Solid particle erosion of glass fibre reinforced flyash filled epoxy resin composites

Srivastava, V. K.; Pawar, A.G.

doi:10.1016/j.compscitech.2006.02.004

Cited by 141 publications

(95 citation statements)

References 13 publications

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“…The effect of fly ash content, impingement angle and erodent velocity on solid particle erosion of glass-epoxy composites has been studied by Srivastava and Pawar. [13] Biswas and Satapathy [14] have incorporated red mud as filler in glassepoxy and bamboo-epoxy composites. They found that for similar test conditions, bamboo-based composites exhibit much better erosion resistance than glass-epoxy composites.…”

Section: Introductionmentioning

confidence: 99%

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

Pati

Satapathy

2014

Polymers for Advanced Techs

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This article reports on the implementation of a soft computing technique based on artificial neural networks (ANNs) in analyzing the wear performance of a new class of hybrid composites filled with Linz-Donawitz slag (LDS). LDS is a major solid waste generated in huge quantities during steel making. It comes from slag formers such as burned lime/dolomite and from oxidizing of silica, iron etc. while refining the iron into steel in the LD furnace. In this work, hybrid composites consisting of short glass fiber (SGF) reinforced epoxy filled with different LDS content (0, 7.5, 15 and 22.5 wt%) are prepared by simple hand lay-up technique. Solid particle erosion trials, as per ASTM G 76 test standards, are conducted on the composite samples following a well-planned experimental schedule based on Taguchi design of experiments. Significant process parameters predominantly influencing the rate of erosion are identified. The study reveals that the LDS content is the most significant among various factors influencing the wear rate of these composites. Further, a model based on ANN for the prediction of erosion performance of these composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well-trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range.

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Section: Introductionmentioning

confidence: 99%

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

Pati

Satapathy

2014

Polymers for Advanced Techs

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“…In viewing past work on erosive wear of composites, most efforts were focused on the study of the influence of the material properties rather than the operating parameters [10][11][12][13]. Srivastava and Pawar [14] studied the effect of additives and impingement angle and eroding particle velocity on erosive wear of neat Eglass fiber reinforced epoxy resin composite materials and composites with 2 and 4 g fly ash additive particles. They concluded that the erosive wear rate of GFRP composite with 4 g fly ash is the lowest and that the maximum erosion occurs at 60°8 impingement angle.…”

Section: Introductionmentioning

confidence: 99%

Study of the strength and erosive behavior of CaCO3/glass fiber reinforced polyester composite

Yılmaz¹,

Ünal²,

Mimaroğlu³

2008

Express Polym. Lett.

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Abstract. In this study, the strength and erosive characteristics of CaCO3 filled unsaturated polyester/glass fiber (UPR/GFR) composite are evaluated. Samples of UPR with 40, 50 and 60 wt% content of CaCO3 and different CaCO3 particle sizes of 1, 2, 3, 5 and 10 micron were prepared and tested under tensile loading, indentation and erosion conditions. The tensile strength, hardness and erosion wear rate of unsaturated polyester/glass fiber (UPR composite)/CaCO3 composite were obtained and evaluated. The results showed that the higher is the percentage of CaCO3 in the composite and the smaller is the CaCO3 particle size, the higher is the strength and the erosive resistance of the glass fiber reinforced/unsaturated polyester composite (UPR-GFR). Furthermore, the highest erosion wear rate is at 90° impingement angle. Finally the results show that the erosive wear of CaCO3 content UPR/GFR composite in a brittle manner.

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“…Measurement of the angular displacement between these marks gives a measure of the flight time of the particles as they cross the space between the discs. The particle velocity can be calculated by using the following equation [14] . (1) where, L the separation of two discs, t the time in seconds, γ the rotation speed of disc per second and θ the angular displacement between the two marks.…”

Section: Test System For Erosionmentioning

confidence: 99%

“…General method to reduce erosion is to enhance the wearability of material surface by means of using wear resistant materials or coating material with better wearability, resulting in cost increase [7][8][9][10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Gas-solid erosion on bionic configuration surface

Han

Zhang

et al. 2011

J. Wuhan Univ. Technol.-Mat. Sci. Edit.

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A three levels orthogonal table-L 9 (3 4 ) was used, namely, impact angle, rotating speed, erodent size, and surface configuration were considered. The three bionic surface configurations are pit, groove, and ring. The experimental results indicate the experiment factors affecting erosive rate are, in their sequence of contribution, erodent size, impact angle, configuration, and rotating speed; the erosive rate increased with increase in rotating speed, erodent size; the erosion resistance of the sample with ring structure is higher than that of the other two samples. Based on this result, regression orthogonal experiment was carried out to select the optimal erosion resistance condition with respect to the ring bionic surface configuration. Regression equations between erosive rate and experimental factors of ring surface configurations were obtained.

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Solid particle erosion of glass fibre reinforced flyash filled epoxy resin composites

Cited by 141 publications

References 13 publications

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

Study of the strength and erosive behavior of CaCO3/glass fiber reinforced polyester composite

Gas-solid erosion on bionic configuration surface

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