caa a22 4500 445291834 CHVBK 20180317142530.0 cr unu---uuuuu 170323e20101201xx s 000 0 eng 10.1007/s12588-010-0004-4 doi (NATIONALLICENCE)springer-10.1007/s12588-010-0004-4 Wear response prediction of TiO2-polyester composites using neural networks [Elektronische Daten] [Alok Satapathy, Dora Tarkes, Nadiya Nayak] The improved performance of polymers and their composites in industrial and structural applications by the addition of particulate fillers has shown a great promise and so has lately been the subject of considerable interest. In the present study, titanium oxide (TiO2) particles of average size 75μm are reinforced in unsaturated polyester resin to prepare particulate filled composites of three different compositions (with 0, 10, and 20wt% of TiO2). Dry sliding wear trials are conducted following 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 TiO2 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 TiO2- polyester composites beyond the experimental domain. Central Institute of Plastics Engineering & Technology, 2010 TiO2-polyester composites nationallicence Sliding wear nationallicence Artificial Neural Networks nationallicence Design of experiments nationallicence Satapathy Alok National Institute of Technology, 769008, Rourkela, India aut Tarkes Dora National Institute of Technology, Thiruchirapally, India aut Nayak Nadiya National Institute of Technology, 769008, Rourkela, India aut International Journal of Plastics Technology Springer-Verlag 14(2010-12-01), 24-29 0972-656X 14<24 2010 14 12588 https://doi.org/10.1007/s12588-010-0004-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12588-010-0004-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Satapathy Alok National Institute of Technology, 769008, Rourkela, India aut NATIONALLICENCE 700 1- Tarkes Dora National Institute of Technology, Thiruchirapally, India aut NATIONALLICENCE 700 1- Nayak Nadiya National Institute of Technology, 769008, Rourkela, India aut NATIONALLICENCE 773 0- International Journal of Plastics Technology Springer-Verlag 14(2010-12-01), 24-29 0972-656X 14<24 2010 14 12588 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer