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.
A certain percentage of mechanical parts in the engineering (parts in automotive industry, medical and measuring devices, parts in energetic sector, and etc.), refers to thin-walled structures with optimized shapes and dimensions. In addition to the requirements for a special shape of the structure, there are also requirements related to the dimensional accuracy and surface quality. In this paper, the milling of thin-walled tubular parts is analyzed. Workpiece of mentioned parts was C45E (AISI 1045) steel. Experimental analysis and optimization is based on Taguchi's experimental plan. The influence of cutting process parameters, depth of cut, feed per tooth and radial depth of cut, was analyzed. As the output cutting parameters, the dimensional accuracy and machined surface quality parameters, were measured and analyzed. Based on experimental data, modeling and optimization were performed. The obtained results defined the optimal input cutting process parameters, which give the best results in milling.
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