Abstract: A grinder selection device includes: an input unit that inputs a grinding condition for a workpiece as a grinding target of grinding machining including at least the geometry of the workpiece and vibration data indicating vibration of a grinding machine, and grinder information about one or more grinders as grinder candidates to be used for the grinding machining; a learned model acquired through supervised learning using training data containing input data and label data, the input data containing an arbitrary grinding condition for a workpiece as a grinding target of grinding machining by an arbitrary grinding machine including at least the geometry of the workpiece and vibration data indicating vibration of the grinding machine, and grinder information about an arbitrary grinder, the label data being data indicating the adequacy or inadequacy of a combination between the grinding condition and the grinder information about the grinder; and a judgment unit.

Abstract: An input error detection device can detect an input error based on input values input in the past and the trend of values from the view point of the whole of a control program. In order to detect an error in inputting a value used to control a manufacturing machine, the input error detection device is provided with a histogram creation unit configured to create a histogram, based on a set of values used to control the manufacturing machine, and a determination unit configured, based on the histogram, to determine input errors of the values used to control the manufacturing machine.

Abstract: To reliably execute processing of a client or application to be prioritized according to processing contents for each processing state for an industrial machine. A control device relates to a control device that executes processing of processing requests for an industrial machine from a plurality of clients, the control device including: a priority determination unit configured to determine, in a case in which a plurality of the processing requests is received from the plurality of clients, a priority of each of the plurality of clients according to a processing state of the industrial machine, and a processing switching unit configured to switch an order of processing of each of the plurality of the processing requests based on a priority of each of the plurality of clients determined by the priority determination unit.

Abstract: To reliably execute processing to be prioritized for each processing state for an industrial machine. A control device executes processing of processing requests for a machine tool from a client, the numerical control device including: a processing switching unit configured to switch, in a case in which a plurality of the processing requests is received from the client, an order of processing for each of the plurality of processing requests based on a priority according to a processing state for the machine tool.

Abstract: Operation of an NC device can be restricted during the time until processing requested by a transmitting side is completed by a receiving side. An information processing device communicatively connected to a controller of an industrial machine including a processing request transmission unit that transmits a processing request to the controller; a processing completion notification management unit that detects a processing completion notification, transmitted from the controller, which notifies of completion of processing according to the processing request; and an operation restriction control unit that restricts processing request operation of the controller for a next operation until the processing completion notification is detected by the processing completion notification management unit.

Abstract: An adjustment necessity determination device is an adjustment necessity determination device that makes a determination, after a workpiece is machined, about a necessity to make an adjustment of a deviation of the axis position of each axis of a machine tool that has performed the machining and includes: a data acquisition unit that acquires a physical quantity relating to a cause of a deviation of the axis position of each axis of the machine tool, the physical quantity observed at the time of the machining; a time-series data storage unit that stores the physical quantity as time-series data; and an adjustment necessity judgement unit that makes a judgment about a necessity to make an adjustment of a deviation of the axis position of each axis of the machine tool based on the time-series data.

Abstract: A numerical controller includes an optimum data amount calculation unit that calculates an optimum value of at least one of the number of vertices and the number of polygons of a workpiece after machining, where the number of vertices or the number of polygons are extracted from the CAD data, a three-dimensional data reduction unit that reduces the number of vertices or the number of polygons of the workpiece after machining extracted from the CAD data, a three-dimensional model generation unit that generates a three-dimensional model of the workpiece after machining based on the vertices or the polygons reduced, and a display unit that generates display data for displaying the three-dimensional model and display the generated display data on the display device.

Abstract: Predicting a state of a mold after molding upon injection molding. A machine learning device includes: an input data acquiring unit that acquires input data including any molding condition including at least a type of resin, a type of additive, a blending ratio of the additive, and a temperature of the resin in molding any article molded by any injection molding machine, and state information indicating a wear amount of a mold before molding at the molding conditions; a label acquiring unit that acquires label data indicating state information of the mold after molding at the molding conditions included in the input data; and a learning unit that executes supervised learning using the input data acquired by the input data acquiring unit and the label data acquired by the label acquiring unit, and generates a learned model.

Abstract: A molding shrinkage ratio is predicted according to molding conditions set in advance in designing a mold. A machine learning device includes an input data acquiring unit that acquires input data including any molding condition including a type of resin, a type of additive, a blending ratio of the additive, a surface temperature of a mold, and a product of a holding pressure and a holding pressure time for any article molded by any injection molding machine, a label acquiring unit that acquires label data indicating a molding shrinkage ratio in a flow direction and a molding shrinkage ratio in a vertical direction perpendicular to the flow direction of a resin measured of the article molded at the molding condition, and a learning unit that executes supervised learning using the input data and the label data, and generates a learned model.

Abstract: A numerical controller that collects data from an industrial machine and writes data into a storage medium includes: an interval setting unit that sets a time interval between cycles in which one cycle involves collection of operation information indicating an operating state of the industrial machine and writing of the collected operation information to the storage medium; a reduced service life calculation unit that calculates a service life of the number of writes of the storage medium reduced by the writing on the basis of the set time interval as a reduced service life; a determining unit that determines whether the calculated reduced service life exceeds a prescribed threshold; and a signal output unit that outputs a signal indicating that the reduced service life exceeds the prescribed threshold when it is determined that the reduced service life exceeds the prescribed threshold.

