Grinder selection device and grinder selection method

A grinder selection device that selects a grinder to be used for grinding machining by a grinding machine prior to the grinding machining, the grinder selection device comprising:

• an input unit that inputs a grinding condition for a workpiece as a grinding target of the grinding machining including at least the geometry of the workpiece and vibration data indicating vibration of the grinding machine;
• a learned model acquired in advance 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, the label data being data indicating grinder information about a grinder adequate for the grinding condition;
• and
• an output unit, by using the learned model, the output unit outputting grinder information about a grinder forming an adequate combination with a grinding condition for the grinding machining on the basis of the grinding condition input by the input unit.

A grinder selection method realized by a computer for selecting a grinder to be used for grinding machining by a grinding machine prior to the grinding machining, the grinder selection method comprising:

• an input step of inputting a grinding condition for a workpiece as a grinding target of the grinding machining including at least the geometry of the workpiece and vibration data indicating vibration of the grinding machine, and grinder information about one or more grinders to become grinder candidates to be used for the grinding machining;
• and
• a judgment step of judging a grinder candidate forming an adequate combination with a grinding condition for the grinding machining on the basis the grinder information input in the input step about one or more grinders to become grinder candidates to be used for the grinding machining by using a learned model acquired in advance 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.