The present application belongs to the technical field of
particle swarm optimization control, and particularly relates to an intelligent
production line control method for an automobile differential shell, which comprises the following steps: acquiring the
cutting resistance, spindle speed, feed speed and measured
cutting temperature of each
machining section at the spindle during the
cutting process of the differential shell; determining the pulsation significance according to the distribution of the cutting resistance in the reference sequence for any sampling point; calculating the thermal
field energy dispersion according to the temperature change of the
machining section; fusing the pulsation significance and the thermal
field energy dispersion to obtain a mismatch factor, and combining a preset
inertia weight to obtain an adaptive
inertia weight, which is input into a
particle swarm optimization algorithm to output the optimal spindle speed and feed speed combination. The present application enables the optimization
algorithm to perceive the
thermal coupling fluctuation and physical
instability state in real time, realizes adaptive jump optimization of the
production line parameters, and improves the
machining size precision and surface quality of the differential shell.