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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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ISSN

1433-3058

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Prediction and characterization of surface roughne

Authors: Hossein Mohammad Khanlou Bee Chin Ang Mohsen Marani Barzani Mahyar Silakhori Sepehr Talebian
Publish Date: 2015/02/19
Volume: 26, Issue: 7, Pages: 1751-1761
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Abstract

An adaptive neurofuzzy system ANFIS model was employed to predict the surface roughness Surface roughening of titanium biomaterials has a crucial effect on increasing the biocompatibility For this purpose sandblasted largegrit acidetched SLA has been introduced as an effective method to change the surface texturing and roughness Subsequent processes—polishing sandblasting and acid etching or SLA—were employed to modify the surface Alumina particles for surface blasting and Kroll’s etchant 3 ml HF + 6 ml HNO3 + 100 ml H2O for acid etching were utilized in this experiment This was performed for three different periods of time 10 20 and 30 s and temperatures 25 45 and 60 centigrade Correspondingly the Ti13Zr13Nb surfaces were evaluated using a field emission scanning electron microscope for texturing contact mode profile meter for the average surface roughness Ra nm and atomic force microscopy for surface texturing at the nanoscale In addition the surface roughness was reduced in each condition particularly in extremely high conditions Significantly the ANFIS model predicted the Ra amount of textured surface with an error band of 10  This research presents an idea to use the ANFIS model to obtain proper biological signs on the roughened surface in terms of surface roughness

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