Conical picks are one kind of cutting tools widely used in engineering machinery. In the process of rock breaking, the conical pick bears great cutting force and wear. To solve the problem, a new method, conical pick assisted with high pressure water jet, could break rock effectively, and four different configuration modes of water jet were presented. In this paper, based on the analysis of the different water jet configuration’s advantages and disadvantages, experiments on front water jet, new typed rear water jet, and the combination of those two water jet configuration modes were conducted to study the assisting cutting performance and obtain the quantitative results.

Boring bars are widely used in deep hole machining. Due to the low stiffness of the long cantilever boring bar, vibration often occurs in the boring process. Vibration suppression can permit higher productivity and a better surface finish. To improve the performance of boring operations, a variable stiffness dynamic vibration absorber (DVA) is added inside the boring bar to reduce the vibration. The stiffness of the DVA is provided by two rubber bushes placed inside the DVA. By changing the axial compression, the stiffness of the DVA can be adjusted to accommodate different excitation frequencies. By establishing the relationship between the stiffness of the DVA and the axial compression of the rubber bush, the optimal stiffness for different excitation frequencies can always be found. A boring experiment is conducted, and the results show that, by selecting a reasonable axial compression, the vibration of the boring bar can be effectively suppressed.

No abstract

Rubber bush is used in dynamic vibration absorber as dissipating devices in damping boring bar. These devices actually have to support radial load in compression when chattering occurs. Mastering the behavior of the radial stiffness of the rubber bush implies an accurate understanding of dynamic vibration absorber. The behavior is, however, complex due to the changeable cross-sectional shape and boundary conditions of the rubber bush. By using artificial neural network, the radial stiffness can be predicted efficiently. According to the authors' knowledge, simulations and tests on radial stiffness of the rubber bush under combined different cross-sectional shape and boundary conditions by using artificial neural network have not been performed yet. The purpose of this study is thus to find the law of radial stiffness of rubber bush under different cross-section shapes and axial precompression conditions. In order to achieve this aim, simulations and tests under different chamfering sizes and axial pre-compression by using artificial neural network were first carried out.