Optimization of grinding parameters based on parts' friction properties

An optimization model with multiple-input and multiple-output was achieved using genetic algorithm method and neural network method. Surface roughness parameters (arithmetic mean deviation, surface bearing index, core fluid retention index and valley fluid retention index) were taken as input layer factors, and the output layer factors were grinding parameters (wheel linear speed, workpiece linear speed, grinding depth and longitudinal feed rate). Moreover, this model was applied to predict grinding parameters for special surface topography aiming at different friction performances in hydrodynamic lubrication. Grinder, grinding wheel and workpiece size different from sample experiment were applied in the verification test. Results show that the maximum error between predicted values and experiments is only 12.87%, which implies the good accuracy, reliability and universality of this optimization model. This optimization model can efficiently improve the design efficiency of process plan.