Open AccessDYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES
Author(s) -
Đorđe Čiča,
Milan Zeljković,
Saša Tešić
Publication year - 2020
Publication title -
facta universitatis. series: mechanical engineering
Language(s) - English
Resource type - Journals
eISSN - 2335-0164
pISSN - 0354-2025
DOI - 10.22190/fume191014012c
Subject(s) - machining , soft computing , machine tool , particle swarm optimization , system identification , adaptive neuro fuzzy inference system , identification (biology) , computer science , engineering , control theory (sociology) , control engineering , artificial neural network , mechanical engineering , fuzzy logic , algorithm , artificial intelligence , fuzzy control system , botany , control (management) , database , biology , measure (data warehouse)
In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.