Prediction of Young’s modulus of weathered igneous rocks using GRNN, RVM, and MPMR models with a new index

Ceryan, Nurcihan

doi:10.1007/s11629-020-6331-9

Cited by 6 publications

(1 citation statement)

References 106 publications

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“…As the generalized regression neural network (GRNN) has great advantages in learning speed and approximation ability compared with the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN), it is first adopted in this research to establish the mathematical mapping relationship between the tool information and axial limiting cutting depth a plim [24,25]. The typical topologic structure of a GRNN model is described in Figure 2, which was composed of four layers of neurons, including the input layer, pattern layer, summation layer, and output layer [26,27]. The input variable vector X = [X 1 , X 2 , .…”

Section: The Grnn Model In Predicting the Tool Clamping Depth-depende...mentioning

confidence: 99%

Chatter Stability Prediction and Process Parameters’ Optimization of Milling Considering Uncertain Tool Information

Lin

He²,

Wang

et al. 2021

Symmetry

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Stability is the prerequisite of a milling operation, and it seriously depends on machining parameters and machine tool dynamics. Considering that the tool information, including the tool clamping length, feeding direction, and spatial position, has significant effects on machine tool dynamics, this paper presents an efficient method to predict the tool information dependent-milling stability. A generalized regression neural network (GRNN) is established to predict the limiting axial cutting depth, where the machining parameters and tool information are taken as input variables. Moreover, an optimization model is proposed based on the machining parameters and tool information to maximize the material removal rate (MRR), where the GRNN model is taken as the stability constraint. A particle swarm optimization (PSO) algorithm is introduced to solve the optimization model and provide an optimal configuration of the machining parameters and tool information. A case study has been developed to train a GRNN model and establish an optimization model of a real machine tool. Then, effects of the tool information on milling stability were discussed, and an origin-symmetric phenomenon was observed as the feeding direction varied. The accuracy of the solved optimal process parameters corresponding to the maximum MRR was validated through a milling test.

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Section: The Grnn Model In Predicting the Tool Clamping Depth-depende...mentioning

confidence: 99%