A method of monitoring an electrical machine, wherein the method includes: a) obtaining temperature measurement values of the temperature at a plurality of locations of the electrical machine, b) obtaining estimated temperatures at the plurality of locations given by a thermal model of the electrical machine, the thermal model including initial weight parameter values, c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values, d) storing the initial weight parameter values to thereby obtain a storage of used weight parameter values, and updating the optimal weight parameter values as new initial weight parameter values, and repeating steps a)-d) over and over during operation of the electrical machine.

Various embodiments include a method for additive manufacturing of a building structure on using a simulation comprising: accessing a data set for the building structure describing the building structure in layers; calculating a global heat development in previous layers based a building history and heat input by an energy beam; determining a local heat development in a vicinity of the heat input; determining the process control based on the global and the local heat development; loading correction measures from a database; and assigning the correction measures locally to individual vectors of a tool path of the energy beam. At least one mass integral is calculated for individual vectors of the tool path. The measures are determined on the basis of a comparison of the calculated mass integral with mass integrals stored in the database.

The present disclosure is directed toward a method that includes logging offset data of a machine over a period of operational time having varying thermal conditions, comparing the logged offset data against a thermal model, estimating offsets for the machine based on the comparing, and adjusting offsets of the machine during operation.

Various embodiments include a method for additive manufacturing of a building structure on using a simulation comprising: accessing a data set for the building structure describing the building structure in layers; calculating a global heat development in previous layers based a building history and heat input by an energy beam; determining a local heat development in a vicinity of the heat input; determining the process control based on the global and the local heat development; loading correction measures from a database; and assigning the correction measures locally to individual vectors of a tool path of the energy beam. At least one mass integral is calculated for individual vectors of the tool path. The measures are determined on the basis of a comparison of the calculated mass integral with mass integrals stored in the database.

A method for calculating the reliability of the thermal error model of a machine tool based on deep neural network (DNN) and the Monte Carlo method, which belongs to the field of the thermal error compensation of computer numerical control (CNC) machine tools. Firstly, according to the probability distribution of the thermal parameters and thermal error model, a set of data for training the DNN is generated. Next, the DNN is constructed based on the deep belief networks (DBNs) and trained with the training data. Then, a group of random sampling data is obtained according to the probability distribution of the thermal characteristic parameters of the machine tool, and the group of random sampling is taken as the input and the output is obtained by the trained depth neural network. Finally, the reliability of the thermal error model is calculated based on the Monte Carlo method.

The present disclosure is directed toward a method that includes logging offset data of a machine over a period of operational time having varying thermal conditions, comparing the logged offset data against a thermal model, estimating offsets for the machine based on the comparing, and adjusting offsets of the machine during operation.

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.

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.