Open AccessSoft Computing Techniques for the Prediction of Hybrid Composites
Author(s) -
C. Kavitha
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1928.029420
Subject(s) - soft computing , surface roughness , machining , artificial neural network , adaptive neuro fuzzy inference system , fuzzy logic , computer science , materials science , mechanical engineering , composite material , fuzzy control system , machine learning , engineering , artificial intelligence
Soft computing techniques such as Artificial Neural Networks and Fuzzy logic are widely used in application of manufacturing technology. Surface roughness plays a vital role for quality of the product using machining parameters. Soft computing techniques are applied to predict the surface roughness in an economical manner. In this paper, prediction of surface roughness is evaluated using ANFIS [Adaptive Neuro-Fuzzy Inference System] methodology for the cutting parameters of end-milling process for machining the halloysite nanotubes (HNTs) with aluminium reinforced epoxy hybrid composite material. Experimental datas are used to analyse the relationship between the input parameter such as depth of cut (d), cutting speed (S), feed-rate (f) and output parameters as surface roughness. Datas are classified into training and testing with different types of membership functions. The observed results accurately predict the output which was not used in training and it is almost very close to the actual output obtained in the experimental work. Moreover it was found that gbellmf is helpful for better prediction with minimum error.