Frequency response function simulation and evaluating in boring

Finite element method of simulating frequency response function (FRF) for boring tool in LS-Dyna solver is investigated in this work. Nowadays, computer numerical simulation allows to obtain FRF using different materials model with high precision compared to real experiments with sensors like impact hammer testing. This function is used in construction of stability lobe diagrams that allows operator of machining center to avoid chatter self-excited vibrations. Such vibration is led to decreasing of productivity and quality in cutting of metals and other materials. Amplitude and phase angle for the model is obtained from LS-Dyna result interpreter, that reads binary files, created during simulation by the program. Amplitude and phase angle of frequency response function are depending on dynamic stiffness of machining system. Real and imaginary part of frequency response function have been obtained during simulation. With luck of dynamic stiffness amplitudes of response increases.

HOLISTIC MONITORING OF MACHINING SYSTEM

Nowadays, the part program describes only the process itself and not the obtained performance. The operator monitors just some of the variables describing the actually obtained product and appropriately adjusts the values of the programmed variables. This adjustment is realised with a considerable delay and without an adequate fundament (many times even intuitively). Moreover, process monitoring currently follows only to notice the occurrence of perturbations and, hence, of deviations from process plan. As consequence, the performance in accomplishing the manufacturing task might be diminished due to an insufficient knowledge about both the system dynamics and the conditions in which the process is performed. Starting from these premises, the challenge addressed here is to rebuild at conceptual level the monitoring system such us the monitoring becomes holistic, this meaning evaluation & reveal of machining system current state & dynamics. In other words, the holistic monitoring concerns both the values of the variables describing the system state and the relations of causality between them. In this paper, the holistic monitoring is introduced through an illustrative sample. The monitoring variables and functions are defined and sampled.