A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction and Simulation of Erosion Wear Behavior of Glass-Epoxy Composites Filled with Blast Furnace Slag
Solid particle erosion (SPE) wear characteristics of particulate filled polymer matrix composites have been widely explored by different investigators. Through judicious control of reinforcing solid particulate phase, selection of matrix and suitable processing technique, composites can be prepared to tailor the properties needed for any specific application. Due to high cost of conventional ceramic fillers, it has become important to explore the potential of cheap materials like mineral ores and industrial wastes for utilization in preparing particle-reinforced polymer composites. Previous researchers have reported the use of industrial wastes such as fly ash and red mud as filler materials in polymeric matrices. But the reinforcing potential of blast furnace slag (BFS) particle, a solid waste generated from pig iron production route, has not been explored so far in polymeric materials. In this work, composite samples are prepared by reinforcing micro-sized blast furnace slag as the particulate filler in epoxy resin reinforced with bi-directional glass fibre. Different specimens with varied BFS content (0, 10, 20 and 30 wt %) are fabricated by simple hand lay-up technique. They are subjected to solid particle erosion using an air jet type erosion test rig. Erosion tests are carried out by following a well designed experimental schedule based on Taguchi’s orthogonal array. Here, factors like BFS content, impact velocity, erodent temperature and impingement angle in declining sequence are found to be significant to minimize the erosion rate. A prediction model based on artificial neural network is proposed to predict the erosion performance of the composites under a wide range of erosive wear conditions. This model is based on the database obtained from the experiments and involves training, testing and prediction protocols. This work shows that an ANN model helps in saving time and resources that are required for a large number of experimental trials and successfully predicts the erosion rate of composites both within and beyond the experimental domain.
Prediction and Simulation of Erosion Wear Behavior of Glass-Epoxy Composites Filled with Blast Furnace Slag
Solid particle erosion (SPE) wear characteristics of particulate filled polymer matrix composites have been widely explored by different investigators. Through judicious control of reinforcing solid particulate phase, selection of matrix and suitable processing technique, composites can be prepared to tailor the properties needed for any specific application. Due to high cost of conventional ceramic fillers, it has become important to explore the potential of cheap materials like mineral ores and industrial wastes for utilization in preparing particle-reinforced polymer composites. Previous researchers have reported the use of industrial wastes such as fly ash and red mud as filler materials in polymeric matrices. But the reinforcing potential of blast furnace slag (BFS) particle, a solid waste generated from pig iron production route, has not been explored so far in polymeric materials. In this work, composite samples are prepared by reinforcing micro-sized blast furnace slag as the particulate filler in epoxy resin reinforced with bi-directional glass fibre. Different specimens with varied BFS content (0, 10, 20 and 30 wt %) are fabricated by simple hand lay-up technique. They are subjected to solid particle erosion using an air jet type erosion test rig. Erosion tests are carried out by following a well designed experimental schedule based on Taguchi’s orthogonal array. Here, factors like BFS content, impact velocity, erodent temperature and impingement angle in declining sequence are found to be significant to minimize the erosion rate. A prediction model based on artificial neural network is proposed to predict the erosion performance of the composites under a wide range of erosive wear conditions. This model is based on the database obtained from the experiments and involves training, testing and prediction protocols. This work shows that an ANN model helps in saving time and resources that are required for a large number of experimental trials and successfully predicts the erosion rate of composites both within and beyond the experimental domain.
Prediction and Simulation of Erosion Wear Behavior of Glass-Epoxy Composites Filled with Blast Furnace Slag
Padhi, Prasanta Kumar (author) / Satapathy, Alok (author)
2012
5 Seiten
Conference paper
English
Glass Phase Structure of Blast Furnace Slag
| British Library Conference Proceedings | 2011
Solid particle erosion behavior of glass-epoxy composites filled with TiC derived from ilmenite
| British Library Online Contents | 2014
| Engineering Index Backfile | 1935