Optimization of Machining Parameters in Blisk Processing Based on Tool Reliability

Abstract

Machining parameters are essential factors affecting the machining efficiency and tool life. Tool reliability varies with the process. Tool reliability affects the life of the tool, and then impacts the processing quality and manufacturing cost. Therefore, machining parameters optimization considering tool reliability is essential and scientific. In this paper, firstly the reliability model of tool life was solved by Markov Chain Monte Carlo (MCMC) method. Then taking the average tool life as the constrain condition, a multi-objective optimization algorithm that integrates the gray correlation analysis (GRA), radial basis neural network(RBF) and particle swarm optimization(PSO) algorithm (GRA-RBF-PSO) was used to search for optimal machining parameters of blisk-tunnel processing. At last, experiments were carried out to validate optimized results. The experimental results indicated that the reliability-based optimization of machining parameters can effectively improve the tool life and as well as ensure smaller cutting force and larger material removal rate during blisk-tunnel processing.

Optimization Method of Tool Parameters and Cutting Parameters Considering Dynamic Change of Performance Indicators

“…Juan et al [ 5 ] used the orthogonal cutting simulation method to obtain the relevant performance indicator values as optimization parameters, and proposed a multi-objective particle swarm optimization algorithm to optimize the performance indicator values. Zhang et al [ 6 ] used the Markov chain Monte Carlo method to solve the reliability model of tool life, and then used the multi-objective optimization algorithm combining grey correlation analysis, radial basis neural network and particle swarm optimization algorithm to search for the optimal processing parameters of the whole blade-disc tunnel processing. Mohammed et al [ 7 ] took cutting force and surface roughness as performance indicators and combined gray correlation method (GRA) and expectation function analysis (DFA) to optimize the milling parameters in the milling process of epoxy glass fiber to obtain the best combination of milling parameters.…”