Abstract: 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.

Abstract: An optimal timing to perform dressing on a grindstone is detected easily. A machine learning device includes: an input data acquisition unit that acquires input data including an arbitrary grinding condition for an arbitrary work material in a grinding process of an arbitrary grinding machine and grindstone information related to an arbitrary grindstone including at least a degree of necessity of dressing process indicating a degree of necessity of dressing before a grinding process is performed under the grinding condition; a label acquisition unit that acquires label data indicating the degree of necessity of dressing process of the grindstone after the grinding process is performed under the grinding condition 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, and generates a learned model.

Abstract: A grinder selection device includes: an input unit that inputs a grinding condition for a workpiece as a grinding target of grinding machining including at least the geometry of the workpiece and vibration data indicating vibration of a grinding machine, and grinder information about one or more grinders as grinder candidates to be used for the grinding machining; a learned model acquired through supervised learning using training data containing input data and label data, the input data containing an arbitrary grinding condition for a workpiece as a grinding target of grinding machining by an arbitrary grinding machine including at least the geometry of the workpiece and vibration data indicating vibration of the grinding machine, and grinder information about an arbitrary grinder, the label data being data indicating the adequacy or inadequacy of a combination between the grinding condition and the grinder information about the grinder; and a judgment unit.

Abstract: During machining of a workpiece, a gripping force adjustment device takes into account the state of the machining and the state of the workpiece in order to set a more appropriate gripping force. The gripping force adjustment device acquires data indicating a machining state implemented by a machine tool and data relating to a gripping state realized on the workpiece by a jig, and creates data to be used in machine learning on the basis of the acquired data. The gripping force adjustment device then executes machine learning processing relating to the gripping force exerted on the workpiece by the jig in the environment in which the machine tool machines the workpiece on the basis of the created data.

Abstract: A cutting fluid amount adjusting device acquires at least data indicating a machining state of a machine tool and data relating to cutting fluid supplied from a cutting fluid supplying device, creates data used in machine learning based on the acquired data, and executes, based on the created data, processing of the machine learning relating to the discharge amount of the cutting fluid from a cutting fluid nozzle in an environment in which machining of a workpiece by the machine tool is performed.

Abstract: A numerical controller capable of determining whether or not an input numerical value of an operator is within a range of a limit value includes: an input receiving unit receiving various operations of the operator and input of numerical values for input items; an input numerical value limiting unit determining whether or not the input numerical value is within the range of the limit value; a limit value changing unit changing the limit value when the input receiving unit receives a specific operation; and a limit value restoring unit restoring the limit value to a value before the change when the specific operation is completed.

Abstract: A warm-up evaluation device includes: a temperature data acquisition unit that acquires temperature data before warm-up operation when a machine performs the warm-up operation; a parameter value acquisition unit that acquires parameter values set in a program for performing the warm-up operation; an evaluation data acquisition unit that acquires evaluation data for evaluating a result of the warm-up operation; a learning unit that learns a machine learning model which receives the temperature data and the parameter values as an input and outputs the evaluation data on the basis of a plurality of warm-up operations performed by the same or the same types of machines; and an evaluation unit that inputs candidates for the parameter values to the machine learning model together with the temperature data and outputs the evaluation data when the same or the same types of machines perform a new warm-up operation.

Abstract: A numerical controller includes an optimum data amount calculation unit that calculates an optimum value of at least one of the number of vertices and the number of polygons of a workpiece after machining, where the number of vertices or the number of polygons are extracted from the CAD data, a three-dimensional data reduction unit that reduces the number of vertices or the number of polygons of the workpiece after machining extracted from the CAD data, a three-dimensional model generation unit that generates a three-dimensional model of the workpiece after machining based on the vertices or the polygons reduced, and a display unit that generates display data for displaying the three-dimensional model and display the generated display data on the display device.

Abstract: An adjustment necessity determination device is an adjustment necessity determination device that makes a determination, after a workpiece is machined, about a necessity to make an adjustment of a deviation of the axis position of each axis of a machine tool that has performed the machining and includes: a data acquisition unit that acquires a physical quantity relating to a cause of a deviation of the axis position of each axis of the machine tool, the physical quantity observed at the time of the machining; a time-series data storage unit that stores the physical quantity as time-series data; and an adjustment necessity judgement unit that makes a judgment about a necessity to make an adjustment of a deviation of the axis position of each axis of the machine tool based on the time-series data.

Abstract: An input error detection device can detect an input error based on input values input in the past and the trend of values from the view point of the whole of a control program. In order to detect an error in inputting a value used to control a manufacturing machine, the input error detection device is provided with a histogram creation unit configured to create a histogram, based on a set of values used to control the manufacturing machine, and a determination unit configured, based on the histogram, to determine input errors of the values used to control the manufacturing machine.