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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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Matyunin, V. M., A. Yu Marchenkov, P. V. Volkov, et al. "Conversion of the kinetic indentation diagrams of ball indenter into stress-strain curves for metallic structural materials." Industrial laboratory. Diagnostics of materials 88, no. 2 (2022): 54–63. http://dx.doi.org/10.26896/1028-6861-2022-88-2-54-63.

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A brief review of known approaches to converting diagrams obtained by indentation into tension diagrams is presented. It is noted that most studies on the transformation of kinetic diagrams of indentation of a spherical indenter into tension diagrams are carried out within the limits of uniform deformation using both computational and experimental approaches including the finite element method (FEM) and neural networks. However, we consider that such a transformation from one diagram to another can be fulfilled successfully when using the proper relationship between indentation and tension def

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Mahmoud Ali, Mohamed, Abdel Nasser Mohamed Omran, and Mohamed Abd-El-Hakeem Mohamed. "Prediction the correlations between hardness and tensile properties of aluminium-silicon alloys produced by various modifiers and grain refineries using regression analysis and an artificial neural network model." Engineering Science and Technology, an International Journal 24, no. 1 (2021): 105–11. http://dx.doi.org/10.1016/j.jestch.2020.12.010.

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Feng, Pei, Yuhua Shi, Peng Shang, et al. "Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings." Materials 14, no. 22 (2021): 6781. http://dx.doi.org/10.3390/ma14226781.

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The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposition method in order to solve the mechanical mismatch problem between substrate and coatings. More importantly, a backward propagation (BP) neural network was applied to efficiently optimize the pulse electrodeposition process of Ni–W graded coating. The SEM, EDS, XRD, Vickers hardness tester and Wei

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Meyveci, Ahmet, İsmail Karacan, Hülya Durmuş, and Uğur Çalıgülü. "Artificial Neural Network (ANN) Approach to Hardness Prediction of Aged Aluminium 2024 and 6063 Alloys." Materials Testing 54, no. 1 (2012): 36–40. http://dx.doi.org/10.3139/120.110290.

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KAMBLE, VIKRAM G., SAGAR G. KAMBLE, and RAMESH K. D. "PREDICTION OF FRICTIONAL AND WEAR BEHAVIOR OF ALUMINIUM MATRIX COMPOSITES BY ARTIFICIAL NEURAL NETWORK." Journal of Molecular and Engineering Materials 02, no. 03n04 (2014): 1450004. http://dx.doi.org/10.1142/s225123731450004x.

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Modern technologies require materials with unusual combination of properties that cannot be met by conventional metal alloys, ceramic, etc. In our work, we prepared the samples of metal alloys such as Al 356- TiB 2. Processing of samples is done by artificial neural network (ANN) which is one of the promising fields of research in predicting experimental results. In our investigation we worked on grain size analysis, micro hardness, regression analysis, friction test, wear test and microstructure analysis of samples to describe the materials properties of Al 356- TiB 2.

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Knap, M., J. Falkus, A. Rozman, K. Konopka, and J. Lamut. "The Prediction of Hardenability using Neural Networks." Archives of Metallurgy and Materials 59, no. 1 (2014): 133–36. http://dx.doi.org/10.2478/amm-2014-0021.

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Abstract The objective of the research that has been presented was to model the effect of differences in chemical composition within one steel grade on hardenability, with a very broad and heterogeneous database used for studying hardness predictions. This article presents the second part of research conducted with neural networks. In the previous article [1] the most influential parameters were defined along with their weights and on the basis of these results, an improved model for predicting hardenability was developed. These developed neural networks were applied to model predictions of ha

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Chan, Billy, Malcolm Bibby, and Neal Holtz. "Predicting HAZ Hardness with Artificial Neural Networks." Canadian Metallurgical Quarterly 34, no. 4 (1995): 353–56. http://dx.doi.org/10.1179/cmq.1995.34.4.353.

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Vivekanandhan, M., K. Rajmohan, and C. Senthilkumar. "Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network." Surface Topography: Metrology and Properties 10, no. 1 (2022): 015007. http://dx.doi.org/10.1088/2051-672x/ac4a44.

