81 research outputs found
Displacements analysis of self-excited vibrations in turning
The actual research deals with determining by a new protocol the necessary
parameters considering a three-dimensional model to simulate in a realistic way
the turning process on machine tool. This paper is dedicated to the
experimental displacements analysis of the block tool / block workpiece with
self-excited vibrations. In connexion with turning process, the self-excited
vibrations domain is obtained starting from spectra of two accelerometers. The
existence of a displacements plane attached to the tool edge point is revealed.
This plane proves to be inclined compared to the machines tool axes. We
establish that the tool tip point describes an ellipse. This ellipse is very
small and can be considered as a small straight line segment for the stable
cutting process (without vibrations). In unstable mode (with vibrations) the
ellipse of displacements is really more visible. A difference in phase occurs
between the tool tip displacements on the radial direction and on the cutting
one. The feed motion direction and the cutting one are almost in phase. The
values of the long and small ellipse axes (and their ratio) shows that these
sizes are increasing with the feed rate value. The axis that goes through the
stiffness center and the tool tip represents the maximum stiffness direction.
The maximum (resp. minimum) stiffness axis of the tool is perpendicular to the
large (resp. small) ellipse displacements axis. FFT analysis of the
accelerometers signals allows to reach several important parameters and
establish coherent correlations between tool tip displacements and the static -
elastic characteristics of the machine tool components tested
New method to characterize a machining system: application in turning
Many studies simulates the machining process by using a single degree of
freedom spring-mass sytem to model the tool stiffness, or the workpiece
stiffness, or the unit tool-workpiece stiffness in modelings 2D. Others impose
the tool action, or use more or less complex modelings of the efforts applied
by the tool taking account the tool geometry. Thus, all these models remain
two-dimensional or sometimes partially three-dimensional. This paper aims at
developing an experimental method allowing to determine accurately the real
three-dimensional behaviour of a machining system (machine tool, cutting tool,
tool-holder and associated system of force metrology six-component
dynamometer). In the work-space model of machining, a new experimental
procedure is implemented to determine the machining system elastic behaviour.
An experimental study of machining system is presented. We propose a machining
system static characterization. A decomposition in two distinct blocks of the
system "Workpiece-Tool-Machine" is realized. The block Tool and the block
Workpiece are studied and characterized separately by matrix stiffness and
displacement (three translations and three rotations). The Castigliano's theory
allows us to calculate the total stiffness matrix and the total displacement
matrix. A stiffness center point and a plan of tool tip static displacement are
presented in agreement with the turning machining dynamic model and especially
during the self induced vibration. These results are necessary to have a good
three-dimensional machining system dynamic characterization
Use of artificial neural networks to analyse tunnelling-induced ground movements obtained from geotechnical centrifuge testing
In geomechanics, centrifuge modelling and digital image analysis enable the acquisition of large amounts of high-quality data related to ground movements. In this paper, modern intelligent methods based on a feedforward artificial neural network (ANN) architecture are applied to study tunnelling-induced ground displacements. Soil displacement data obtained from a geotechnical centrifuge test are used to investigate the capabilities of ANNs in this context. Because this work represents a feasibility study, the centrifuge dataset is limited to a single test. The trial-and-error process is used to identify three architectures of varying complexity that achieve a good level of performance. Predictions are evaluated both statistically (R2) and qualitatively (analysing the shape of vertical and horizontal displacement profiles). Results show the applicability of modern intelligent analysis methods for analysing centrifuge datasets and highlight certain strengths and deficiencies of feedforward ANN architectures compared to empirical methods
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