A adjustment system for machining parameter includes a storage device and a processor. The processor includes a mapping module and a prediction module. The mapping module determines a type of a tool under test. When the type of the tool under test is determined as the same as the type of the first cutting tool, the mapping module obtains the first machining data from the database and as it as a reference data of the tool under test. When a machining program related to at least one of the NC program blocks is going to be executed for the tool under test, the prediction module predicts a predicted capacity loss value of the tool under test at a predetermined PRM while executing the machining program.

A method includes obtaining historical data associated with an industrial process, which is associated with multiple independent variables. The method also includes automatically excluding at least one portion of the historical data and automatically extracting data segments from at least one non-excluded portion of the historical data.

The method further includes iteratively performing model identification using the data segments to identify one or more models and using the model(s) to design, monitor, or tune at least one industrial process controller for the industrial process. Iteratively performing the model identification includes recursively analyzing the data segments to (i) select the data segment(s) associated with each variable that have a highest energy and provide a high signal to noise ratio and (ii) eliminate poorly performing segments associated with each variable. Iteratively performing the model identification also includes generating a model for each variable using the selected data segment(s) for that variable.

Prediction of a machining time at higher accuracy considering a machine delay generated in a machine is allowed by a numerical controller of the invention. The numerical controller includes a reference machining time prediction unit for predicting a reference machining time corresponding to a machining time not considering a delay time of servo control and machine motion based on the machining program, a program analysis unit for extracting a combination of at least one program command included in the machining program, a data storage unit for storing information related to an actual delay time of servo control and machine motion measured for each combination of program command types, a correction time calculation unit for calculating a correction time for correction of the reference machining time based on the combination of the program commands extracted by the program analysis unit and the information stored in the data storage unit, and a machining time prediction unit for calculating a predicted machining time obtained by correcting the reference machining time using the correction time.

A round hole machining method and a round-hole machining device in which machining can be accurately performed regardless of a residual stress caused by casting. An out-of-round hole machining device, which carries out inner diameter machining for a bore in a cast cylinder block includes the following: a stress deformation amount correspondence table that stores, for each portion of the cylinder block, a stress deformation amount based on stress that resides in the cylinder block during casting; a machining shape prediction section that predicts the machining shape on the basis of the stress deformation amount read from the stress deformation amount correspondence table; and a motor control unit that reverses the predicted machining shape with respect to a target shape and carries out reverse machining on a workpiece.

This disclosure relates generally to recommending tool configurations in machining. The machining tool configuration selection involves the selection of several tool specification parameters concerning the material, geometry and composition of the machining tool. The state-of-the-art methods uses a rule and knowledge-based system to select tool configuration, however these methods do not recommend tool configurations which satisfy customer requirement. Embodiments of the present disclosure uses a hierarchical model which is trained to predict acceptable tool specification parameters for a given requirement by learning the patterns from past tool selection data. Further a probabilistic approach is used to predict the top set of recommendations of tool configurations with a probability score for each prediction. The disclosed method is used for recommending tool configurations in a cylindrical grinding wheel process.

Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.