the mechanical performance assessment – through a robust novel methodology combining the Ogden’s analytical model with numerical simulation provided by Ansys® and experimental tests of tensile and compression allows to comprehensively understand the mechanical performance of novel auxetic structures intended to energy absorption and impact resistance applications. Our findings reveal significant variations in mechanical performance based on printing orientation, with the 0°direction offering superior ductility and strength. These results suggest that optimizing the printing direction is crucial for enhancing the performance of TPU auxetic structures, particularly in applications requiring high impact resistance, energy absorption, and damping. This study contributes to the advancement of 3D printing technology for the development of next-generation materials with potential applications in protective gear, medical devices or damping devices, among others.
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    Analysis of AZ31B -Ti6Al4V bimetallic extrusion by numerical simulation and Taguchi method
    (Cambridge University Press, 2021-06-23) Fernández Bermejo, Daniel; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    This paper investigates the extrusion force and damage induced during an extrusion process to manufacture bimetallic cylinders combining a titanium alloy sleeve (Ti6Al4V) and a magnesium alloy core (AZ31B). A study has been carried out to determine the damage factor distribution through the extrusion process and how this factor together with the extrusion force are influenced by the manufacturing parameters (extrusion ratio, friction and die semi-angle) by means of finite element (FE) simulations. Also, a Taguchi Design of Experiments (DoE) and an Analysis of Variance (ANOVA) have been performed in order to study the influence of each parameter to minimize the extrusion force needed to perform the process and the damage in the extrudate. The results show that damage distribution in the sleeve does not follow any pattern, appearing in different region in a random way. However, in the core the damage always occurs in the region outside the contour of the sleeve, where it reaches the maximum value and afterwards remains stationary during the rest of the process. In the core, damage increases as friction factor does and it is independent of the cross-section reduction for low die semi-angles (15°) and reaches the maximum values for 60° die semi-angle. In both cases, damage and extrusion force, the more relevant factor to obtain minimum values is the die semi-angle.
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    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 María
    In 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.
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    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 María
    In 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%.
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    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 María
    This 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.
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    Analytical and numerical study for selecting polymeric matrix composites intended to nuclear applications
    (SAGE, 2019-12) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    This 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.