Fabrication and prediction of tensile strength of Al-Al2O3 nano composites

Harsha, N.; Raju, K. Sita Rama; Ramana, V.S.N. Venkata; Reddy, V Venkata

doi:10.1016/j.matpr.2019.07.543

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The Levenberg-Newton method algorithm trained an ANN with a 1-hidden-layer, 8-neuron architecture. In the research by Harsha et al [104], ANN models were developed to predict the tensile strength of Al-Al 2 O 3 nanocomposites fabricated through powder metallurgy. Achieving a 98% accuracy with a 5-5 hidden layer architecture, the study highlighted the significant impact of the number of neurons and hidden layers on ANN model efficacy.…”

Section: Bo Dnn [89]mentioning

confidence: 99%

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Nandipati,

Fatoki,

Desai

2024

Materials

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Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

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Section: Bo Dnn [89]mentioning

confidence: 99%

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Nandipati,

Fatoki,

Desai

2024

Materials

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“…Young's modulus prediction [105] Random forest (RF) Microscale elastic strain field prediction [106] GAN Microstructure and mechanical properties relation of composite materials [37] ANN and linear regression (LIR) Prediction of mechanical behavior from microstructures [107] Accumulative roll bonding (ARB) Generation of composition and tensile properties prediction [108] ANN Prediction of non-linear structural deformations [109] Recurrent neural network (RNN) Prediction of material plasticity [110] Convolutional LSTM Prediction of fracture behavior [49,111] ANN Prediction of tensile strength of nanocomposites [112] ANN Prediction of flexural modulus and bending shear of nanocomposites [113] Compositionally complex materials, soft matter, and metamaterials Hybrid active-learning Design of high entropy alloys [114] RF Prediction of thermodynamic and composition for generation of ceramic materials [115] Reinforced learning for structural evolution (ReLeaSE) Design of de novo molecules [116] ANN Prediction complex systems behavior [117] Gaussian process regression (GPR) Prediction of tensile strength of polymer-CNT composites [118] ANN Prediction of thermal properties of conductive nanocomposites [119] Integrated deep neural network (DNN) Prediction of stress-strain responses of porous metamaterials [120] CNN Optimal structural design of 2D metamaterials [121]…”

Section: Model/ai Algorithm Common Issues Covered Referencesmentioning

confidence: 99%

“…To this end, Harsha and colleagues [112] employed an ANN to prognosticate the tensile strength of Al/Al 2 O 3 nanocomposites. By feeding the ANN with inputs including the quantity of Al 2 O 3 nanoparticles, the hardness, and the elongation percentage, the AI algorithm adeptly gauged the strength, aligning it with the weight percentage of reinforcement.…”

Section: Ai Algorithms In Complex Materials Soft Matter and Metamater...mentioning

confidence: 99%

Unleashing the Power of Artificial Intelligence in Materials Design

Badini,

Regondi,

Pugliese

2023

Materials

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The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.

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“…Because of the widespread incorporation of Al 2 O 3 , TiC, SiC, and B4C particles into the metallic matrix, ML has already been extensively applied in MMCs [24,30]. The mechanical properties of AMCs with various additions, including Al/Al 2 O 3 [31][32][33], AA2219/Al 2 O 3 /TiC [34], A356/Al 2 O 3 [35], A356/B4C [36], AA6061/Al 2 O 3 /SiC [37], and Al-Si-Mg/Al 2 O 3 /SiC [38,39], have all been predicted using ML. Adding reinforcement particles to the base metal makes it more complicated.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

et al. 2023

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This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2xxx series) was employed in the current study, and Al2O3 was used as a reinforcement. The performance evaluation of the Al-4Cu/Al2O3 composite involved analyzing the influence of the Al2O3 weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al2O3 reinforcement, namely 0%, 2.5%, 5.0%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4Cu/Al2O3 composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4Cu/Al2O3 composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results.

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Fabrication and prediction of tensile strength of Al-Al2O3 nano composites

Cited by 6 publications

References 14 publications

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Unleashing the Power of Artificial Intelligence in Materials Design

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

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