A computer-implemented method for providing a trained function for performing a workpiece quality control includes receiving a plurality of training machining datasets, wherein different training high-frequency machining datasets are representative for the quality of different workpieces, transforming the plurality of training machining datasets into the time-frequency domain to generate a plurality of training time-frequency domain datasets, and training a function based on the plurality of training time-frequency domain datasets, wherein the function is based on an autoencoder. The autoencoder has input layers, output layers and a hidden layer. The plurality of training time-frequency domain datasets are provided to the input layers and the output layers during training, and a trained autoencoder function is outputted.

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 tool machine servo control simulation device and an establishing method of structure model are provided. The tool machine servo control simulation device includes a structure model and a processor. The structure model includes a position function, a velocity function and a drive property parameter. The processor includes a control signal receiver and a simulation component. The control signal receiver is used for receiving a servo command. The simulation component, response to the servo command, generates a simulation path according to the position function, the velocity function and the drive property parameter.

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 tool machine servo control simulation device and an establishing method of structure model are provided. The tool machine servo control simulation device includes a structure model and a processor. The structure model includes a position function, a velocity function and a drive property parameter. The processor includes a control signal receiver and a simulation component. The control signal receiver is used for receiving a servo command. The simulation component, response to the servo command, generates a simulation path according to the position function, the velocity function and the drive property parameter.

A computer-implemented method for providing a trained function for performing a workpiece quality control includes receiving a plurality of training machining datasets, wherein different training high-frequency machining datasets are representative for the quality of different workpieces, transforming the plurality of training machining datasets into the time-frequency domain to generate a plurality of training time-frequency domain datasets, and training a function based on the plurality of training time-frequency domain datasets, wherein the function is based on an autoencoder. The autoencoder has input layers, output layers and a hidden layer. The plurality of training time-frequency domain datasets are provided to the input layers and the output layers during training, and a trained autoencoder function is outputted.