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Abstract Aluminum matrix composites (AMCs) are gaining increasing attention from various industries due to their lightweight and more excellent wear resistance than conventional materials. Manufacturers embracing that difficulty in machining MMC due to reinforcing particles abrasive nature shorten the tool life. Electro-discharge machining (EDM) is an enormously used non-conventional process to remove material in die making, aerospace, and automobile industries and machine any material with the highest hardness. Hence in the present study, EDM was performed on an aluminium alloy 8081 (AA8081)

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Krajewski, A., W. Włosiński, T. Chmielewski, and P. Kołodziejczak. "Ultrasonic-vibration assisted arc-welding of aluminum alloys." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 4 (2012): 841–52. http://dx.doi.org/10.2478/v10175-012-0098-2.

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Abstract The structure and hardness of the surface-welds and fusion-welds made on a 2017A aluminum alloy waveguide using the MIG and TIG methods with and without the participation of ultrasonic vibrations were examined. Cross-sections of the fusions and surface-welds thus obtained were observed in a microscope and the hardness distributions were determined. The aim of the study was to analyze the effects of the ultrasonic vibrations applied to the melted metal pool by a vibrating substrate which in our experiments was a waveguide. The interactions of the ultrasonic vibrations with the molten m

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Erygin, E., and T. Duyun. "DEVELOPMENT OF NEURAL NETWORKS FOR FORECASTING ROUGHNESS WHEN MILLING VARIOUS MATERIALS." Bulletin of Belgorod State Technological University named after. V. G. Shukhov 5, no. 11 (2020): 113–24. http://dx.doi.org/10.34031/2071-7318-2020-5-11-113-124.

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The article presents a methodology for the development and testing results of artificial neural networks for predicting roughness in finishing milling. Experimental data of various researchers in the processing of materials with different physical and mechanical properties are used as the initial data base for the creation and training of neural networks. The value of material hardness is taken as the main identifier of physical and mechanical properties. In addition to the hardness of the material, the input parameters of the nets are also the cutting modes: tool feed, depth and cutting speed

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Pookamnerd, Yodprem, Thanatep Phatungthane, and Chuthong Summatta. "Optimized GMAW parameters for enhancing mechanical properties of dissimilar AA6061 and AA7075 alloy welds using hybrid ANN-GA approach." EUREKA: Physics and Engineering, no. 2 (March 28, 2025): 166–80. https://doi.org/10.21303/2461-4262.2025.003504.

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Connecting aluminum alloys AA6061 and AA7075 presents significant challenges due to differences in thermal behaviors and metallurgical characteristics, often causing issues like cracking or warping during welding. Gas Metal Arc Welding (GMAW) is commonly used for aluminum alloys, but optimizing welding parameters for high-quality joints remains complex. Traditional methods are often inefficient and inadequate in multi-objective scenarios. Recent advancements in artificial intelligence (AI) offer promising alternatives, but applying AI in GMAW optimization for dissimilar aluminum alloys is stil

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Muttineni, Sasidhar, and Pandu R. Vundavilli. "Modeling of Friction Stir Welding of AL7075 Using Neural Networks." International Journal of Applied Evolutionary Computation 3, no. 1 (2012): 66–79. http://dx.doi.org/10.4018/jaec.2012010104.

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Friction stir welding (FSW) is a solid state welding process, which is used for the welding of aluminum alloys. It is important to note that the mechanical properties of the FSW process depends on various process parameters, such as spindle speed, feed rate and shoulder depth. Two different tool materials, such as High speed steel (HSS) and H13 are considered for the welding of Al 7075. The present paper deals with the modeling of FSW process using neural networks. A three layered feed forward neural network (NN) has been used to model the FSW of aluminum alloys. It is important to note that t

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Ved’, M. V., M. D. Sakhnenko, V. V. Shtefan, S. B. Lyon, S. V. Oleinyk, and L. M. Bilyi. "Computer modeling of the nonchromate treatment of aluminum alloys by neural networks." Materials Science 44, no. 2 (2008): 216–21. http://dx.doi.org/10.1007/s11003-008-9066-2.

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Mincheva, Desislava Yordanova, and Georgi Stefanov Antonov. "Artificial neural network (ANN) approach to predicting micro hardness profile values of iron-based sintered alloys." ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 1, no. 1 (2017): 1–5. http://dx.doi.org/10.29114/ajtuv.vol1.iss1.23.

