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• W2560482892 abstract "The forces acting on a single drag-typed pick are important parameters for excavation machine design and selection. For better prediction of tool forces including cutting and normal forces generally, a general model of cutting forces was proposed based on theoretical models. Also, a general model of normal forces was proposed using the ratio of the normal force to cutting force. Subsequently, the effect of relevant geometrical parameters on the cutting force was discussed. The friction angle between pick and rock, the cone angle and the attack angle were employed to develop the cutting force models of conical picks. The rake angle and the friction angle between pick and rock were included in the peak cutting force model of radial picks. Finally, the peak and mean cutting forces models of conical picks and the peak cutting force model of radial picks under unrelieved cutting mode were developed using principle component regression analysis and ridge regression analysis based on the raw data from linear full-scale cutting test. The results show the proposed regression coefficients and equations are more reasonable physically. Some empirical models used for practical application were then developed by introducing relevant modified coefficients considering tool wear, relieved cutting and complex shapes of picks. The results show a good agreement between the measured and predicted cutting force of sharp picks under unrelieved cutting mode. The performance of modified models using relevant modified coefficients would be decreased to a certain extent. However, they are all statistical valid according to the results of t-test. The models of this work can be used for preliminarily estimation of tool forces acting on drag-typed picks." @default.
• W2560482892 created "2016-12-16" @default.
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• W2560482892 date "2017-02-01" @default.
• W2560482892 modified "2023-10-09" @default.
• W2560482892 title "Empirical models for tool forces prediction of drag-typed picks based on principal component regression and ridge regression methods" @default.
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• W2560482892 doi "https://doi.org/10.1016/j.tust.2016.11.006" @default.
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