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Relevant bibliographies by topics / Aluminum alloys; hardness; neural networks

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Academic literature on the topic 'Aluminum alloys; hardness; neural networks'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Aluminum alloys; hardness; neural networks"

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B., Zahran. "Using Neural Networks to Predict the Hardness of Aluminum Alloys." Engineering, Technology & Applied Science Research 5, no. 2 (2015): 757–59. https://doi.org/10.5281/zenodo.14903.

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Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough data

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Zahran, B. "Using Neural Networks to Predict the Hardness of Aluminum Alloys." Engineering, Technology & Applied Science Research 5, no. 1 (2015): 757–59. http://dx.doi.org/10.48084/etasr.529.

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Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough data

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Merayo, David, Alvaro Rodríguez-Prieto, and Ana María Camacho. "Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys." Materials 13, no. 22 (2020): 5227. http://dx.doi.org/10.3390/ma13225227.

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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

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Bataineh, Omar, and Mohammad Smadi. "Using Artificial Neural Networks to Predict Hardness and Impact Toughness of Aluminum Alloy 6061-T6." Materials Science Forum 1079 (December 26, 2022): 3–13. http://dx.doi.org/10.4028/p-3l7vo5.

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Predicting the material's mechanical properties is essential for reducing testing time, cost, and effort. In this study, the effect of temperature and holding time on the hardness and impact toughness of Al 6061 was investigated using the design of experiments (DOE) methodology. Analysis of variance (ANOVA) was used to analyze the results of DOE-factorial experiments. Two factors with five replicates were studied in the experiments: temperature with four levels (393.15, 423.15, 453.15, and 483.15 oK) and holding time with four levels (60, 120, 180, and 240 min). An artificial neural network (A

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Crăciun, Răzvan Sebastian, Virgil Gabriel Teodor, Nicușor Baroiu, Viorel Păunoiu, and Georgiana-Alexandra Moroșanu. "Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351." Machines 12, no. 12 (2024): 937. https://doi.org/10.3390/machines12120937.

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Duralumin 2024-T351 is an alloy characterized by a good mechanical strength, relatively high hardness and corrosion resistance frequently used in the aeronautical, automotive, defense etc. industries. In this paper, the variation of axial forces and torques when drilling aluminum alloy 2024-T351 was investigated, analyzing the measured values for different cutting regimes. Experimental data on the forces and moments generated during the drilling process were collected using specialized equipment, and these data were preprocessed and analyzed using MatLab R218a. The experimental plan included 2

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Ren, J. P., and R. G. Song. "Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks." Advanced Materials Research 217-218 (March 2011): 1458–61. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1458.

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In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms. A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using si

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Campana, Rodrigo C., P. C. Vieira, and R. L. Plaut. "Applicability of Adaptive Neural Networks (ANN) in the Extrusion of Aluminum Alloys and in the Prediction of Hardness and Internal Defects." Materials Science Forum 638-642 (January 2010): 303–9. http://dx.doi.org/10.4028/www.scientific.net/msf.638-642.303.

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Adaptive Neural Networks (ANN) can be used in the analysis of a complex panorama of interconnected input/output industrial data, even when they present substantial noise. The ANN, despite presenting substantial mathematical complexity associated with non-linear parameterization (which includes transfer equations and corresponding “training”), are largely used under industrial conditions in several engineering areas (such as in steelmaking), with substantial success. This work shows the applicability of the ANN in a specific case related to the analysis of internal defects of extruded aluminum

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Durmuş, Hülya, Bekir Unlü, and Cevdet Meriç. "Determination of Hardness of Pre-Aged AA 6063 Aluminum Alloy by Means of Artificial Neural Networks Method." Mathematical and Computational Applications 9, no. 2 (2004): 249–56. http://dx.doi.org/10.3390/mca9020249.

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Maleki, Erfan, Okan Unal, Seyed Mahmoud Seyedi Sahebari, Kazem Reza Kashyzadeh, and Nima Amiri. "Enhancing Friction Stir Welding in Fishing Boat Construction through Deep Learning-Based Optimization." Sustainable Marine Structures 5, no. 2 (2023): 1–14. http://dx.doi.org/10.36956/sms.v5i2.875.

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In the present study, the authors have attempted to present a novel approach for the prediction, analysis, and optimization of the Friction Stir Welding (FSW) process based on the Deep Neural Network (DNN) model. To obtain the DNN structure with high accuracy, the most focus has been on the number of hidden layers and the activation functions. The DNN was developed by a small database containing results of tensile and hardness tests of welded 7075-T6 aluminum alloy. This material and the production method were selected based on the application in the construction of fishing boat flooring, beca

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Beytüt, Hüseyin, Kerim Özbeyaz, and Şemsettin Temiz. "A Novel Hybrid Die Design for Enhanced Grain Refinement: Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP)." Applied Sciences 15, no. 1 (2025): 359. https://doi.org/10.3390/app15010359.

