A method for predicting status of machining operation, in particular chatter occurrence comprising the following steps: training a neural network having an input layer, at least one hidden layer, an output layer and a plurality of weights in a pre-training phase and a final-training phase, wherein in the pre-training phase a pre-training data set is provided to the neural network to obtain a pre-trained neural network and in the final-training phase a final-training data set is fed to the pre-trained neural network to obtain a final-trained neural network, wherein the pre-training data set comprises simulated data and the final-training data set comprises experimental data; and performing prediction by utilizing the final-trained neural network to derive prediction data.

A method and apparatus for machining parts with variable stiffness includes determining, by a controller, a chatter-lobe plot of a cutter assembly. A preliminary tool path is developed by the controller. Virtual machining of a blank part using the preliminary tool path is performed by the controller. A chatter-lobe plot of the virtually machined part is determined by the controller. A dynamic chatter-lobe plot using the chatter-lobe plot of the cutting tool assembly and the chatter-lobe plot of the virtually machined part is determined by the controller. A chatter-free rotational speed of the cutting tool from the dynamic chatter-lobe plot is determined by the controller. A machining apparatus, controlled by the controller, uses the determined chatter-free rotational speed of the cutting tool to machine a blank part.

A computerized method of machining a workpiece including, prior to machining the workpiece, establishing, based on empirical data obtained from machining activity at an earlier time, an historical mapping indicating pairings of depth of cut and rpm at which undesirable chatter (UDC) did not occur during machining activity at an earlier time using at least one given type of milling machine, at least one given type of cutting tool and at least one given type of workpiece material, prior to commencing machining of the workpiece, programming a machine tool to machine the workpiece using a given type of milling machine, a given type of cutting tool and a given type of workpiece material at at least one depth of cut and rpm, which, based on the historical mapping, avoid UDC and operating the machine tool in accordance with the programming to machine the workpiece.

A computerized method of machining a workpiece including, prior to machining the workpiece, establishing, based on empirical data obtained from machining activity at an earlier time, an historical mapping indicating pairings of depth of cut and rpm at which undesirable chatter (UDC) did not occur during machining activity at an earlier time using at least one given type of milling machine, at least one given type of cutting tool and at least one given type of workpiece material, prior to commencing machining of the workpiece, programming a machine tool to machine the workpiece using a given type of milling machine, a given type of cutting tool and a given type of workpiece material at at least one depth of cut and rpm, which, based on the historical mapping, avoid UDC and operating the machine tool in accordance with the programming to machine the workpiece.

A rotation mechanism rotates a spindle to which a cutting tool or a workpiece is attached. A rotation controller controls the rotation of the spindle by the rotation mechanism. A feed mechanism moves the cutting tool relative to the workpiece. The rotation controller alternately exercises acceleration control under which the rotation of the spindle is accelerated to make a speed fluctuation ratio between the current cutting speed and a cutting speed one rotation before at the same rotation position equal to or greater than the first value that is greater than 1 and deceleration control under which the rotation of the spindle is decelerated to make the speed fluctuation ratio equal to or less than the second value that is less than 1.

A machine tool includes: a main spindle that holds a work piece; a tool holding unit that holds a tool for processing the work piece; a detection unit that detects vibration generated during processing of the work piece; and a processor that controls machining of the work piece performed by the tool, in which the processor compares a detected value detected by the detection unit with a predefined threshold value, interrupts the machining by releasing contact between the work piece and the tool at time of generation of chatter vibration or when there is a sign of the generation of the chatter vibration in which the detected value exceeds the threshold value, and analyzes the chatter vibration while the machining is being interrupted on basis of the detected value detected by the detection unit before the interruption.

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 drawing unit, a variation position display unit, and a reduction effect index display unit. The drawing unit is configured to display a variation diagram that illustrates a relationship between a variation amplitude and a variation cycle of the rotation speed. The reduction effect index display unit is configured to display a reduction effect index on the variation diagram. The reduction effect index represents a reduction effect of chatter vibration. The reduction effect index is calculated based on a speed ratio that is a ratio of a rotation speed of one rotation before to a rotation speed at an identical rotation position of the main spindle at any given timing.

The present invention is directed to a method for predicting chatter of a machine tool. The method comprises the following steps: Feeding first input data into an artificial neural network, which includes a plurality of weights; Determining first output data at the output of artificial neural network based on the first input data and the plurality of weights; Providing the first output data into a stability model to generate prediction data; Comparing the prediction data with measurement stability data and adjusting the plurality of weights of the artificial neural network.

A chatter vibration determination device is provided with a machine learning device configured to observe machining condition data including a feed speed and a spindle rotational frequency in cutting as state data representative of the current state of environment, execute processing related to machine learning using a learning model obtained by modeling the relationship of chatter vibration with a machining condition for the cutting, based on the state data, and estimate the occurrence/non-occurrence of chatter vibration and the improvement of the chatter vibration. The chatter vibration determination device outputs the result of the estimation of the occurrence/non-occurrence of the chatter vibration and the improvement of the chatter vibration.

A method and apparatus for machining parts with variable stiffness includes determining, by a controller, a chatter-lobe plot of a cutter assembly. A preliminary tool path is developed by the controller. Virtual machining of a blank part using the preliminary tool path is performed by the controller. A chatter-lobe plot of the virtually machined part is determined by the controller. A dynamic chatter-lobe plot using the chatter-lobe plot of the cutting tool assembly and the chatter-lobe plot of the virtually machined part is determined by the controller. A chatter-free rotational speed of the cutting tool from the dynamic chatter-lobe plot is determined by the controller. A machining apparatus, controlled by the controller, uses the determined chatter-free rotational speed of the cutting tool to machine a blank part.