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Your search keyword '"Hussein, Hussein Mohamed Abdelmoneam"' showing total 12 results
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12 results on '"Hussein, Hussein Mohamed Abdelmoneam"'

2. Electrical conductivity analysis of extrusion-based 3D-printed graphene.

Author

R., Hushein, Shajahan, Mohamed Iqbal, Čep, Robert, Salunkhe, Sachin, Murali, Arun Prasad, Sharad, Gawade, Hussein, Hussein Mohamed Abdelmoneam, and Nasr, Emad Abouel

Subjects
ELECTRIC conductivity, FUSED deposition modeling, GRAPHENE, THREE-dimensional printing, SCANNING electron microscopy, PRINT materials
Abstract

Nowadays, research has shown the emergence of the 3D printing method for printing a functionalized component. Graphene nanomaterial has an enormous conducting property that can compete with conducting materials like copper and silicon. This paper describes the electrical conductivity investigation of 3D-printed graphene nanomaterial in extrusion-based 3D printing methods. In extrusion, two different approaches of the 3D printing method were used to print the graphene-based structure: the fused deposition modeling (FDM) method and the direct ink writing (DIW) method. Both printing methods follow the two printing processes and select material forms. Selection of testing was made to analyze the characterization variations in the printed material, such as XRD, TGA, viscosity, Raman shift, and Scanning Electron Microscopy analyses, which shows the changes of effect in the conductivity due to various parameter differences in both the printing methods. A four-point probe technique was used to analyze the electrical conductivity of the two different methods. These analysis results prove that the characterization variations differ in the FDM and DIW printed models. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Wear and Friction Analysis of Brake Pad Material Using Natural Hemp Fibers.

Author

Naidu, Mithul, Bhosale, Ajit, Munde, Yashwant, Salunkhe, Sachin, and Hussein, Hussein Mohamed Abdelmoneam

Subjects
NATURAL fibers, SISAL (Fiber), FRICTION, MECHANICAL wear, SCANNING electron microscopy, SURFACE morphology
Abstract

Non-exhaust brake dust and pollution arising from metal, semi-metal, and ceramic brake pads have made recent research consider their replacement by potential natural fibers such as hemp, flax, sisal, etc. These natural fibers are lightweight, biodegradable, and cheap. This paper discusses the wear and friction analysis of hemp fiber reinforced polymer brake pad material. Three test specimens viz. HF4P20, HF5P20, and HF6P20 were prepared per ASTM G99 standards for the pin-on disc tribo-test. The test trials and validation were done using the Taguchi design of experiments and ANOVA. The optimum result showed a consistent coefficient of friction and lowered specific wear rate for HF6P20 brake pad material. Worn surface morphology was done using scanning electron microscopy. [ABSTRACT FROM AUTHOR]

Published
2023
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12. Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

Author

Mahalingam Siva Kumar, Devaraj Rajamani, Ahmed M. El-Sherbeeny, Esakki Balasubramanian, Krishnasamy Karthik, Hussein Mohamed Abdelmoneam Hussein, Antonello Astarita, Siva Kumar, Mahalingam, Rajamani, Devaraj, El-Sherbeeny, Ahmed M, Balasubramanian, Esakki, Karthik, Krishnasamy, Hussein, Hussein Mohamed Abdelmoneam, and Astarita, Antonello

Subjects
salp swarm optimization, abrasive waterjet cutting, fiber intermetallic laminates, intelligent modeling, optimization, ANFIS, General Materials Science, fiber intermetallic laminate
Abstract

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm.

Published
2022

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