A cutting tool, system, method and computer program product for increasing traceability of at least a first cutting edge of a cutting tool. The method includes detecting at least a first identification marker on the cutting tool, reading the at least first identification marker, decoding the at least first identification marker to determine at least a first cutting edge information data included in the machine readable code of the at least first identification marker, generating a first cutting tool identification data based on at least the at least first cutting edge information data, and storing at least any of the at least first cutting tool identification data and the at least first cutting edge information data in a memory.

The present invention discloses a method and device for evaluating a performance state of a numerical control cutting tool bit for flexible materials. The method includes: detecting three-phase current data of a brushless direct current motor during operation in real time by a preset current probe when the brushless direct current motor rotates to drive the numerical control cutting tool bit to vibrate; extracting characteristic values from the three-phase current data; clustering the characteristic values, and generating m real-time clusters, wherein m is an integer greater than 0; and acquiring j reference clusters, and determining a performance state level of the numerical control cutting tool bit based on the j reference clusters and the m real-time clusters, wherein j is an integer greater than 0.

A machine tool includes a first motor that receives load fluctuation when the workpiece is processed and a second motor that operates to change the plural kinds of tools. An information processing device takes out the current of the first motor measured by the first current sensor for each signal that occurs at the changing operation of the plural kinds of tools and is measured by the second current sensor, and relatively compares a non-negative function value that has a current value at each taken-out segment as a parameter for each number of times of processing on the workpiece, thereby detecting a tool abnormality for each kind of the tool.

A milling machine can include a frame; a milling assembly coupled to the frame and including a drum housing and a cutting rotor located within the drum housing; a plurality of load detection sensors extending along a width of the cutting rotor; and a controller operatively coupled to each of the plurality of load detector sensors and configured to determine an active cut width of the cutting rotor based on load information received from the plurality of load detector sensors.

A processing abnormality diagnostic device includes an abnormality diagnostic unit that diagnoses whether processing is abnormal using an abnormality threshold by a preset diagnostic model, a success or failure input unit that inputs success or failure of the diagnosis of the abnormality by the abnormality diagnostic unit, a measure determination unit that determines a measure when the diagnosis of the abnormality is input as failure through the success or failure input unit, an abnormality threshold change unit that updates the abnormality threshold, and a learning unit that relearns the diagnostic model using operation information of the machine tool when the diagnosis of the abnormality has failed. The measure determination unit determines which of the abnormality threshold change unit and the learning unit is to be adopted as the measure based on the operation information diagnosed by the diagnostic model when the diagnosis of the abnormality has failed.

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 present invention relates to an apparatus and a method of automatically converting thermal displacement compensation parameters of a machine tool, which automatically convert a compensation parameter of a thermal displacement compensation equation of a machine tool so that the compensation parameter is optimized to a current thermal displacement state of the machine tool in real time based on Z-directional or Y-directional displacement data of a tool tip end of a reference tool measured by a tool measuring unit according to an operation state of the machine tool or various kinds of machine tools or thermal displacement data of the machine tool calculated by measuring a processed portion of a processed material, and temperature data measured by a temperature measuring unit, to minimize a processing error according to thermal displacement and improve processing accuracy of the machine tool.

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 includes acquiring an image of a cutter on a drill bit, determining a cutter dull condition based on the image of the cutter using a machine-learning method, wherein the machine-learning method is trained using a set of training images and a set of known cutter dull conditions, wherein each of the set of known cutter dull conditions is associated with one or more of a set of cutters depicted in the set of training images and determining a cutter degradation severity based the image of the cutter. The method also includes generating bit modification instructions based the cutter dull condition and the cutter degradation severity.

A system for testing blade sharpness comprises a mounting arrangement for mounting a material to be cut; a force measuring device operable, in use, to determine variations in force along a blade as parts of the blade contact the material; and a control system. The control system is adapted to receive a signal that a blade sharpness test is to be performed; in response to the signal, prepare the mounting arrangement for a blade sharpness test; detect when force on the material rises above a pre-determined level; detect when the blade sharpness test is completed; and provide an indication of the force of the blade on the material.