The invention relates to a
numerical control machine tool multi-working-condition
cutting parameter optimization method based on a
multilayer perceptron, and belongs to the field of
numerical control machine tool intelligent manufacturing equipment. The invention provides a multi-working-condition
cutting parameter optimization method of a
numerical control machine tool based on a multi-layer
perceptron, aiming at solving the problem of uncertainty of
machine tool coordinates and cutter parameters of the numerical control
machine tool, which can cause strong
flutter of the numerical control
machine tool in a
cutting process. The method comprises the steps that firstly, a tool
nose frequency response function curve based on different main shaft coordinates, tool diameters and overhanging lengths needs to be obtained under a hammering
modal experiment, and a milling stability lobe graph is drawn by combining a
modal theoretical formula and milling chatter stability
mathematical model analysis; according to a cutting chatter stability prediction method, constructing a limit cutting depth MLP prediction model taking the displacement of a moving part in each direction, the
diameter of a cutter, the overhanging length, the rotating speed of a main shaft, the cutting width and the feeding amount of each tooth as input; the regression prediction model is adopted as a cutting stability constraint to establish a multi-objective optimization model of the
material removal rate and the cutter life, and optimal
machining parameter configuration is solved through a non-dominated sorting NSGAII
algorithm with an elitist strategy. And example research and analysis are carried out by one
machining center, so that the obtained optimal configuration can meet stable cutting of the machine tool, and the reliability and the effectiveness of the method are verified.