A machine tool for machining a workpiece includes a spindle configured to rotate a holder mounted with a tool used for machining, one or more moving shafts configured to move the holder and/or a work base on which the workpiece is placed, a torque measurement unit configured to measure driving torque of the spindle and/or the one or more moving shafts, a reference value calculation unit configured to use, as a reference value, the driving torque measured by performing a no-load operation by rotating the spindle in a normal state, a torque comparison unit configured to compare, to the reference value, the driving torque measured by performing a no-load operation by rotating the spindle before actual machining, and an alarm unit configured to determine whether to issue an alarm on the basis of results of the comparison.

A machine tool for machining a workpiece includes a spindle configured to rotate a holder mounted with a tool used for machining, one or more moving shafts configured to move the holder and/or a work base on which the workpiece is placed, a torque measurement unit configured to measure driving torque of the spindle and/or the one or more moving shafts, a reference value calculation unit configured to use, as a reference value, the driving torque measured by performing a no-load operation by rotating the spindle in a normal state, a torque comparison unit configured to compare, to the reference value, the driving torque measured by performing a no-load operation by rotating the spindle before actual machining, and an alarm unit configured to determine whether to issue an alarm on the basis of results of the comparison.

A method and system for optimal control of ultra-precision cutting. The method for optimal control is based on time-precipitates-temperature characteristics of AlMgSi series aluminum alloy, and includes first determining types of precipitates of machined materials, and establishing a Lifshitz-Slyozov-Wagner (LSW) model of each precipitate. A temperature range is determined corresponding to each precipitate according to the LSW model to obtain a comprehensive temperature range. A relation model is established between cutting parameters and a cutting temperature according to the LSW model. Finally the cutting parameters are optimized according to the comprehensive temperature range and the relation model, so that the cutting temperature is beyond the comprehensive temperature range to inhibit the generation of the precipitates.

A method and system for optimal control of ultra-precision cutting. The method for optimal control is based on time-precipitates-temperature characteristics of AlMgSi series aluminum alloy, and includes first determining types of precipitates of machined materials, and establishing a Lifshitz-Slyozov-Wagner (LSW) model of each precipitate. A temperature range is determined corresponding to each precipitate according to the LSW model to obtain a comprehensive temperature range. A relation model is established between cutting parameters and a cutting temperature according to the LSW model. Finally the cutting parameters are optimized according to the comprehensive temperature range and the relation model, so that the cutting temperature is beyond the comprehensive temperature range to inhibit the generation of the precipitates.

An abnormality factor identification apparatus includes a sensor signal obtaining unit that obtains sensor signals associated with the physical state of a machine, an operating state determination unit that determines operating states of the machine based on information obtained from the machine, an abnormality level calculation unit that calculates the abnormality levels of the sensor signals for each operating state of the machine determined by the operating state determination unit, and a factor identification unit that determines a factor in an abnormality in the machine from historical data being a series of the abnormality levels for each operating state.

An abnormality factor identification apparatus includes a sensor signal obtaining unit that obtains sensor signals associated with the physical state of a machine, an operating state determination unit that determines operating states of the machine based on information obtained from the machine, an abnormality level calculation unit that calculates the abnormality levels of the sensor signals for each operating state of the machine determined by the operating state determination unit, and a factor identification unit that determines a factor in an abnormality in the machine from historical data being a series of the abnormality levels for each operating state.

Laser cutting device includes control unit that controls operations of laser machining robot and laser oscillator. A plurality of machining condition tables are stored in memory of control unit. Each of the machining condition tables carries data of laser power output and its duty, a usable range of a cutting speed of cutting work, the usable range being set based on a speed range in which laser cutting robot can move with given tracking accuracy, and an effective range of the cutting speed and the laser power output that are set so that a cut surface of work meets given finishing conditions. Control unit selects a machining condition table from the plurality of machining condition tables so that the cutting speed and the laser power output meet given conditions, and controls cutting of work based on the selected machining condition table.

Laser cutting device includes control unit that controls operations of laser machining robot and laser oscillator. A plurality of machining condition tables are stored in memory of control unit. Each of the machining condition tables carries data of laser power output and its duty, a usable range of a cutting speed of cutting work, the usable range being set based on a speed range in which laser cutting robot can move with given tracking accuracy, and an effective range of the cutting speed and the laser power output that are set so that a cut surface of work meets given finishing conditions. Control unit selects a machining condition table from the plurality of machining condition tables so that the cutting speed and the laser power output meet given conditions, and controls cutting of work based on the selected machining condition table.

A non-contact tool setting apparatus, suitable for use with machine tools and the like, is described in which a transmitter emits light that is received by a receiver. An analysis unit is provided for analysing the light received by the receiver and generating a trigger signal therefrom. The receiver includes an imaging sensor, such as a CMOS or CCD sensor, having a plurality of pixels. The analysis unit generates the trigger signal by analysing the light intensity measured by a first subset of the plurality of pixels. This analysis may involve, for example, determining a resultant received light intensity or performing edge detection. The non-contact tool setting apparatus can thus emulate the operation of a laser based non-contact tool setting apparatus whilst also permitting imaging of cutting tools.

A spindle with intelligent auto-detection system may comprise a spindle, a shell configured for covering the spindle, a first conducting ring, a second conducting ring and at least a sensor. The spindle has a connecting section and a working section, and the connecting section is configured for connecting a power unit of a processing machine. Moreover, a tool is secured on the working section, and the sensor is positioned in an inner tube of the spindle. The first conducting ring and the second conducting ring in a recess of the shell are respectively electrically connected to the sensor and an analytical instrument. When the spindle is spinning, the sensor is adapted to measure various data of statuses of the spindle and the processing machine, and the obtained data is configured to be sent to the analytical instrument, thereby achieving monitoring effect.