https://hdl.handle.net/20.500.12619/939 Mon, 13 Apr 2026 16:03:26 GMT 2026-04-13T16:03:26Z https://hdl.handle.net/20.500.12619/50089 Study of abrasive wear volume map for PTFE and PTFE composites Ünal, Hüseyin; Şen, Uğur; Mimaroğlu, Abdullah The potential of this work is based on consideration of wear volume map for the evaluation of abrasive wear performance of polytetrafluoroethylene (PTFE) and PTFE composites. The fillers used in the composite are 25% bronze, 35% graphite and 17% glass fibre glass (GFR). The influence of filler materials, abrasion surface roughness and applied load values on abrasive wear performance of PTFE and PTFE composites were studied and evaluated. Experimental abrasive wear tests were carried out at atmospheric condition on pin-on-disc wear tribometer. Tests were performed under 4, 6, 8 and 10 N load values, travelling speed of 1 m/sec and abrasion surface roughness values of 5, 20 and 45 mu m. Wear volume maps were obtained and the results showed that the lowest wear volume rate for PTFE is reached using GFR filler. Furthermore, the results also showed that the higher is the applied load and the roughness of the abrasion surface, the higher is the wear rate. Finally it is also concluded that abrasive wear process mechanism include ploughing and cutting mechanisms. Mon, 01 Jan 2007 00:00:00 GMT https://hdl.handle.net/20.500.12619/50089 2007-01-01T00:00:00Z https://hdl.handle.net/20.500.12619/50088 Cost assessment of concrete and steel types for office buildings: an exploratory study Özkan, Ömer; Gündüz, Mehmet Mon, 01 Jan 2007 00:00:00 GMT https://hdl.handle.net/20.500.12619/50088 2007-01-01T00:00:00Z https://hdl.handle.net/20.500.12619/50085 Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine Parlak, Adnan; İslamoğlu, Yaşar; Yaşar, Halit; Eğrisöğüt Tiryaki, Aysun The ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings is studied. The proposed new model is compared with experimental results. The comparison showed that the consistence between experimental and the network results are achieved by a mean absolute relative error less than 2%. It is considered that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems. (c) 2005 Elsevier Ltd. All rights reserved. Sun, 01 Jan 2006 00:00:00 GMT https://hdl.handle.net/20.500.12619/50085 2006-01-01T00:00:00Z https://hdl.handle.net/20.500.12619/50086 Stress Intensity Factors for Three-Dimensional Cracks in Functionally Graded Materials Using Enriched Finite Elements Ayhan, Ali Osman Mon, 01 Jan 2007 00:00:00 GMT https://hdl.handle.net/20.500.12619/50086 2007-01-01T00:00:00Z