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.