A machining method for machining workpieces preferably consisting at least in sections of wood, wood materials, plastic or the like on a machining device, wherein a vibration state of the machining device is detected during a machining process, a closed-loop or open-loop control towards a lower or preferably optimal vibration state of the machining device is performed while the machining process is continued.

A numerical controller decelerates a spindle feed rate in a bite portion to a bite velocity lower than a commanded feed rate when drilling is carried out and then, after passage through a velocity recovery point where a measured spindle load is made substantially constant, accelerates the spindle feed rate from the bite velocity to the commanded feed rate.

A system for acceleration adjustment of machine tool at rapid traverse includes a signal measurement module, a signal judgment module and an acceleration optimization module. The machine tool has a servo motor and a working platform. The signal measurement module measures signals while the servo motor drives the working platform from a first specific position to a second specific position, or from the second specific position back to the first specific position. The signal judgment module judges whether the actual maximum current value of the motor is equal to the manufacturer's specification according to the signals; and if negative, the acceleration optimization module calculates and optimizes an axial acceleration till an optimal value is achieved. Then, a curve smoothing time of the optimal acceleration is calculated and optimized by the acceleration optimization module. In addition, a method for acceleration adjustment of machine tool at rapid traverse is provided.

The numerical control system includes: detecting circuitry to obtain cutting force generated in a machine tool; controlling circuitry to calculate a control amount according to a cutting condition and to control a feed drive mechanism of the machine tool; countermeasure determining circuitry to, when it is detected from the cutting force or a state of the feed drive mechanism of the machine tool that a machining defect has occurred, calculate a plurality of deviation degrees for possible causes of the machining defect, and compare the calculated deviation degrees and to thereby determine a cause of the machining defect whose occurrence has been detected; and correction-amount calculating circuitry to calculate, according to the cause of the machining defect determined by the countermeasure determining circuitry, a correction amount with respect to the control amount, and then output the correction amount to the controlling circuitry.

A numerical control device according to the present invention includes: an upper limit value acquisition unit which acquires an upper limit value for a cutting speed that is a relative speed of the cutting tool to the workpiece; a reference speed calculation unit which calculates a revolution number of the spindle, and a feedrate; an oscillation speed calculation unit which calculates an oscillation speed that is superimposed on the feedrate; a cutting speed calculation unit which calculates the cutting speed based on the revolution number of the spindle, the feedrate and the oscillation speed; and a speed adjustment unit which adjusts at least either one of the revolution number of the spindle and the feedrate, so that a maximum value of the cutting speed calculated by the cutting speed calculation unit does not exceed the upper limit value acquired by the upper limit value acquisition unit.

A machine tool is configured to receive input data defining a profile to be machined onto a workpiece, with the profile defined in a plane perpendicular to an axis of rotation of the workpiece and non-circular in that plane, and calculate using an evolutionary algorithm a workpiece velocity profile corresponding to the velocities at which the workpiece is to be rotated by the workpiece mount over at least part of a rotation of the workpiece during machining. The machine tool then rotates the workpiece according to the workpiece velocity profile during machining of the workpiece.

This machining program generation device is provided with: a storage unit that stores machining conditions for respective tool regions determined on the basis of the number of effective edges in a multi-blade tool; a contact region calculation unit that calculates a tool region which comes into contact with a workpiece during machining on the basis of the shapes of the workpiece and the edge portion of the tool and of a tool path; and a machining program generation unit that generates a machining program on the basis of the tool path and the machining conditions stored in the storage unit in association with the tool region coming into contact with the workpiece.

A machining condition determining apparatus (1) includes a first setter (2a) setting a cutting speed of a cutting tool, a storage (3) storing a maximum output value of a drive motor rotating a spindle holding the cutting tool and a number of revolutions of the drive motor corresponding to the maximum output value, a number-of-revolutions determiner (4) obtaining a steady-state value of the maximum output value of the drive motor stored in the storage (3) and determining a number of revolutions of the drive motor corresponding to the obtained steady-state value of the maximum output value, and a tool-diameter determiner (5) calculating a tool diameter of the cutting tool based on the cutting speed set by the first setter (2a) and the number of revolutions of the drive motor determined by the number-of-revolutions determiner (4).

An online precise control method for truncating parameters of microscale abrasive grains includes the steps of: (1) clamping an electrode and a diamond grinding wheel to form a discharge circuit, and communicating a workstation with a power supply and a controller of a numerical control machine tool; (2) feedback controlling movement parameters of the machine tool and parameters of the power supply according to pulse discharge parameters, controlling a discharge current and a discharge voltage, and calculating a number of rotations of the grinding wheel; (3) determining a maximum truncating area of a cutting edge and a maximum effective number of rotations of the grinding wheel according to grinding wheel parameters and pulse discharge parameters, and precisely controlling a truncating area of a cutting edge of abrasive grains online by the calculated number of rotations of the grinding wheel; and (4) after the calculated number of rotations of the grinding wheel reaches a target value, calculating a truncating area of the cutting edge and a protrusion height of truncating microscale abrasive grains, and stopping the machine tool.

A machine learning device of a controller observes, as state variables that express a current state of an environment, feeding amount data indicating a feeding amount per unit cycle of a tool and vibration amount data indicating a vibration amount of a cutting part of the tool when the cutting part of the tool passes through the workpiece. In addition, the machine learning device acquires determination data indicating a propriety determination result of the vibration amount of the cutting part of the tool when the cutting part of the tool passes through the workpiece. Then, the machine learning device learns the feeding amount per unit cycle of the tool when the cutting part of the tool passes through the workpiece in association with the vibration amount data, using the state variables and the determination data.