A thermal displacement compensation system detects a state quantity indicating a state of a machine, infers a thermal displacement compensation amount of the machine from the detected state quantity, and performs a thermal displacement compensation of the machine based on the inferred thermal displacement compensation amount of the machine. The thermal displacement compensation system generates a learning model by machine learning that uses a feature quantity, and stores the generated learning model in association with a combination of specified conditions of individual difference of the machine.

A thermal displacement correction device for a machine tool that corrects thermal displacement of a spindle unit includes a memory that defines the spindle unit as a two-dimensional model in a thermally symmetrical plane or in a plane parallel to the thermally symmetrical plane, divides the two-dimensional model into regions, and stores a linear expansion coefficient, a heating coefficient and a radiation coefficient corresponding to each region, and a thermal conductivity coefficient between the each region and its adjacent region; a temperature estimating unit that estimates a temperature of the each region; a correction estimating unit; and a thermal displacement correction unit that performs correction in driving a feed shaft to a command position.

A thermal compensation system for machine tools includes a thermal compensation-monitoring device and a cloud processing device. The thermal compensation-monitoring device receives a plurality of temperature signals of a workpiece and corresponding processing tolerance data to build or update a thermal compensation database. The cloud processing device provides a thermal compensation model, and applies the model with the characterized temperature signals and the tolerance data to generate a compensation value so as to decide whether or not to modify the model or to run a compensation is necessary.

A numerical controller which compensates an estimated value of a thermal displacement amount of a machine tool is provided with an estimation unit comprising a learning model having learned the correlations between information on the temperature of the machine tool and information on a thermal displacement and configured to acquire the information on the temperature from the machine tool and calculate an estimated value of the thermal displacement, based on the information on the temperature and the learning model, a compensation condition acquisition unit configured to acquire positioning information from the machine tool, and a compensation unit configured to compensate the estimated value of the thermal displacement based on the positioning information.

The present disclosure generally describes a method for processing a workpiece in a machine, where the method determines an offset of the machine and adjusts for the offset during production operation. In one form, the method includes logging offset data of the machine over a period of operational time having varying thermal conditions, and comparing the logged offset data against a thermal model, where the thermal model is generated based on a probing routine and dry cycling for a plurality of test cycles on a calibration artifact. Based on the comparing, the method estimates offsets for the machine and adjusts offsets of the machine during operation.

A thermal displacement correction apparatus for a machine tool first determines the coefficient k in E=a+k|F| where F is a thermal displacement correction amount and E is an adjustment value (first step). Next, in actual processing, a is set if a has not been set yet (second step). After a and the coefficient k are determined in advance, thermal displacement correction unit is enabled and an operation of a machining program is started. The thermal displacement correction amount F is calculated, the adjustment value E is calculated based on E=a+k|F|, a thermal displacement correction amount F after adjustment (=EF) is calculated, and F is sent to the thermal displacement correction unit.

A method may include, responsive to determining that the earlier-generation information handling system includes the information handling resource for which a second thermal table of the second management controller requires updated thermal control parameters for thermal control of the information handling resource: (i) reading from a first thermal table of the first management controller an entry associated with the information handling resource and a second information handling system including thermal control parameters for thermal control of the information handling resource by the second information handling system; and (ii) communicating from the first management controller to the second management controller the thermal control parameters for thermal control of the information handling resource by the second information handling system in order to update the second thermal table with the thermal control parameters.

A plant or refinery may include equipment such as reactors, heaters, heat exchangers, regenerators, separators, or the like. Types of heat exchangers include shell and tube, plate, plate and shell, plate fin, air cooled, wetted-surface air cooled, or the like. Operating methods may impact deterioration in equipment condition, prolong equipment life, extend production operating time, or provide other benefits. Mechanical or digital sensors may be used for monitoring equipment, and sensor data may be programmatically analyzed to identify developing problems. For example, sensors may be used in conjunction with one or more system components to detect and correct maldistribution, cross-leakage, strain, pre-leakage, thermal stresses, fouling, vibration, problems in liquid lifting, conditions that can affect air-cooled exchangers, conditions that can affect wet-cooled exchangers, conditions that can affect a wetted-surface air-cooled heat exchanger, or the like. An operating condition or mode may be adjusted to prolong equipment life or avoid equipment failure.

A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.

A calculation formula learning unit sets a coefficient relating to a time lag element in a thermal displacement estimation calculation formula by machine learning while fixing a coefficient relating to measured data except the coefficient relating to the time lag element at a predetermined value based on a difference between a thermal displacement estimated value about a machine element calculated by substituting a measured data group into the thermal displacement estimation calculation formula and a thermal displacement actual measured value about the machine element; sets the coefficient relating to the measured data except the coefficient relating to the time lag element in the thermal displacement estimation calculation formula by machine learning based on the difference while fixing the coefficient relating to the time lag element at a predetermined value; and repeats the machine learning.