Blade sequencing optimization of aero-engine based on deep reinforcement learning

The unreasonable sorting of single-stage rotor blades leads to the over-tolerance of rotor unbalance, which is the main cause of excessive engine vibration. Aiming at the problems of long search time, poor repeatability, weak adaptability and difficulty in obtaining global optimum by using heuristic algorithm for blade sorting, this paper presents a deep reinforcement learning method is proposed to solve the blade ordering problem. A pointer network model including an encoder and a decoder structure is established. For the case where the blade data cannot be obtained, the unbalance of the single-stage rotor is used as the reward function, and the pointer network model is trained by the Actor Critic reinforcement learning algorithm. The experimental results show that the trained enhanced pointer network model can directly perform end-to-end reasoning on the input sequence, avoiding the iterative solution process of traditional heuristic algorithms, and has high solution efficiency. Using the enhanced pointer network blade sorting optimization model in this paper to sort a set of blade sequences, the unbalanced value of the rotor after sorting is 14.78 g.mm, which is 84.8% better than the genetic algorithm, and the search speed is increased by 95.9%. The results show that the method can quickly and accurately give the arrangement order of the leaves, and the proposed model has generalization. It can provide a reliable measurement method for rotor assembly measurement of large engine manufacturing enterprises such as China Aero-Engine Company.