Even if there is no similar record of plan generation for a manufacturing line, a logic of production scheduling is appropriately configured. An information processing apparatus stores a graph showing a manufacturing line configured to manufacture a plurality of items, task information indicating graphs showing basic modules and tasks of production scheduling, and a rule for selecting a logic of the production scheduling based on the tasks, identifies graphs showing basic modules included in the graph showing the manufacturing line by referring to the task information, extracts tasks corresponding to the identified graphs of the basic modules, selects a logic corresponding to the extracted tasks by referring to the rule, and outputs information indicating the selected logic.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

A control apparatus for controlling a machine tool on the basis of a machining program is provided with a program analysis unit which analyzes an input machining program, a process table creation unit which, on the basis of the results of the analysis by the program analysis unit, creates a process table that sequentially lists processes according to the execution flow of the machining program, and a display unit which displays the process table created by the process table creation unit.

A numerical control system, a machine tool having a numerical control system, and a data structure of a machining program, capable of automatically setting a plurality of tailstock settings corresponding to various machining processes, without increasing hardware resources on the numerical control apparatus side, are provided. A numerical control system 1 includes a numerical control apparatus 12 that controls driving of a machine tool 11 provided with a tailstock 10, and an external storage device 13. The external storage device 13 includes a machining program storage unit 14 that stores a machining program having a data structure including job data and setup data associated with a plurality of tailstock settings. The numerical control apparatus 12 includes a tailstock setting buffer 32, an active buffer region selection unit 43, and a tailstock setting writing unit 44.

Even if there is no similar record of plan generation for a manufacturing line, a logic of production scheduling is appropriately configured. An information processing apparatus stores a graph showing a manufacturing line configured to manufacture a plurality of items, task information indicating graphs showing basic modules and tasks of production scheduling, and a rule for selecting a logic of the production scheduling based on the tasks, identifies graphs showing basic modules included in the graph showing the manufacturing line by referring to the task information, extracts tasks corresponding to the identified graphs of the basic modules, selects a logic corresponding to the extracted tasks by referring to the rule, and outputs information indicating the selected logic.