There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.
W referacie przedstawiono problemy doboru parametrów kinematycznych napędu stołu obrotowego automatu malarskiego. Podstawowym kryterium optymalizacji parametrów kinematycznych jest minimalny czas obrotu stołu przy ograniczeniach wynikających z konieczności stabilizacji przedmiotów ułożonych swobodnie na podporach stołu.
EN
The problems of kinematic parameters selection for drive of the painting automaton rotational table drive are presented in the paper. MinimaI time of the table rotation is the basic criterion of the optimisation taking into account limitations coming from necessity of simple-supported objects stabilisation.
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