Abstract: In order to improve the decision-making efficiency of module substitution and the flexibility and production efficiency of the entire cigarette manufacturing system, a substitution degree based SOM neural network model was proposed to classify cigarette blend modules, and the effect of this model was compared with the historical substitution statistical results. The results showed that the substitution degree could better measure the degree of substitution between modules. The larger the substitution degree, the stronger the consistency of the quality indicators in each category, the more similar the quality of the modules, and the more recommended for mutual substitution. When classifying cigarette formula modules with different substitution degree standard values, the larger the value was, the finer the classification was. It was most appropriate to select the substitution degree standard value of 3.06 as the standard of strong substitution of cigarette formula modules for classification where the quality of cigarette blend modules in each category had a high similarity. The classification results of SOM neural networks based on substitution degree showed that the proportion of intra-class substitution was superior to general SOM neural network algorithms, two-stage clustering algorithms, and K-means clustering algorithms. When the substitution degree standard value was 3.06, the intra-class mutual substitution rate could reach 95.39%, while the inter-class substitution rate was less than 5.00%. The replacement rate of modules in the same class was excellent.

Key words: cigarette, blend module classification, substitution degree, SOM neural network