Abstract: A controller for a machine tool includes a machine learning apparatus configured to learn an Nth-order time-derivative component of a speed of each axis of the machine tool. The machine learning apparatus includes: a state observation section configured to observe first state data representing the Nth-order time-derivative component of the speed of each axis as a state variable representing a current state of an environment; a determination data acquisition section configured to acquire determination data representing a properness determination result of at least any one of machining accuracy, surface quality, and machining time of the machined workpiece; and a learning section configured to learn the Nth-order time-derivative component of the speed of each axis in relation to at least any one of the machining accuracy, the surface quality, and the machining time of the machined workpiece using the state variable and the determination data.

Abstract: A program generation device generates a program for controlling machining of a groove which is performed by machining a plurality of holes on a workpiece. The program generation device receives specified values including a tool radius, a groove shape (a length of the groove), and a cusp height and calculates positions of the plurality of holes based on the specified values which are received. Then, the program generation device calculates a machining order of the plurality of holes based on the specified values and respective values calculated by a hole machining set value calculation unit so that a bias is not generated in a removal stock for a tool in machining.

Abstract: A machined surface quality evaluation device includes a machine learning device that learns a result of evaluation on machined surface quality of a workpiece by an observer which correspond to an inspection result on the machined surface quality of the workpiece. The machine learning device observes the inspection result on the machined surface quality of the workpiece as a state variable, acquires label data indicating the result of the evaluation on the machined surface quality of the workpiece by the observer, and learns the state variable and the label data in a manner such that they are correlated each other.

Abstract: A machining defect factor estimation device includes a machine learning device that learns an occurrence factor of a machined-surface defect based on an inspection result on a machined surface of a workpiece. The machine learning device observes the inspection result on the machined surface of the workpiece from an inspection device, as a state variable, acquires label data indicating the occurrence factor of the machined-surface defect, and learns the state variable and the label data in a manner such that they are correlated each other.

Abstract: A controller for a machine tool includes a machine learning apparatus configured to learn an Nth-order time-derivative component of a speed of each axis of the machine tool. The machine learning apparatus includes: a state observation section configured to observe first state data representing the Nth-order time-derivative component of the speed of each axis as a state variable representing a current state of an environment; a determination data acquisition section configured to acquire determination data representing a properness determination result of at least any one of machining accuracy, surface quality, and machining time of the machined workpiece; and a learning section configured to learn the Nth-order time-derivative component of the speed of each axis in relation to at least any one of the machining accuracy, the surface quality, and the machining time of the machined workpiece using the state variable and the determination data.

Abstract: A program generation device generates a program for controlling machining of a groove which is performed by machining a plurality of holes on a workpiece. The program generation device receives specified values including a tool radius, a groove shape (a length of the groove), and a cusp height and calculates positions of the plurality of holes based on the specified values which are received. Then, the program generation device calculates a machining order of the plurality of holes based on the specified values and respective values calculated by a hole machining set value calculation unit so that a bias is not generated in a removal stock for a tool in machining.

Abstract: A numerical controller controls an object for movement along a movement path according to a machining program. If consecutive command blocks are oriented in the same direction, they are set as a single vector. These three consecutive vectors (first, second, and third vectors) thus created are determined to be located at a pick-feed section if the first and third vectors are oriented in the same direction and if the length of the second vector is within a predetermined range. The movement path is corrected to a path that successively linearly connects the start point of the first vector, points on the first and third vectors, and the end point of the third vector.

Abstract: A numerical controller controls an object for movement along a movement path according to a machining program. If consecutive command blocks are oriented in the same direction, they are set as a single vector. These three consecutive vectors (first, second, and third vectors) thus created are determined to be located at a pick-feed section if the first and third vectors are oriented in the same direction and if the length of the second vector is within a predetermined range. The movement path is corrected to a path that successively linearly connects the start point of the first vector, points on the first and third vectors, and the end point of the third vector.