• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2020, Vol. 56 ›› Issue (7): 193-203.doi: 10.3901/JME.2020.07.193

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Multi-objective Drilling Parameters Optimization Method for CFRP/Ti Stacks

LIU Shunuan, XIA Wenqiang, WANG Ning, SONG Ye, LUO Bin, ZHANG Kaifu   

  1. School of Mechanical Engineering, NorthWestern Polytechnical University, Xi'an 710072
  • Received:2019-08-12 Revised:2019-12-13 Online:2020-04-05 Published:2020-05-12

Abstract: Carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks are widely used in aerospace field because of their excellent performance. In order to ensure the coaxiality of the hole and processing efficiency, the same parameters are usually used to drill CFRP/Ti stacks. The material properties and cutting properties of CFRP and titanium alloys are different. There are many quality defects in the integrated drilling, such as CFRP tearing, burr at the exit of titanium alloys, hole diameter deviation and so on. It is difficult to find the appropriate parameters to minimize the quality defects due to the different influence of process parameters on each quality defect or even the contradiction. In order to obtain the optimum drilling parameters and improve the comprehensive quality of drilling, the multi-objective optimization method of drilling parameters for CFRP/Ti stacks is studied. A multi-objective optimization model for drilling parameters of CFRP/Ti stacks is established according to the actual process conditions. Combining the characteristics of discretization of drilling parameters, an exhaustive search algorithm for Pareto optimal process parameters set is constructed based on Pareto dominance principle, and an optimal process parameters decision algorithm is constructed based on forbearing stratified sequencing method. Taking T700-TDE85/Ti-6Al-4V stacks as the object, satisfactory optimal process parameters are obtained, and the feasibility and validity of the method are verified.

Key words: CFRP/Ti stacks, multi-objective optimization, process parameters, pareto optimization, forbearing stratified sequencing method

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