A chatter avoidance method and device is provided, including steps of: providing a stable operating condition plot; partially removing a first layer of a workpiece with a predetermined first removal depth according to a safe removal depth of the stable operating condition plot and sensing a chatter caused by the removal operation; if no chatter is sensed, completing the removal operation, otherwise, continuing to partially remove the first layer with a second removal depth less than the predetermined first removal depth; and determining a minimum removal depth according to the removal operation, and removing a last layer of the workpiece with a last removal depth less than or equal to the minimum removal depth, allowing the workpiece to have a target thickness. The disclosure prevents a chatter from continuously occurring without requiring a shut-down and thereby maintains a desired production rate.

A system, method, and computer-readable medium for providing a user interface. The system includes circuitry configured to generate chatter information based on sensor data collected from a machining operation of a machine performed at a previously selected tool speed setting. The chatter information includes a chatter level value and a chatter frequency value. A plurality of different candidate tool speed settings is determined based on the generated chatter frequency value from the machining operation. The circuitry generates the user interface that includes a plurality of different tool speed settings, including the previously selected tool speed setting and the plurality of different candidate tool speed settings for selection by a user. The user interface is configured to indicate the chatter level value for the previously selected.

Using a machine tool, a workpiece is machined while periodically varying the rotational speed of a spindle thereof. Correlation data on correlation between a speed variation rate RVA of the spindle rotational speed, a speed variation period ratio RVF thereof, and vibration of a tool when machining the workpiece while periodically varying the spindle rotational speed is previously obtained. Based on the correlation data, the speed variation rate RVA and the speed variation period ratio RVF are set so that the vibration of the tool and machining accuracy are within their respective allowable ranges, and based on the set speed variation rate RVA and speed variation period ratio RVF, a variation amplitude and a variation period of the spindle rotational speed are determined. The spindle is rotated at the rotational speed varying at the determined amplitude and period with respect to a target rotational speed, thereby machining the workpiece.

A chatter avoidance method and device is provided, including steps of: providing a stable operating condition plot; partially removing a first layer of a workpiece with a predetermined first removal depth according to a safe removal depth of the stable operating condition plot and sensing a chatter caused by the removal operation; if no chatter is sensed, completing the removal operation, otherwise, continuing to partially remove the first layer with a second removal depth less than the predetermined first removal depth; and determining a minimum removal depth according to the removal operation, and removing a last layer of the workpiece with a last removal depth less than or equal to the minimum removal depth, allowing the workpiece to have a target thickness. The disclosure prevents a chatter from continuously occurring without requiring a shut-down and thereby maintains a desired production rate.

Disclosed is a setting method of revolutions per minute on a real time of a rotating cutting tool, and a control device, and more particularly, a vibration characteristic is consistently evaluated and analyzed when a cutting process is in progress to suppress and avoid suppression.

A numerical controller includes a machine learning device for performing machine learning of machining condition adjustment of a machine tool. The machine learning device calculates a reward based on acquired machining-state data on a workpiece, and determines an adjustment amount of machining condition based on a result of machine learning and machining-state data, and adjusts machining conditions based on the adjustment amount. Further, the machine learning of machining condition adjustment is performed based on the determined adjustment amount of machining condition, the machining-state data, and the reward.

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.