A robot system includes a robot that self-travels along a traveling shaft and is provided with a position detection sensor at a distal end, a support member that has a plurality of reference positions juxtaposed and supports a workpiece, a plurality of calibration members that are juxtaposed along the traveling shaft, and a control device, in which the calibration members each have a calibration position, and the control device is configured to cause the robot to move by a predetermined first distance along the traveling shaft, calibrate position coordinates of the robot based on position coordinates of the calibration positions detected by the position detection sensor, and subsequently calibrate position coordinates of the workpiece based on position coordinates of the reference positions detected by the position detection sensor.

A warm-up method for a machine system including a machine component and a machine sensor configured to sense temperature of the machine component is provided. The method comprises steps of; A) activating the machine component to execute a warm-up operation for warming up the machine component; B) determining whether the machine component is warmed up based on a target temperature corresponding to the machine component and a temperature of the machine component that is currently sensed by the machine sensor; and C) when it is determined that the machine component is warmed up, making the machine component not execute the warm-up operation.

The invention relates to a machining tool for machining a workpiece, including a main spindle with a driven shank, a tool holder which can be clamped in the shank, a cutting tool which is arranged on the tool holder, a distance sensor for determining a distance of the shank of the main spindle in relation to a reference point, and a control unit which is configured to perform compensation of the tool path during machining of the workpiece based on an elongation and displacement of the shank and an elongation of the tool holder holding the cutting tool, wherein the elongation and displacement of the shank is determined based on the distance determined using the distance sensor, and wherein the elongation of the tool holder holding the cutting tool is determined based on a rotational speed of the shank.

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

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.

An application method of the thermal error-temperature loop in the spindle of a CNC machine tool. This uses a bar and two displacement sensors to determine radial thermal errors of the spindle. Meanwhile two temperature sensors are used to determine the temperature of the upper and lower surfaces of the spindle box. Then, the thermal error-temperature loop is drawn with the temperature difference between two temperature sensors as the abscissa and the radial thermal error of the spindle as the ordinate. Finally, the loop is employed to analyze the mechanism of the radial thermal deformation of the spindle and the thermal error level is evaluated. Since the method is based on measured data, the results of the analysis are closer to the reality, compared to those from the numerical simulations.

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

A warm-up method for a machine system including a machine component and a machine sensor configured to sense temperature of the machine component is provided. The method comprises steps of; A) activating the machine component to execute a warm-up operation for warming up the machine component; B) determining whether the machine component is warmed up based on a target temperature corresponding to the machine component and a temperature of the machine component that is currently sensed by the machine sensor; and C) when it is determined that the machine component is warmed up, making the machine component not execute the warm-up operation.

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.

An application method of the thermal error-temperature loop in the spindle of a CNC machine tool. This uses a bar and two displacement sensors to determine radial thermal errors of the spindle. Meanwhile two temperature sensors are used to determine the temperature of the upper and lower surfaces of the spindle box. Then, the thermal error-temperature loop is drawn with the temperature difference between two temperature sensors as the abscissa and the radial thermal error of the spindle as the ordinate. Finally, the loop is employed to analyze the mechanism of the radial thermal deformation of the spindle and the thermal error level is evaluated. Since the method is based on measured data, the results of the analysis are closer to the reality, compared to those from the numerical simulations.