Process optimization for enhanced tribological properties of Al/MWCNT composites produced by powder metallurgy using artificial neural networks

Turkoglu, Turker; Çelik, Sare

doi:10.1088/2051-672x/ac3a53

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…In the literature, it is possible to identify several production routes for MMNCs. Still, powder metallurgy is an efficient, simple, accessible, and widely used technique for manufacturing these nanocomposites [10][11][12]. The powder metallurgy process can be subdivided into four key steps, starting with the powder mixing and dispersion with the metal powder and reinforcement, followed by the pressing and the sintering of the samples [13].…”

Section: Introductionmentioning

confidence: 99%

Production and Characterization of Cu/CNT Nanocomposites

et al. 2023

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In this research, copper nanocomposites reinforced with carbon nanotubes (CNTs) were produced by ultrasonication and conventional sintering, followed by cold rolling. These nanocomposites may be good candidates due to their excellent properties for components in the electrical, electronics, or aerospace industries with highly demanding requirements. The main objectives of this work were to produce and characterize the Cu/CNT nanocomposites, identify the strengthening mechanisms, and study the deformation behavior of the nanocomposites during cold rolling. The nanocomposites exhibited an improvement in hardness and tensile strength of 17 and 67%, respectively, attesting to the strengthening effect of the reinforced material. The yield strength of the nanocomposites was determined considering different mechanisms: (1) load transfer, (2) grain refinement or texture, (3) dislocation, and (4) Orowan strengthening mechanisms. The microstructural and calculated results show that the mechanism that contributes the most to the increase in the properties of the nanocomposite is the load transfer. The nanocomposites show a different texture evolution of the Cu matrix during cold rolling. This can be due to differences in the active slip planes between the matrix and the nanocomposite, which affects the lattice rotation.

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Section: Introductionmentioning

confidence: 99%

Production and Characterization of Cu/CNT Nanocomposites

et al. 2023

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“…They observed that the wear properties of the MWCNTs/Al nanocomposites could be accurately assessed using the artificial neural network technique. 20 The development of functionalized multiwalled carbon nanotubes-aluminum (f-MWCNTs/Al) nanocomposite for industrial applications is still in its early stages. As a result, a better understanding of fabrication process parameters is required for industrial applications of this nanocomposite.…”

Section: Introductionmentioning

confidence: 99%

“…They observed that the wear properties of the MWCNTs/Al nanocomposites could be accurately assessed using the artificial neural network technique. 20…”

Section: Introductionmentioning

confidence: 99%

Optimization of MWCNTs/Al nanocomposite fabrication process parameters for mass density and hardness

Hussain

Khan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Engineering materials and their development are essential for a civilized society, and they have always played a key role in the industrialization of a country. The performance of materials can be increased by selecting appropriate fabrication process parameters. In this paper, the effect of fabrication processing parameters on multi-responses of lightweight material, namely, functionalized multiwalled carbon nanotubes-aluminum nanocomposite have been investigated through Taguchi-based grey relational analysis. Ethanol wt. %, milling time, compaction pressure, and sintering temperature were considered controlled parameters. Phase, surface morphology, and chemical compositions of the nanocomposite have been analyzed by X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray techniques. The scanning electron microscope images revealed that the optimal parameter combination is essential for decreasing void and crack formation during the powder metallurgy process and increasing the material performance index. Grey relational analysis was performed to evaluate optimal sample fabrication processing parameters by using grey relational grade as performance index measurement. The most influential parameter was investigated using main effect plots, interaction plots, contour plots, and analysis of variance. The results show that the optimal combination of sample fabrication parameters is A1B2C3D3. The results also show that the compaction pressure has a stronger correlation to the responses with a 46.92% contribution followed by sintering temperature. The % error between the predicted and experimental grey relational grades at the optimum-level combination is 1.83%. The Monte Carlo simulation data distribution plots show that the ranges of experimental mass density and hardness values are consistent with estimated simulated model values. Further, a regression equation has also been developed to establish the predictive model for the grey relational grade. The model analysis shows that the predictive model is adequate for evaluating grey relational grades. Therefore, this study confirms that the proposed approach can be a useful tool for improving materials’ performance.

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AI for tribology: Present and future

Yin,

Yang,

Liu

et al. 2024

Friction

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With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of “tribo-informatics” as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of “AI for tribology”. In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

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Process optimization for enhanced tribological properties of Al/MWCNT composites produced by powder metallurgy using artificial neural networks

Cited by 5 publications

References 49 publications

Production and Characterization of Cu/CNT Nanocomposites

Production and Characterization of Cu/CNT Nanocomposites

Optimization of MWCNTs/Al nanocomposite fabrication process parameters for mass density and hardness

AI for tribology: Present and future

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