Abstract: An object is to provide an application execution control system which can reduce a burden on an application and the number of development steps.

Abstract: A control system includes a controller that controls machining of a workpiece, and a photographing device that photographs an image of the workpiece under machining operation. The controller generates a three-dimensional model of the workpiece under machining operation based on the acquired image, compares the generated three-dimensional model and a three-dimensional model generated by a machining simulation with each other, and determines a presence or absence of a machining defect based on a result of the comparison. When the machining defect is present and re-machining is possible, a setting is modified depending on a cause of the machining defect and additional machining is executed based on the modified setting.

Abstract: To provide a numerical controller and a machine learning device that predict an abnormality, based on machine learning with perception of temporal change in data. The numerical controller includes the machine learning device provided with a learning unit that conducts machine learning of trends in operation of a machine on occasions of occurrence of abnormalities in the machine, based on time-series data acquired by a data logger device and relating to the operation of the machine and abnormality information relating to the abnormalities which have occurred in the machine and a prediction unit that predicts an abnormality which will occur in the machine, based on results of the machine learning in the learning unit and time-series data acquired by the data logger device and relating to current operation of the machine.

Abstract: An object is to provide an application execution control system which can reduce a burden on an application and the number of development steps.

Abstract: Screen information including a contents image required by an operator of a terminal is received from a control device and is displayed on a display unit. A control device includes: a first screen information creation unit that creates at least one piece of first screen information; a second screen information creation unit that creates second screen information including a contents image on the basis of at least one contents image selected from the first screen information by a communication terminal and layout information of a screen selected by the communication terminal; and a communication unit that transmits the first screen information and the second screen information to the communication terminal and receives the layout information of the screen and the contents image or the selection information of the contents image from the communication terminal.

Abstract: A control system includes a controller that controls machining of a workpiece, and a photographing device that photographs an image of the workpiece under machining operation. The controller generates a three-dimensional model of the workpiece under machining operation based on the acquired image, compares the generated three-dimensional model and a three-dimensional model generated by a machining simulation with each other, and determines a presence or absence of a machining defect based on a result of the comparison. When the machining defect is present and re-machining is possible, a setting is modified depending on a cause of the machining defect and additional machining is executed based on the modified setting.

Abstract: A numerical controller which controls a machine tool acquires tool information including a shape of a tool, a machining condition in machining, and information related to a machining result of a workpiece after machining. A machine learning device performs machine learning on tendency of the information related to a machining result with respect to the tool information and the machining condition based on the tool information and the machining condition used as input data and based on the information related to a machining result used as teacher data, so as to construct a learning model. The machine learning device determines whether or not a machining result is good by using the learning model based on the tool information and the machining condition before the machine tool machines a workpiece.

Abstract: An autonomous control system having a numerical control device configured to perform machining operation by driving control of an item in a machining tool and an external device, the numerical control device includes a configuration data storing unit configured to store configuration data representing configuration of the item, and a machining program looking-ahead processing unit configured to generate item disposition data, and the external device includes a work information storing unit, an interference determination processing unit configured to command start of operation of the external device at time when the operation of the external device does not interfere with the item driven in the machining tool.

Abstract: To provide a numerical controller and a machine learning device that predict an abnormality, based on machine learning with perception of temporal change in data. The numerical controller includes the machine learning device provided with a learning unit that conducts machine learning of trends in operation of a machine on occasions of occurrence of abnormalities in the machine, based on time-series data acquired by a data logger device and relating to the operation of the machine and abnormality information relating to the abnormalities which have occurred in the machine and a prediction unit that predicts an abnormality which will occur in the machine, based on results of the machine learning in the learning unit and time-series data acquired by the data logger device and relating to current operation of the machine.

Abstract: A numerical controller which controls a machine tool acquires tool information including a shape of a tool, a machining condition in machining, and information related to a machining result of a workpiece after machining. A machine learning device performs machine learning on tendency of the information related to a machining result with respect to the tool information and the machining condition based on the tool information and the machining condition used as input data and based on the information related to a machining result used as teacher data, so as to construct a learning model. The machine learning device determines whether or not a machining result is good by using the learning model based on the tool information and the machining condition before the machine tool machines a workpiece.

Abstract: An autonomous control system having a numerical control device configured to perform machining operation by driving control of an item in a machining tool and an external device, the numerical control device includes a configuration data storing unit configured to store configuration data representing configuration of the item, and a machining program looking-ahead processing unit configured to generate item disposition data, and the external device includes a work information storing unit, an interference determination processing unit configured to command start of operation of the external device at time when the operation of the external device does not interfere with the item driven in the machining tool.

Abstract: A programmable controller capable of automatically adjusting a percentage of execution period for each of a plurality sequence programs according to a desired condition. By executing the sequence programs according to predetermined execution percentages, execution time for each of the sequence programs, which is the time actually required for one execution of each of the sequence programs, is measured. The total of lengths of the execution time measured for each sequence program is obtained, and the minimum number of execution cycles for obtaining this total execution time is obtained. On the assumption that the sequence programs are executed in this minimum number of execution cycles, time required for execution of each of the sequence programs in one execution cycle is obtained. On the basis of the time required for execution of each of the sequence programs thus obtained, an execution percentage for each of the sequence programs in one execution cycle is calculated and fixed.