Influence of milling process parameters and significance of tools to improve the surface quality of GFRP composites

Composite material consumption is booming and is expected to increase exponentially in many industrial applications such as aerospace, automotive, marine and defense. However, in most cases, composite products require further processing before they can be used or assembled. Machining of composite materials is extremely difficult due to their anisotropic and non-homogeneous structure. This paper provides a comprehensive review of the literature on composite materials and their machining processes, such as turning, milling and drilling. Damage related to these processes is also discussed. The paper is divided into seven main parts; the first, second and third parts give a brief overview of composite materials, reinforcements used in composite materials and composite manufacturing methods, respectively. The fourth part deals with post-processing machining operations, while the fifth, sixth and seventh parts are devoted to the machining of carbon fiber reinforced polymer composite, glass fiber reinforced polymer and natural fiber reinforced polymer composites, respectively. An analysis of the factors that influence the machining and the machinability criteria used for these materials is also presented, with particular emphasis on cutting forces, tool wear, delamination and surface finish. Non-traditional manufacturing methods are not discussed in this paper.

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Analysis and Prediction of Temperature Using an Artificial Neural Network Model for Milling Glass Fiber Reinforced Polymer Composites

Spanu,

Abaza,

Constantinescu

2024

Polymers

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Milling parts made from glass fiber-reinforced polymer (GFRP) composite materials are recommended to achieve the geometric shapes and dimensional tolerances required for large parts manufactured using the spray lay-up technique. The quality of the surfaces machined by milling is significantly influenced by the temperature generated in the cutting zone. This study aims to develop an Artificial Neural Network (ANN) model to predict the temperature generated when milling GFRP. The ANN model for temperature prediction was created using a virtual instrument developed in the graphical programming language LabVIEW. Predicting temperature is crucial because excessive heat during milling can lead to several issues, such as tool wear and thermal degradation in the polymer matrix. The temperature in the tool–workpiece contact surface during the milling process was measured using a thermography technique with a ThermaCAM SC 640 camera (provided by FLIR Systems AB, Danderyd, Sweden), and the data were analyzed using the ThermaCAM Researcher Professional 2.8 SR-2 software. Experimental research shows that the cutting speed has a much more significant effect on the temperature in the cutting zone compared to axial depth of cut and feed speed. The maximum temperature of 85.19 °C was measured in the tool–workpiece contact zone during machining at a cutting speed of 75.39 m/min, a feed rate of 250 mm/min, and an axial depth of cut of 12 mm. This temperature rise occurred due to the larger contact area and heightened friction resulting from the abrasive characteristics of the reinforcement material.

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Influence of milling process parameters and significance of tools to improve the surface quality of GFRP composites

Cited by 3 publications

References 28 publications

Optimization of machining parameters during milling on glass fiber-reinforced textile composite

Optimization of machining parameters during milling on glass fiber-reinforced textile composite

A review on the machining of polymer composites reinforced with carbon (CFRP), glass (GFRP), and natural fibers (NFRP)

Analysis and Prediction of Temperature Using an Artificial Neural Network Model for Milling Glass Fiber Reinforced Polymer Composites

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