A processor performs an experiment of machining a device to acquire first-type and second-type information each indicating conditions of the experiment of machining and third-type and fourth-type information each indicating a result of the experiment of machining (S401). The processor derives a first expression and a second expression, where the first expression receives first-type and second-type information as inputs and outputs third-type information as more than one solution, and the second expression receives first-type and second-type information as inputs and outputs fourth-type information. The processor derives more than one third expression from the first expression, where the more than one third expression each receives second-type and third-type information as inputs and outputs first-type information (S402). The processor receives second-type and third-type information each measured in machining as inputs and outputs fourth-type information indicating a result of machining using the second expression and the more than one third expression (S403).

The present application relates to a data processing method for a numerical control system, a computer device and a storage medium. The method comprises: receiving a data request, the data request carrying a target data identifier; parsing the data request to obtain an interactive type corresponding to the target data identifier; when the interactive type corresponding to the target data identifier is a type corresponding to real-time data, searching for data corresponding to the target data identifier in a shared memory of the numerical control system; transferring the data corresponding to the target data identifier from the shared memory to a data cache of the numerical control system and outputting the data.

The present application relates to a data processing method for a numerical control system, a computer device and a storage medium. The method comprises: receiving a data request, the data request carrying a target data identifier; parsing the data request to obtain an interactive type corresponding to the target data identifier; when the interactive type corresponding to the target data identifier is a type corresponding to real-time data, searching for data corresponding to the target data identifier in a shared memory of the numerical control system; transferring the data corresponding to the target data identifier from the shared memory to a data cache of the numerical control system and outputting the data.

The invention relates to a method for ascertaining a rough trajectory from a specified contour for controlling a machine tool which has at least two mutually redundant drive devices for carrying out superimposed movements, wherein the contour is determined by a contour function which is defined in portions at least by contour nodal points P.sub.0-P.sub.n+1 with ascending indices and contour portion functions p.sub.0-p.sub.n assigned to the contour nodal points P.sub.0-P.sub.n+1 and has a contour starting nodal point P.sub.0, wherein the rough trajectory is determined by a rough trajectory function which is defined in portions by rough trajectory nodal points Q.sub.0 to Q.sub.n+1 with ascending indices and has a rough trajectory starting nodal point Q.sub.0, wherein the rough trajectory starting nodal point Q.sub.0 is equated to the contour starting nodal point P.sub.0 and then in a first iteration step, on the basis of the contour nodal points P.sub.j to P.sub.n+1, the index value k of which is greater than or equal to the index value j of the respective rough trajectory starting nodal point that contour nodal point P.sub.k which has the smallest possible index value k and the distance of which from the rough trajectory starting nodal point Q.sub.j still just satisfies a specified distance condition is ascertained, and in a second iteration step, a respective following rough trajectory nodal point Q.sub.j+1 which follows the respective rough trajectory starting nodal point Q.sub.j and lies on a connecting line between Q.sub.j and P.sub.k or between Q.sub.j and a centroid of the portion contour P.sub.j to P.sub.k is ascertained.

Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Systems for reducing reduce downtime include industrial machines, sensor(s) associated therewith, edge agent(s), central controller(s), and display(s). The sensor(s) detect operational parameters (e.g., error condition) of the industrial machines associated therewith. The edge agent(s) receive information (e.g., logging messages) from the industrial machines and/or information from the sensors. The central controller(s). The central controllers receive and transform the information into standardized logging messages that are formatted identically, analyze the standardized logging messages to determine occurrence(s) of a current or anticipated error condition for any of the industrial machines, and transmit an alert and/or notification to the display(s) so users can take corrective action to ensure proper operation of the industrial machines. Such a system can be used in performing a corresponding method for reducing downtime associated with current or anticipated error conditions of such industrial machines.

Systems for reducing reduce downtime include industrial machines, sensor(s) associated therewith, edge agent(s), central controller(s), and display(s). The sensor(s) detect operational parameters (e.g., error condition) of the industrial machines associated therewith. The edge agent(s) receive information (e.g., logging messages) from the industrial machines and/or information from the sensors. The central controller(s). The central controllers receive and transform the information into standardized logging messages that are formatted identically, analyze the standardized logging messages to determine occurrence(s) of a current or anticipated error condition for any of the industrial machines, and transmit an alert and/or notification to the display(s) so users can take corrective action to ensure proper operation of the industrial machines. Such a system can be used in performing a corresponding method for reducing downtime associated with current or anticipated error conditions of such industrial machines.

The state of a cutting fluid after machining is predicted. A machine learning device includes: an input data acquisition unit that acquires input data including arbitrary machining conditions for an arbitrary work in machining by an arbitrary machine tool and state information indicating a state of a cutting fluid before machining is performed under the machining conditions; a label acquisition unit that acquires label data indicating state information of the cutting fluid after the machining is performed under the machining conditions included in the input data; and a learning unit that executes supervised learning using the input data acquired by the input data acquisition unit and the label data acquired by the label acquisition unit to generate a learned model.

A method includes defining a plurality of variables to modify in a control loop; collecting first data using a first variable of the plurality of variables while executing the control loop, generating a first result based on the collecting first data step, substituting a second variable of the plurality of variables for the first variable, collecting second data using the second variable while executing the control loop, generating a second result based on the collecting second data step, comparing the first result and the second result; and taking an action based on the comparing step.