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Recent interest in artificial neural networks has considerably extended their use in the field of powder metallurgy. Advanced in the paper is a model for predicting the micro hardness of sintered compacts made from iron powders and powder mixtures through the process of sintering performed in different atmospheres. The proposed model is based on three layer neural network with backpropagation learning algorithm. Specially developed software has been used to provide for the proper functioning of the neural network. Moreover, it should also be noted that the training data used to carry out the r

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Buitrago Diaz, Juan C., Carolina Ortega-Portilla, Claudia L. Mambuscay, Jeferson Fernando Piamba, and Manuel G. Forero. "Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks." Metals 13, no. 8 (2023): 1391. http://dx.doi.org/10.3390/met13081391.

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The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural network

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Meghlaoui, A., R. T. Bui, L. Tikasz, J. Thibault, and R. Santerre. "Predictive control of aluminum electrolytic cells using neural networks." Metallurgical and Materials Transactions B 29, no. 5 (1998): 1007–19. http://dx.doi.org/10.1007/s11663-998-0069-z.

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An, Sungbin, Juyeon Han, Seoyeon Jeon, Dowon Kim, Jae Bok Seol, and Hyunjoo Choi. "Development of Aluminum Alloys for Additive Manufacturing Using Machine Learning." Journal of Powder Materials 32, no. 3 (2025): 202–11. https://doi.org/10.4150/jpm.2025.00150.

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The present study introduces a machine learning approach for designing new aluminum alloys tailored for directed energy deposition additive manufacturing, achieving an optimal balance between hardness and conductivity. Utilizing a comprehensive database of powder compositions, process parameters, and material properties, predictive models—including an artificial neural network and a gradient boosting regression model, were developed. Additionally, a variational autoencoder was employed to model input data distributions and generate novel process data for aluminum-based powders. The similarity

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Smokvina Hanza, Sunčana, Tea Marohnić, Dario Iljkić, and Robert Basan. "Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance." Metals 11, no. 5 (2021): 714. http://dx.doi.org/10.3390/met11050714.

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Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs

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Chun, M. S., J. Biglou, J. G. Lenard, and J. G. Kim. "Using neural networks to predict parameters in the hot working of aluminum alloys." Journal of Materials Processing Technology 86, no. 1-3 (1999): 245–51. http://dx.doi.org/10.1016/s0924-0136(98)00318-5.

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Zafar, Muhammad Hamza, Hassaan Bin Younis, Majad Mansoor, Syed Kumayl Raza Moosavi, Noman Mujeeb Khan, and Naureen Akhtar. "Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys." Materials 15, no. 18 (2022): 6198. http://dx.doi.org/10.3390/ma15186198.

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Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordanc

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Raja, V. L., A. M. Senthil Kumar, K. Shantha Kumari, et al. "Analytical and Neural Network Analysis on Flux-Coated Aluminium Alloy by Activated TIG Welding with Synthesized Nanocomposites." Journal of Nanomaterials 2023 (February 20, 2023): 1–11. http://dx.doi.org/10.1155/2023/3657314.

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This research focused to synthesize the material by the tungsten inert gas (TIG) welding process with support of appropriate flux coating material. Therefore the required amount of flux coating material was utilized to enhance the mechanical properties of the specified localized welded regions. Hence, this study concentrated to select the nano-SiO2 flux particles that were employed for TIG process. This activated TIG welding composes the flux-coated welding on the base metal of AA5083-H111, as this material was highly reactive with SiO2 by the presence of magnesium precipitates and well synthe

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Younis, Hassaan Bin, Khurram Kamal, Muhammad Fahad Sheikh, and Amir Hamza. "Prediction of fatigue crack growth rate in aircraft aluminum alloys using optimized neural networks." Theoretical and Applied Fracture Mechanics 117 (February 2022): 103196. http://dx.doi.org/10.1016/j.tafmec.2021.103196.

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van der Wolk, Pieter J., Jiajun Wang, Jilt Sietsma, and Sybrand van der Zwaag. "Modelling the continuous cooling transformation diagram of engineering steels using neural networks." International Journal of Materials Research 93, no. 12 (2002): 1208–16. http://dx.doi.org/10.1515/ijmr-2002-0209.

