A system comprising a finite element method, FEM, analysis device and a chip processing device for a machine tool , the chip processing device comprising a cleaning nozzle control unit configured to control a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control unit includes: a position estimation unit configured to estimate an accumulation position of generated chips by analyzing machining conditions of a workpiece; and a nozzle orientation adjustment unit configured to adjust an orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation unit , characterized in that the position estimation unit includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool are input to the machine learning device, and in that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a FEM analysis performed by the FEM analysis device based on the machining parameter and the in-machine shape. a cleaning nozzle control unit configured to control a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control unit includes: a position estimation unit configured to estimate an accumulation position of generated chips by analyzing machining conditions of a workpiece; and a nozzle orientation adjustment unit configured to adjust an orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation unit , characterized in that the position estimation unit includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool are input to the machine learning device, and in that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a FEM analysis performed by the FEM analysis device based on the machining parameter and the in-machine shape. a position estimation unit configured to estimate an accumulation position of generated chips by analyzing machining conditions of a workpiece; and a nozzle orientation adjustment unit configured to adjust an orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation unit , characterized in that the position estimation unit includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool are input to the machine learning device, and in that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a FEM analysis performed by the FEM analysis device based on the machining parameter and the in-machine shape. <EOS>