The present invention relates to a method and
system for automating a
metal processing process to minimize burr generation and optimize
processing efficiency by utilizing an
artificial intelligence model learned based on
metal processing process data. A
system for automating a
metal processing process to minimize burr generation and optimize processing efficiency by utilizing an
artificial intelligence model learned based on metal processing process data according to one embodiment of the present invention comprises: a memory; a
communication unit; The device includes a processor connected to the
communication unit and memory and executing program commands stored in the memory, wherein the processor collects metal processing process data in real time, including
cutting condition information, tool status information, material information, and sensor data during processing from a metal processing device, and uses the collected metal processing process data as input values to predict the probability of burr generation during processing as a probability value between 0 and 1 through a first model that has been trained, and based on the probability of burr generation, recalculates the probability of burr generation by resetting the value of at least one
process variable among feed rate, spindle rotation speed,
cutting depth, and
cutting path as the input value of the first model, and obtains
process conditions by repeatedly performing the change of process variables and prediction by the first model until the recalculated probability of burr generation becomes less than a preset reference value, transmits the
process conditions to the metal processing device to adjust the processing conditions in real time, and can retrain the first model based on the metal processing process data additionally collected during the processing and the data obtained by applying the burr generation result received from the metal processing device or operator terminal after the processing is completed to the metal processing process data.