In order to reduce the repeated disassembly and reassembly in aero-engine fuel nozzle assembly and improve the success rate of one assembly

a key part selective assembly method based on atomization performance prediction was proposed. First

based on the historical assembly data of nozzle

the nozzle geometric precision-atomization performance case library was constructed. Next

considering the impact of large fluctuations in sample space size and nozzle geometric accuracy

as well as poor consistency

the sample space was expanded by adaptive comprehensive oversampling method

and simultaneously the continuous attribute was discretized by improved K-means clustering algorithm. Finally

the correlation between geometric accuracy and atomization performance was established by association rule mining algorithm

and the accuracy of each rule was quantified by rule fitness evaluation method. Based on these association rule sets

the nozzle atomization performance prediction model was constructed to guide nozzle assembly. The research results show that the nozzle atomization performance prediction model proposed in this paper has the best prediction effect

with a prediction accuracy of up to 98.33%

compared with methods such as decision tree

support vector machine

and artificial neural network

which can effectively predict the atomization performance of different parts combination

thus reducing invalid assembly and improving the assembly efficiency of nozzle.