Open AccessPython implementation of fuzzy logic for artificial intelligence modelling and analysis of important parameters in drilling of hybrid fiber composite (HFC)
Author(s) -
Vimal Samsingh,
Achyuth Ramachandran,
Anirudh Selvam,
Karthick Subramanian
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1012/1/012037
Subject(s) - python (programming language) , composite number , machining , fuzzy inference system , thrust , fuzzy logic , torque , computer science , drilling , drill , inference , glass fiber , materials science , mechanical engineering , adaptive neuro fuzzy inference system , process engineering , composite material , algorithm , engineering , artificial intelligence , fuzzy control system , physics , thermodynamics , operating system
Composite materials present the advantage of being able to be specially designed for a particular application by combining appropriate reinforcement materials with a matrix material suited to withstand the operant conditions. The use of Hybrid-Fiber Composites (HFCs) addresses the need for greener manufacturing processes while also meeting product specifications in a wide range of applications, all for nominal prices. In order to improve our understanding of the machining processes compatible with HFCs, this paper presents findings from a study in which the effects of drilling on glass-flax-hemp fibre hybrid composite samples are observed and modeled. Pivotal parameters in drilling, namely drill bit diameter, spindle speed and feed rate are studied, and a fuzzy-logic inference system (FIS) coded in Python is used to model the thrust force and torque acting on the composite sample. A comparison between experimentally obtained and model-generated values of the same indicate very good correlation, thus verifying the effectiveness of the FIS.