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Abstract The neural network model of Van der Wolk et al. [1] describes the effect of composition on the phase regions of the continuous cooling transformation (CCT) diagram, yet does not consider the fractions of microstructural components and the hardness data that are often quoted in CCT diagrams. In the present paper, the construction of two more neural network models, one for the fractions of ferrite, pearlite, bainite and martensite in the microstructure, and one for the hardness after cooling, using the data of 338 and 412 diagrams, respectively. The accuracy of each model was found to b

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Soundararajan, R., A. Ramesh, S. Sivasankaran, and A. Sathishkumar. "Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique." Advances in Materials Science and Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/714762.

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Artificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD). The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed f

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Sharath, Ballupete Nagaraju, Channarayapattana Venkataramaiah Venkatesh, Asif Afzal, et al. "Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks." Materials 14, no. 11 (2021): 2895. http://dx.doi.org/10.3390/ma14112895.

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Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B4C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B4C and Gr particles led to continuous improvements in wear resistance. The micros

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Hassanin, Hany, Yahya Zweiri, Laurane Finet, Khamis Essa, Chunlei Qiu, and Moataz Attallah. "Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo Alloy and Properties Prediction Using Deep Learning Approaches." Materials 14, no. 8 (2021): 2056. http://dx.doi.org/10.3390/ma14082056.

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Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy sam

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Huang, Tianhao, Xueyuan Li, Yongzhen Zhang, Leijiang Yao, and Tao Zhang. "Multifactorial prediction of corrosion fatigue crack growth in aluminum alloys using physics-informed neural networks." Engineering Failure Analysis 174 (June 2025): 109521. https://doi.org/10.1016/j.engfailanal.2025.109521.

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Hijazi, Ala, Sameer Al-Dahidi, and Safwan Altarazi. "Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks." Materials 13, no. 22 (2020): 5216. http://dx.doi.org/10.3390/ma13225216.

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Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test pane

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Kumar, K. J. Santosh, Ganesh Arjun Bhargav, Yuvaraja Naik, and K. Bommanna. "Friction Stir Welding of Different Aluminum-Silicon Alloy Compositions Utilizing Conventional Vertical Milling Machine." Journal of Computers, Mechanical and Management 1, no. 1 (2022): 30–41. http://dx.doi.org/10.57159/gadl.jcmm.1.1.22012.

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Friction-Stir Welding (FSW) is a solid-state procedure for welding two plates in which there is relative motion between the tool and workpiece, which creates the heat required for the material of the two edges to join by atomic diffusion. The present research article focuses on friction stir welding of dissimilar aluminum-silicon alloys utilizing a vertical milling machine and altering process parameters. Moreover, testing is done on the weld joints for the best process parameter. The process parameters considered in the present work for joining dissimilar aluminum alloys primarily were a cons

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Campanella, B., E. Grifoni, S. Legnaioli, et al. "Classification of wrought aluminum alloys by Artificial Neural Networks evaluation of Laser Induced Breakdown Spectroscopy spectra from aluminum scrap samples." Spectrochimica Acta Part B: Atomic Spectroscopy 134 (August 2017): 52–57. http://dx.doi.org/10.1016/j.sab.2017.06.003.

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Meghlaoui, A., R. T. Bui, L. Tikasz, J. Thibault, and R. Santerre. "Intelligent control of the feeding of aluminum electrolytic cells using neural networks." Metallurgical and Materials Transactions B 28, no. 2 (1997): 215–21. http://dx.doi.org/10.1007/s11663-997-0087-2.

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R.M.M. Alenzi, Ahmad, and S. S. Mohammed. "MODELLING OF THERMAL DRILLING OF AA7075 ALUMINUM ALLOYS USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS TECHNIQUES." Engineering Research Journal - Faculty of Engineering (Shoubra) 49, no. 1 (2021): 60–66. http://dx.doi.org/10.21608/erjsh.2021.227490.

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Merayo, David, Alvaro Rodríguez-Prieto, and Ana María Camacho. "Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning." Metals 11, no. 8 (2021): 1289. http://dx.doi.org/10.3390/met11081289.

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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

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XIE, YUMING, XIANGCHEN MENG, and YONGXIAN HUANG. "Entire-Process Simulation of Friction Stir Welding — Part 2: Implementation of Neural Networks." Welding Journal 101, no. 6 (2022): 172–77. http://dx.doi.org/10.29391/2022.101.013.

