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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1188

Authors: Kranthi, Ganguluri
Nayak, R
Biswas, Sandhyarani
Satapathy, Alok
Keywords: composites
Pine wood dust
Sliding Wear
Artificial Neural Networks
Issue Date: 2010
Citation: International Conference on Advancements in Polymeric Materials APM 2010
Abstract: This paper reports the development and wear performance evaluation of a new class of epoxy based composites filled with pine wood dust. The dust particles of average size 100 μm are reinforced in epoxy resin to prepare particulate filled composites of three different compositions (with 0, 5 and 10 wt% of pine wood dust). Dry sliding wear trials are conducted following a well planned experimental schedule based on design of experiments (DOE) using a standard pin-on-disc test set-up. Significant control factors predominantly influencing the wear rate are identified. Effect of pine wood dust content on the wear rate of polyester composites under different test conditions is studied. An Artificial Neural Networks (ANN) approach taking into account training and test procedure to predict the dependence of wear behavior on various control factors is implemented. This technique helps in saving time and resources for large number of experimental trials and predicts the wear response of pine w...
URI: http://hdl.handle.net/2080/1188
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