• International Journal of Aerospace System Engineering
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• 제7권1호
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• pp.16-20
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• 2020

The recent trend is increasing towards the usage of polymer matrix composites since they have a wide variety of applications. They have applications in the field of aircraft and space industry, sporting goods, medical devices, marine and automotive applications and also in commercial usage. The most commonly used fibre-reinforced polymer matrix composite is Glass fibre reinforced epoxy (GFRE) composite which is used in aviation, sports and automotive industries. However, the strength of GFRE composites is not adequate for structural applications. Therefore, the current research focuses on increasing the strength of GFRE composites by reinforcing with micro Graphite (Gr) particulates. The Gr used is an ultra-fine powder with particle size 250 ㎛. Gr is known to have good wear resistance, thermal conductivity and can operate at high temperatures. Gr particulates are mixed with the epoxy matrix in various weight ratios. Hand-lay technique is used for fabricating the composites. Mechanical properties such as tensile strength, elongation, compressive strength and flexural strength are obtained experimentally to study the effect of change in Gr content (0-5 wt. %). The tests were done as per ASTM standards.

• Structural Monitoring and Maintenance
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• 제3권4호
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• Advances in aircraft and spacecraft science
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• 제5권4호
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• pp.499-513
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• 2018

One of the major problems in glass fiber reinforced epoxy (GFRE) composite pipes is the durability under water absorption. This condition is generally recognized to cause degradations in strength and mechanical properties. Therefore, there is a need for an intelligent system for detecting the absorption rate and computing the mass of water absorption (M%) as a function of absorption time (t). The present work represents a new non-destructive evaluation (NDE) technique for detecting the water absorption rate by evaluating the dielectric properties of glass fiber and epoxy resin composite pipes subjected to internal hydrostatic pressure at room temperature. The variation in the dielectric signatures is employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such defects. ECS consists of twelve electrodes mounted on the outer surface of the pipe. Radius-electrode ratio is defined as the ratio of inner and outer radius of pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes on the basis of water absorption rate in the pipe material as a function of absorption time. The arrangements for positioning12-electrode sensor parameters such as capacitance, capacitance change and change rate of capacitance are analyzed by ANSYS and MATLAB to plot the mass of water absorption curve against absorption time (t). An analytical model based on a Fickian diffusion model is conducted to predict the saturation level of water absorption ($M_S$) from the obtained mass of water absorption curve. The FE results are in excellent agreement with the analytical results and experimental results available in the literature, thus, validating the accuracy and reliability of the proposed expert system.

• Steel and Composite Structures
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• 제36권4호
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• pp.417-426
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• 2020

Drilling processes in fiber-reinforced polymer composites are essential for the assembly and fabrication of composite structural parts. The economic impact of rejecting the drilled part is significant considering the associated loss when it reaches the assembly stage. Therefore, this article tends to illustrate the effect of cutting conditions (feed and speed), and laminate thickness on thrust force, torque, and delamination in drilling woven E-glass fiber reinforced epoxy (GFRE) composites. Four feeds (0.025, 0.05, 0.1, and 0.2 mm/r) and three speeds (400, 800, and 1600 RPM) are exploited to drill square specimens of 36.6×36.6 mm, by using CNC machine model "Deckel Maho DMG DMC 1035 V, ecoline". The composite laminates with thicknesses of 2.6 mm, 5.3 mm, and 7.7 mm are constructed respectively from 8, 16, and 24 glass fiber layers with a fiber volume fraction of about 40%. The drilled specimen is scanned using a high-resolution flatbed color scanner, then, the image is analyzed using CorelDraw software to evaluate the delamination factor. Multi-variable regression analysis is performed to present the significant coefficients and contribution of each variable on the thrust force and delamination. Results illustrate that the drilling parameters and laminate thickness have significant effects on thrust force, torque, and delamination factor.