The Research on Machining of Ultrahigh Strength Steel Used for Aircraft Landing Gear and Optimization of Cutting Parameters
|School||Harbin University of Science and Technology|
|Course||Mechanical Design and Theory|
|Keywords||Ultra strength steel300M multiple linear regression machinability optimization of cutting parameters improved NSGA-Ⅱ algorithm|
Due to landing gear supporting aircraft weight and absorbing impact energy asa part, set high demands on what kind of materials used. Ultra high strength300M (40CrNi2Si2MoVA) has unique mechanical property, making itself for thatgear and aircraft difficult-to-cut materials after Titanium alloy and super alloy.The key in market is how to improve machining quality and decrease productioncost. Based on experiment, analyzing the machinability of this steel and buildinga series of empirical models are to provide reliable basis for optimization ofcutting parameters.Firstly,300M steel according to its poor machinability is turned in the threefactors and four levels orthogonal test, and then range analysis on how cuttingparameters (cutting speed v, feed f and back cutting depth ap) affect cuttingforces and cutting temperature is done. Through square sum of minimized errorsearching for optimal function match and multiple linear regression equation weestablish empirical models of those forces and that temperature. Those modelsare verified by data collected in turning spot, and more the results show thatthose empirical models can predict actual field greatly.Secondly, analyze on factors that affect machinability. Secondly, analyzecutability from these four aspects: cutting forces and cutting temperature, surfacequality, chip, and tool wear. Further more, the main measures improve thatmachinability including heat treatment changing mechanical property, differentsurroundings and new technology, showing that heat treatment is better thanothers two.Moreover, there are minimum cutting forces and lowermost cuttingtemperature, maximum productivity, and lowest production cost used as objectivefunctions confined by constraint functions from machine processing environment, surface quality as well. NSGA-Ⅱ algorithm (non-dominated sorting in geneticalgorithm) is improved by means of adding interval scale control factors tooriginal codes to monitor generations, avoiding inadequate iterations that makenon-uniform distribution and lacking solutions of Pareto spatial set, andexcessive tourments that bring about calculating redundancy. Cutting parametersare optimized by improved NSGA-Ⅱ algorithm compared to GA (GeneticAlgorithm), showing the former algorithm gets ideal Pareto optimized set.