A multiobjective optimization method for robotic grinding of aeroengine blades based on FANP-GRA

Robotic grinding has become an effective method for improving the surface quality of aeroengine blades. To address the asymmetric relationship between the surface roughness and the material removal rate during grinding of these types of blades, a multiobjective optimization method for the robotic grinding process parameters of aeroengine blades was proposed based on an integration of the Fuzzy Analytic Network Process (FANP) and a Gray Relational Analysis (GRA). First, the FANP assigned different Intuitionistic Trapezoidal Fuzzy Numbers (ITrFNs) according to the importance of the grinding parameters, which quantified the uncertainties in their relationships. Second, GRA was employed to address the mapping relationship between multiple grinding objectives and the process parameters, converting the multiobjective optimization problem into a single-objective problem. Then, through a mean analysis, the combination of process parameters that had the greatest impact on the multiobjective quality indicators was identified. Finally, an experimental validation revealed that the surface roughness was reduced by 7.76%, the material removal rate was increased by 22.72%, and the gray relational degree was increased by 7.18%, achieving the goal of simultaneously reducing the surface roughness and increasing the material removal rate.