Fatigue crack growth in unidirectional metal matrix composite : Ghosn, L.J., Telesman, J. and Kantzos, P.NASA Lewis Research Center Report No NASA TM-103102 1990 8 pp

Abstract

Multi-hole parts made of difficult-to-cut materials like discs, blisks and casings are common and require high surface quality in aero engines. In workshops, a large number of holes in these parts are drilled successively in one process to ensure their positional accuracy. Due to the fast time-varying drill wear, the surface roughness of the holes is unstable and difficult to be satisfied. To this end, this paper presents a varying-parameter drilling (VPD) method to improve machining efficiency and hole surface roughness for multi-hole parts made of Ni-based superalloy. This method uses varying cutting parameters for each hole to adapt to the varying drill wear. The main issue of this method lies in an optimization problem in which the optimal sequence of cutting parameters need to be found, with the objective of the processing time and the constraint of the hole surface roughness. As the cutting parameter sequence has a high dimension and the surface roughness of all the holes must be guaranteed, the challenge of this optimization problem is the strict constraint with a complicated non-linear boundary of the feasible zone. To address the convergence difficulty of the searching algorithm, a soft computing method based on particle swarm optimization (PSO) algorithm with a self-adaptive penalty method (SAPM) is applied. The hole surface roughness is predicted with a radial basis function (RBF) neural network. Different types of drill wear comprising flank wear, crater wear, chisel wear and outer corner wear are considered, and the grey relational analysis (GRA) is employed to select the input drill wear parameters to the network. The PSO algorithm coupled with the SAPM is used to search the global optimal solution of the optimization problem. It is found that the satisfied solutions can be searched in all the three trials with the proposed algorithm, even though the proportion of feasible solutions is severely fluctuant during the searching process. The drilling experiment confirm that, when compared with the fixed-parameter drilling, the proposed VPD and the soft computing method for solving the optimization problem can effectively improve machining efficiency and surface quality for drilling Ni-based superalloy multi-hole parts.