A method for inspecting normality of a spindle of a machine tool is provided. Spectral analysis, time domain analysis and principal components analysis are performed on a vibration signal that results from the vibration of the spindle, so as to build a Gaussian mixture model. Then, based on a difference between the Gaussian mixture model and a predetermined reference model, whether the machine tool is operating normally can be determined in real time.

The invention relates to a method for producing or machining a toothing (2) on a workpiece (3), in which method the workpiece, which is rotationally driven about its axis of rotation (C), is brought into rolling machining engagement with tool toothing (5) rotating about an axis of rotation (C2) which is, in particular, at a non-null crossed-axes angle to the axis of rotation of the workpiece, wherein the machining operation is automatically monitored, using sensors to record same automatically, already at the machine operation stage for a recurring irregularity originating from tool wear (52), in particular higher wear of at least one tool tooth (51) compared to other tool teeth.

The invention relates to a method for producing or machining a toothing (2) on a workpiece (3), in which method the workpiece, which is rotationally driven about its axis of rotation (C), is brought into rolling machining engagement with tool toothing (5) rotating about an axis of rotation (C2) which is, in particular, at a non-null crossed-axes angle to the axis of rotation of the workpiece, wherein the machining operation is automatically monitored, using sensors to record same automatically, already at the machine operation stage for a recurring irregularity originating from tool wear (52), in particular higher wear of at least one tool tooth (51) compared to other tool teeth.

A method for monitoring chatter in a machining process includes the following steps: collecting an original signal related to chatter in the machining process; for the original signal, obtaining a signal segment for calculation and analysis by updating data points in a sliding window with a set step-length, where the step-length refers to a number of data points updated every time in the sliding window, and is not greater than the size of the sliding window; calculating fractal dimensions of the signal segments in the sliding window by using a fractal algorithm; and comparing the calculated fractal dimension with an identification threshold to determine whether chatter occurs in the machining process. The measured signal does not need to be preprocessed by using the method, which can greatly improve calculation efficiency and can ensure accuracy of chatter identification.

A method for monitoring chatter in a machining process includes the following steps: collecting an original signal related to chatter in the machining process; for the original signal, obtaining a signal segment for calculation and analysis by updating data points in a sliding window with a set step-length, where the step-length refers to a number of data points updated every time in the sliding window, and is not greater than the size of the sliding window; calculating fractal dimensions of the signal segments in the sliding window by using a fractal algorithm; and comparing the calculated fractal dimension with an identification threshold to determine whether chatter occurs in the machining process. The measured signal does not need to be preprocessed by using the method, which can greatly improve calculation efficiency and can ensure accuracy of chatter identification.

A machine tool (1) includes: a front spindle (10) having a front chuck (12); a rear spindle (20) having a rear chuck (22); a controller (50) configured to cause the rear chuck (22) to perform a fastening operation for fastening a work (W) fastened to the front chuck (12) to the rear chuck (22); and a detector (60) configured to detect an abnormality caused by the fastening operation by the rear chuck (22), based on measurement data associated with a vibration of at least one of the front chuck (12) and the rear chuck (22).

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 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 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.