| CPC G05B 13/0265 (2013.01) [B24B 39/00 (2013.01); B24B 51/00 (2013.01); G05B 13/026 (2013.01)] | 10 Claims |
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1. A machine learning device, comprising:
a non-transitory memory configured to store a program;
a processor configured to execute the program stored on the memory to cause the machine learning device to:
obtain, as input data, machining information for burnishing in which surface treatment was performed by pressing an arbitrary tool against a machined surface of a workpiece, wherein the machining information including at least information on the workpiece before the burnishing and information on a machining condition for the burnishing;
obtain label data indicating machined state information including a machined state of the workpiece after the burnishing and a surface roughness of the workpiece in a case where the machined state is normal; and
use the obtained input data and the obtained label data to execute supervised learning and generate a learned model that takes as an input machining information regarding burnishing to be performed and outputs machined state information for the burnishing to be performed,
wherein the information on the workpiece before the burnishing includes surface roughness before machining of the workpiece,
the machined state information includes a surface roughness after machining in the case where the machined state is normal,
the machined state information outputted by the learned model is utilized to output an instruction to execute burnishing or an instruction to change the machining information, and
further the machined state information outputted by the learned model is utilized to generate an operational command which are utilized by a machine tool to execute burnishing on the machining target workpiece.
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