A tuneable clamping device (1) is envisaged which comprises a stationary part (2) which is fixed to a table of a machine tool (10) or being part of a table of a machine tool (10), and a moving table (3) on which the actual flexible workpiece (11) is clamped. The tuneable clamping device (1) achieves tuning between the standing part (2) and the machine tool table (10) in order to damp a dominant mode of the workpiece (11) by playing the role of a device that provides serial dynamic coupling or coupling through the process to dissipate indirectly kinetic energy related to the workpiece (11).

A motor control device includes an acceleration detecting section configured to detect an acceleration of a control object, and an acceleration control section configured to control an acceleration of a motor driving the control object based on the detected acceleration, in which the acceleration control section includes a vibration component extraction filter configured to extract a vibration component generated between the motor and the control object, and the vibration component extraction filter changes a filter characteristic frequency according to at least one of a position and a mass of the control object.

In a method for monitoring an operating behavior of a tool holder, a resultant measurement axis signal is calculated using at least two measurement axis signals of at least two radially oriented measurement axes of a sensor provided in the tool holder. The monitoring is performed using the resultant measurement axis signal or the resultant.

A monitoring device of a main spindle rotation speed in a machine tool displays a variation state of the rotation speed by a rotation speed variation unit using a display unit in the machine tool. The monitoring device includes a variation value setting unit, a drawing unit, a first display unit, and a settable range calculating unit. The settable range calculating unit is configured to calculate a settable range of a variation amplitude and a variation cycle based on a calculation formula. The calculation formula includes the variation cycle, a difference between a first rotation speed and a second rotation speed at mutually different timings among the rotation speeds varied relative to a reference rotation speed, an inertia of a rotating body, a rated output of a motor that drives a main spindle, and a usage proportion to the rated output of the motor.

The present invention provides a cross-axis and cross-point modal testing and parameter identification method for predicting the cutting stability, which is used to improve the accuracy of existing prediction methods of cutting stability. The method firstly installs a miniature tri-axial acceleration sensor at the tool tip, and conducts the cross-axis and cross-point experimental modal tests respectively. The measured transfer functions are grouped according to different measuring axes, and the dynamic parameters are separately identified from each group of transfer functions. Then, the contact region between the cutter and workpiece is divided into several cutting layer differentiators. After that, together with other dynamic parameters, all the parameters are assembled into system dynamic parameter matrices matching with the dynamic model. Finally, dynamic parameter matrices including the effects of cross-axis and cross-point model couplings are obtained. Moreover, the acceleration sensor in the method only needs to be installed once.

A Bayesian learning approach for stability boundary and optimal parameter identification in milling without the knowledge of the underlying tool dynamics or material cutting force coefficients. Different axial depth and spindle speed combinations are characterized by a probability of stability which is updated based upon whether the result is stable or unstable. A likelihood function incorporates knowledge of stability behavior. Numerical results show convergence to an analytical stability lobe diagram. An adaptive experimental strategy identifies optimal operating parameters that maximize material removal rate. An efficient and robust learning method to identify the stability lobe diagram and optimal operating parameters with a limited number of tests/data points.

A controller includes a determination circuit, a setting circuit, and a control circuit. The determination circuit is configured to determine whether a chatter vibration is occurring while a spindle is rotating and a workpiece is being cut. The setting circuit is configured to set an upper limit and a lower limit on a change amount of a rotational speed of the spindle. The control circuit is configured to determine the change amount randomly from a range between the upper limit and the lower limit and configured to rotate the spindle at a second rotational speed obtained by changing a first rotational speed by the change amount that has been determined if the chatter vibration is determined as occurring while the control circuit controls the rotational speed of the spindle at the first rotational speed.

A tool holder is configured for rotation about a tool holder axis of rotation defining an axial direction. The tool holder, at one axial longitudinal end thereof, has a tool section with a tool-receiving formation for receiving a tool and, at the opposite axial longitudinal end, has a coupling section with a coupling formation for torque-transmitting coupling to a machine spindle of a machine tool. A measuring apparatus is configured for acquiring data relating to the operation of the tool holder. The measuring apparatus has a sensor, in particular an acceleration sensor, with at least two measurement axes. The two measurement axes are oriented substantially radially with respect to the tool holder axis of rotation.

In a method for monitoring an operating behavior of a tool holder, a resultant measurement axis signal is calculated using at least two measurement axis signals of at least two radially oriented measurement axes of a sensor provided in the tool holder. The monitoring is performed using the resultant measurement axis signal or the resultant.

A control device of a machine tool receives a target machining, in accordance with which a workpiece should be machined by a tool of the machine too!. The control device also receives via a human-machine interface or via an interface to an external memory device a selection, a parameterization and/or a specification of a sequence of predefined rules, which define the manner in which the machining of the workpiece should be modified in the event of undesired vibrations during machining. When undesired vibrations do not occur, as determined from acquired sensor signals, machining is carried out in accordance with the target machining. When undesired vibrations occur, the machining is modified in accordance with the rules, wherein the control device selects the rules in accordance with the selection, parameterizes the rules in accordance with the parameterization and/or carries out the rules in accordance with the specified sequence. ma