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A novel hybrid Severe Plastic Deformation (SPD) method called Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP) was developed and applied to AA6082 workpieces in this study. Before experimental application, a comprehensive optimization of the die design was performed considering effective strain, strain inhomogeneity, and pressing load parameters. The optimization process utilized an integrated approach combining Finite Element Analysis (FEA), artificial neural networks (ANNs), and the non-dominated sorting genetic algorithm II (NSGA-II). The optimized die successfully achieved a balanc

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Dissertations / Theses on the topic "Aluminum alloys; hardness; neural networks"

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Gambina, Federico. "Corrosion Resistance Characterization of Coating Systems Used to Protect Aluminum Alloys Using Electrochemical Impedance Spectroscopy and Artificial Neural Networks." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281361408.

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Book chapters on the topic "Aluminum alloys; hardness; neural networks"

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Rabe, Pascal, Alexander Schiebahn, and Uwe Reisgen. "Volumetric Defect Detection in Friction Stir Welding Through Convolutional Neural Networks Generalized Across Multiple Aluminum-Alloys and Sheet Thicknesses." In Proceedings in Engineering Mechanics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54732-4_4.

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"Advanced data processing of ATF claddings." In Book of Abstracts - RAD 2025 Conference. RAD Centre, Niš, Serbia, 2025. https://doi.org/10.21175/rad.abstr.book.2025.16.1.

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Digital image processing (DIP), neural networks, and artificial intelligence (AI) are revolutionizing materials science, enabling precise and efficient analysis of microscopic features. From nuclear fuel inspections to advanced microscopy studies, DIP has become a cornerstone of material analysis to obtain relevant data quality. At CVR, integrating DIP and AI has streamlined processes, enhanced data reliability, and provided valuable insights in areas such as various microscopy studies (SEM, TEM), reactor shielding evaluations and nuclear fuel inspections based on image data processing with di

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Conference papers on the topic "Aluminum alloys; hardness; neural networks"

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Sonmez, Fikret, Hudayim Basak, and Sehmus Baday. "The mechanical strength of aluminum alloys which are joined with friction stir welding modelling with artificial neural networks." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090325.

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Gonchikar, Ugrasen, Holalu Venkatadasu Ravindra, Prathik Jain Sudhir, Umeshgowda Bettahally Mahadevegowda, and Shankarnarayan Maskibail Suresh. "Estimation and Comparison of Welding Responses Using MRA, GMDH and ANN Technique of Al6061 and Al7075 Material in FSW." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11168.

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Abstract Friction Stir Welding (FSW) is a solid state welding which uses non-consumable steel rod to weld two materials. Friction stir welding is an emerging process which is based on frictional heat generated through contact between a non-consumable rotating tool and work piece. Friction stir welding technique possesses several advantages over other conventional types of welding due to the fact that process is carried out in solid state. Removal of melting helps in minimizing porosity and eliminates oxide inclusion. In this study, we focus on the optimization of the process parameters in fric

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Pashchenko, Oleksandr, Oleksandr Kamyshatskyi, Elmira Omirzakova, and Sara Ratova. "Development and optimization of hard alloy compositions for rock destruction." In 24th International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies, 2025. https://doi.org/10.22616/erdev.2025.24.tf110.

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Drilling tools operating under extreme conditions face high temperatures, pressure, abrasive wear, and corrosion, leading to rapid wear and failure. Traditional hard alloys, such as those containing 85% tungsten carbide and 15% cobalt, often fail under such stresses, increasing equipment replacement costs and reducing drilling efficiency. To address this issue, machine learning methods were employed to develop new alloys with enhanced properties. Data on the composition and properties of various hard alloys, including elemental percentages, mechanical properties, and test results, were collect

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Hunt, Johnathon, Brigham Larsen, and Yuri Hovanski. "In Line Nondestructive Testing for Sheet Metal Friction Stir Welding." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0069.

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<div class="section abstract"><div class="htmlview paragraph">As automotive designs add more aluminum to lightweight their vehicles, friction stir welding (FSW) will likely become a principal joining process in the industry. FSW is a solid-state joining process which avoids many of the traditional problems of welding aluminum alloys such as hot cracking, porosity and solidification shrinkage. These attributes enable high preforming friction stir welded joints of cast, 5XXX, 6XXX, 7XXX or mixed aluminum alloy combinations. Although FSW technologies have advanced to support high volu

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Monaco, E. "Deep learning algorithms for delamination identification on composites panels by wave propagation signals analysis." In Aeronautics and Astronautics. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902813-90.

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Abstract. Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption as possible. Composite materials replaced aluminum alloys even in primary structures to achieve higher performances with lighter components. However, random events such as low-velocity impacts may induce damages that are typically more dangerous and mostly not visible than in metals. Structural Health Monitoring deals mainly with sensorised structures providing signals related to their “health status” aiming at lower maintenance costs and weights of aircrafts. Much effort has b

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