Abstract
In order to evaluate the assembly precision of the CNC machine tools in different assembly sequences more accurately and effectively, this paper proposes a new assembly sequence deviation model and quality evaluation approach considering characteristic parameters of glued joint. A BP neural network model is established for predicting characteristic parameters of glued joint in each assembly step. The method is able to obtain the assembly sequence with best assembly quality and improve the design tolerance of parts.
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