Filler plays a major role in determining the properties and behavior of particulate composites. In this study a series of glass fiber reinforced polyester composites are fabricated using flyash, aluminum oxide (Al 2O3) and silicon carbide (SiC) particles as filler materials. The effects of these three different ceramics on the mechanical properties of glass—polyester composites are investigated. Comparative analysis shows that with the incorporation of these fillers, the tensile strength of the composites decrease significantly. The flexural properties, interlaminar shear strength, density and hardness are also affected by the type and content of filler particles. It is found that the presence of SiC improves the hardness of the glass—polyester composites, whereas the other two fillers show marginal effect. The study reveals that the reduction in tensile strength is the minimum in case of flyash among all the fillers. Further, the composite with low flyash content (10 wt%) exhibits improved flexural strength. It is thus interesting to find that an industrial waste-like flyash shows better filler characteristics compared to those of alumina and SiC. Moreover, being cheap and easily available, it would hopefully provide a cost effective solution to composite manufacturers.

This paper investigates the solid particle erosion wear performance of a multi component hybrid composite consisting of polyester, glass fibers and alumina particles. A mathematical model for damage assessment in erosion is developed and validated by a well designed set of experiments. For this, the design of experiments approach using Taguchi's orthogonal arrays is used. The study reveals that glass-polyester composite without any filler suffers greater erosion loss than the hybrid composite with alumina filling. Significant control factors and their interactions that influence the wear rate are identified. Finally, optimal factor settings are determined using genetic algorithm.

With the increased use of fiber/filler-reinforced polymer composites in erosive work environments, it has become extremely important to investigate their erosion characteristics intensively. This article describes the development of a multi-component composite system consisting of thermoplastic polyester resin reinforced with E-glass fiber and SiC particles, and studies its erosion behavior under different operating conditions. A room temperature erosion test facility and Taguchi's orthogonal arrays are used for experimentation. It identifies significant control factors influencing the erosion wear and also outlines significant interaction effects. Finally, optimal factor settings for minimum wear rate have been determined using a genetic algorithm.

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The improved performance of polymer-based hybrid composites in tribological applications has recently been a subject of considerable interest. A hybrid composite consists of the matrix reinforced with both fibers and particulate fillers. Alumina has the potential to be used as filler in such a multi-component system. This article investigates the effect of alumina filling on the erosion wear performance of glass fiber-reinforced polyester composites. For this purpose, an air jet type erosion test configuration and the design of experiment approach utilizing Taguchi's orthogonal arrays are used. Taguchi's design eliminates the need for repeated experiments; thus saving time, materials, and cost. The systematic experimentation leads to identifying significant factors and their interactions that predominantly influence the erosion wear. Pure glass-polyester composite without filler shows greater erosion rate whereas a significant improvement in the erosion resistance is observed with alumina fillers. This may be due to restriction of fiber-matrix debonding. The morphologies of the eroded surface are examined by a scanning electron microscope. Finally, optimal factor settings for minimum wear rate have been determined using genetic algorithm.

Polyester composites reinforced with three different weight fractions of woven E-glass fiber reinforcement are developed. To study the effect of various operational and material parameters on erosive wear behavior of these composites in an interacting environment, erosion test are carried out. For this purpose, an air jet type erosion test rig and the design of experiments approach utilizing Taguchi's orthogonal arrays are used. The findings of the experiments indicate that erodent size, fiber loading, impingement angle and impact velocity are the significant factors in a declining sequence affecting the wear rate. Significance of erosion efficiency in identifying the wear mechanism is highlighted. It is confirmed that the glass-reinforced-polyester composites exhibit mostly semi-ductile erosion response. An optimal parameter combination is determined, which leads to minimization of erosion rate. Analysis of variance (ANOVA) is performed on the measured data and signal to noise (S/N) ratios. A correlation derived from the results of Taguchi experimental design is proposed as a predictive equation for estimation of erosion wear rate of these composites. It is demonstrated that the predicted results obtained using this equation are consistent with experimental observations. Finally, optimal factor settings for minimum wear rate are determined using genetic algorithm.KEY WORDS: glass fiber reinforced polyester, taguchi method, design of experiment.

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Solid particle erosion of polymer composites is a complex surface damage process, strongly affected by material properties and operational conditions. To avoid repeated experimentation, it is important to develop predictive equations to assess material loss due to erosion wear under any impact conditions. This paper presents the development of a mathematical model for estimating erosion damage caused by solid particle impact on flyash filled glass fiber reinforced polyester composites. The model is based upon conservation of particle kinetic energy and relates the erosion rate with composite properties and test conditions. Another correlation derived from the results of Taguchi experimental design is proposed as a predictive equation for erosion wear of these fiber reinforced composites. Further, considering the complexity and high degree of nonlinearity in the erosion process, an artificial neural networks (ANN) technique is implemented as an effective tool for prediction of wear response of these composites in a larger space. Finally, the results of a mathematical model and the ANN model are compared with those obtained from experimentation.