A Prediction Method for Cutting Performance of Shield Tunneling Cutters in Partitions

Abstract

The invention discloses a method for predicting the cutting performance of a shield cutterhead cutter partition, which uses the reduction of the cutting performance reliability of the cutter system to characterize the reduction degree of the cutting performance of the cutterhead partition, and takes the reliability theory combined with the idea of ​​degeneration as the idea, using A reasonable mathematical statistical model, and considering the propulsion environment of the shield, proposes the function of the cutting performance reliability of the tool partition with the propulsion distance of the shield, and draws it in combination with engineering practice considering the propulsion environment of the shield tunnel, the reliability of the tool partition system, and the propulsion distance of the shield tunnel The reference surface group under the space Cartesian coordinate system provides prediction and reference for the relationship between the cutting performance of the shield cutterhead partition and the change of the shield advancing distance in various environments. This patent can provide a scientific method for the active prediction and selection of the opening and tool change position during the construction plan of the shield tunnel. Taking into account the environmental factors of the shield tunneling, the prediction of the cutting performance of the final tool system is more realistic and effective.

Description

technical field [0001] The invention belongs to the technical field of shield tunneling engineering construction, and in particular relates to a method for predicting the partition cutting performance of a shield cutterhead cutter. Background technique [0002] Shield construction method, as one of the important construction methods for tunnel excavation in soft ground, is widely used in the construction of subways in major cities at home and abroad. However, the cutting performance of the cutterhead due to the wear of the thrusting tool of the shield tunneling occasionally seriously affects the propulsion efficiency. At this stage, there is still a lack of an effective method for evaluating the cutting performance of the cutterhead as a function of the advancing distance. [0003] The reduction of the cutting performance of the cutter head is largely due to tool wear, and the research on tool wear at this stage mainly focuses on two aspects: 1) Based on empirical analysis a...

AI Technical Summary

Problems solved by technology

Nowadays, all kinds of shield cutting tool systems are generally equipped with tool wear detection knives to evaluate the cutting performance of the cutter head, but only passive tool change can be realized through the detection knife data, and the formation tool of the shield tunnel cannot be checked before the shield tunneling. Pre-evaluation of the wear situation cannot predict the active tool change point. In addition, due to the limited number of detection knives, it cannot reflect the overall wear of the cutterhead tool system

[0004] Dong Hanjun et al. (China Harbor Engineering, 2013 (04): 11-14) used the formula proposed by Zhang Fengxiang (2004) to calculate the amount of tool wear. The impact on tool wear has been highly recognized by the engineering community, but this model only evaluates the cutting ability of the entire cutter head with the wear amount of a single tool. However, the cutter head is a working system composed of several tools, and only a single tool It is inevitable to overgeneralize when considering the working ability of the entire cutter head

[0005] Wu Jun et al. (Journal of Civil Engineering, 2015, 48 (S1): 250-255) used the partial least squares method to analyze the wear amount of the sample, and finally obtained the transformation function of the tool wear amount with various propulsion parameters. Multiple linear regression analysis, canonical correlation analysis and principal component analysis are integrated, which can effectively prevent the influence of too many input variables on the built model, but still does not take into account the comprehensive impact of various wear conditions of the overall cutter on the machinability of the entire cutterhead , in addition, this method requires a lot of sample data, and it is difficult to obtain enough cutter head wear data in actual engineering. When the ground conditions change, it is necessary to collect data again for calculation, in order to obtain a new prediction formula

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