A wire electric discharge machine according to the present invention includes a machine learning device which performs machine learning for adjustment of a machining condition of the wire electric discharge machine, the machine learning device includes a state observation unit which acquires data related to a machining state of a workpiece, a reward calculation unit which calculates a reward based on data related to a machining state, a machining condition adjustment learning unit which determines an adjustment amount of a machining condition based on a machine learning result and data related to a machining state, and a machining condition adjustment unit which adjusts a machining condition based on the determined adjustment amount of a machining condition, and the machining condition adjustment learning unit performs machine learning for adjustment of a machining condition based on the determined adjustment amount of a machining condition, data related to a machining state and acquired by the state observation unit, and a reward which is calculated by the reward calculation unit.

A method for designing cutting conditions for cutting a workpiece with a cutting tool uses design parameters, including a feed speed, an axial direction cutting amount, a radial direction cutting amount, and a cutting speed of/by the cutting tool. A deflection amount of the cutting tool is calculated from the design parameters. Then a chattering vibration occurs or not in the cutting tool is determined. Depending on the determination result, a maximum cutting thickness of the workpiece is calculated. Then a cutting temperature of the cutting tool is calculated. Then whether a tool life of the cutting tool is satisfied or not is determined. Depending on the determination result, a cutting efficiency of the cutting tool is calculated and compared with data of a cutting efficiency stored in advance. When the calculated cutting efficiency is a maximum value among the data, the design parameters are used as the cutting conditions.

A numerical control (NC) program generating apparatus includes a standby state analyzing unit 12 analyzing an NC program to determine whether a block in which an operation unit is brought into a standby state is present, and when such a block is present, determining whether a power source 39, 45, 46 corresponding to the operation unit can be stopped at the time of execution of the block to specify a stoppable block, and a power operation code inserting unit 13 inserting an operation code for stopping the corresponding power source 39, 45, 46 at the time of execution of the stoppable block into the NC program and inserting an operation code for restarting the power source 39, 45, 46 after the execution of the block into the NC program.

A machining status display apparatus includes an achievement degree data storage storing achievement degree data relating to a degree of achievement of each of predetermined machining-related evaluation items within a range determined by attainable maximum and minimum values of the evaluation item and storing the degrees of achievement of the evaluation items obtained under each of predetermined sets of machining conditions in association with the set of machining conditions, a display part displaying the degrees of achievement of the evaluation items corresponding to a selected set of machining conditions by referring to the data in the achievement degree data storage, and an input part inputting a selection signal for selecting a set of machining conditions. The display part displays the degrees of achievement of the evaluation items obtained under the set of machining conditions corresponding to the selection signal by referring to the data in the achievement degree data storage.

Systems, devices, and methods including selecting one or more sequences of machining types for a feature of one or more features, where the selection of the one or more sequences of machining types is based on the feature and a database of prior selections of machining types; selecting one or more tools for the selected one or more sequences of machining types, where the selection of the one or more tools is based on the feature, the selected one or more sequences of machining types, and a database of prior selections of one or more tools; and selecting one or more machining parameters for the selected one or more tools, where the selected machining parameters are based on the feature, the selected one or more sequences of machining types, the selected one or more tools, and a database of prior selections of one or more machining parameters.

A method to control a material remover can include generating a test path to be processed by the processing circuitry to cause the material remover of the machine tool to move along a predetermined path; causing the processing circuitry to execute the test path and move the material remover along the test path; timing at least one of the performance of the processing circuitry and the movement of the material remover along the test path to generate machine tool timings; and using the machine tool timings to set limits which are arranged to subsequently be used when cutting paths are generated for the machine tool for which the test path has been generated.

A wire electric discharge machine according to the present invention includes a machine learning device which performs machine learning for adjustment of a machining condition of the wire electric discharge machine, the machine learning device includes a state observation unit which acquires data related to a machining state of a workpiece, a reward calculation unit which calculates a reward based on data related to a machining state, a machining condition adjustment learning unit which determines an adjustment amount of a machining condition based on a machine learning result and data related to a machining state, and a machining condition adjustment unit which adjusts a machining condition based on the determined adjustment amount of a machining condition, and the machining condition adjustment learning unit performs machine learning for adjustment of a machining condition based on the determined adjustment amount of a machining condition, data related to a machining state and acquired by the state observation unit, and a reward which is calculated by the reward calculation unit.