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To further understand the structure-parameter-property relationships of friction stir welded aluminum alloy joints, a nested neural network was proposed to map the macro- and microstructural response. The uncoupled effect of each primitive parameter on the joint performance was depicted. Reducing heat input and keeping an adequate load-bearing area of the welding nugget zone were proven to be the sufficient and necessary conditions to obtain high load-bearing performance. The entire-process simulation strategy showed great potential for prediction and optimization of the macro- and microstruct

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Nikolić, Filip, Ivan Štajduhar, and Marko Čanađija. "Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys." Metals 11, no. 5 (2021): 756. http://dx.doi.org/10.3390/met11050756.

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This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. The model was trained on images of polished samples of high-pressure die-cast alloy EN AC 46000 AlSi9Cu3(Fe), the gravity die cast alloy EN AC 51400 AlMg5(Si) and the alloy cast as ingots EN AC 42000 AlSi7Mg. Color images were converted to grayscale to reduce the number of training parameters. It is shown that a relatively simple CNN structure can p

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Mosleh, Ahmed O., Elena G. Kotova, Anton D. Kotov, Iosif S. Gershman, and Alexander E. Mironov. "Bearing Aluminum-Based Alloys: Microstructure, Mechanical Characterizations, and Experiment-Based Modeling Approach." Materials 15, no. 23 (2022): 8394. http://dx.doi.org/10.3390/ma15238394.

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Due to the engine’s start/stop system and a sudden increase in speed or load, the development of alloys suitable for engine bearings requires excellent tribological properties and high mechanical properties. Including additional elements in the Al-rich matrix of these anti-friction alloys should strengthen their tribological properties. The novelty of this work is in constructing a suitable artificial neural network (ANN) architecture for highly accurate modeling and prediction of the mechanical properties of the bearing aluminum-based alloys and thus optimizing the chemical composition for hi

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Alvaro, Mariana Soares, Joao Victor Santana de Oliveira, Monica Costa Rezende, Ana Isabel de Carvalho Santana, Luiz Henrique de Almeida, and Sinara Borborema. "MECHANICAL CHARACTERIZATION OF HOMOGENIZED TI-12MO-13NB AND TI-10MO-20NB ALLOYS." Revista Contemporânea 4, no. 11 (2024): e6451. http://dx.doi.org/10.56083/rcv4n11-025.

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Beta titanium alloys have been developed for biomedical applications due to their outstanding mechanical properties, including low elastic modulus, high strength, excellent fatigue resistance, good ductility, and exceptional corrosion resistance. To enhance safety, the metastable beta titanium alloys Ti-12Mo-13Nb and Ti-10Mo-20Nb have been introduced as alternatives to the traditional Ti-6Al-6V alloy. The Ti-6Al-6V alloy contains vanadium, which is biotoxic and can lead to cell death, and aluminum, which may contribute to neural cell degeneration and accelerate the progression of Alzheimer’s d

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Dharmadhikari, Susheel, and Amrita Basak. "Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks." Machine Learning with Applications 7 (March 2022): 100247. http://dx.doi.org/10.1016/j.mlwa.2021.100247.

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Sun, Jianhang, Yepeng Xu, and Lei Wang. "Evaluation of the Elastic Modulus and Plateau Stress of a 2D Porous Aluminum Alloy Based on a Convolutional Neural Network." Metals 13, no. 2 (2023): 284. http://dx.doi.org/10.3390/met13020284.

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Porous metals are a new ultra-light material with high specific stiffness, specific strength, and good energy absorption properties. The elastic modulus and plateau stress of porous metals are essential parameters. There have been many studies on the effects of the matrix material, porosity, and pore size on the elastic modulus and plateau stress of porous metals, but few studies can be found on the impact of pore arrangement. The pore arrangement of porous metals cannot be quantitatively described, and the design space of a porous metal structure under the same porosity is vast. With the powe

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Di Bella, Guido, Federica Favaloro, and Chiara Borsellino. "Effect of Process Parameters on Friction Stir Welded Joints between Dissimilar Aluminum Alloys: A Review." Metals 13, no. 7 (2023): 1176. http://dx.doi.org/10.3390/met13071176.

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Friction Stir Welding is a suitable solid-state joining technology to connect dissimilar materials. To produce an effective joint, a phase of optimization is required which leads to the definition of process parameters such as pin geometry, tool rotational speed, rotation direction, welding speed, thickness of the sheets or tool tilt angle. The aim of this review is to present a complete and detailed frame of the main process parameters and their effect on the final performance of a friction stir welded joint in terms of mechanical properties and microstructure. Attention was focused in partic

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