Machine learning device, numerical control device and machine learning method for learning threshold value of detecting abnormal load

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, comprising:

• a state observation unit that observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool;
• and
• a learning unit that learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data, wherein the learning unit comprises:
• an error calculation unit that calculates an error between the training data and the teacher data;
• and
• a learning model updating unit that updates a learning model for defining an error of a condition correlated to a detection of an abnormal load in the machine tool based on an output of the state observation unit and an output of the error calculation unit.