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- W3176660254 abstract "Drum blades play an important role in the process of coal seam cutting. The blade wear failure will directly reduce the production efficiency of the shearer, and it is difficult to repair, and the replacement cost is high. Therefore, the shearer blade wear is analyzed, and its performance optimization is crucial. The Archerd model of EDEM (Engineering Discrete Element Method) software is used to study the wear of MG2 × 55/250BW thin seam shearer drum blade. The coupling model of the drum with different traction speeds, drum rotational speeds, blade spiral angles and coal walls with different gangue firmness coefficients are established respectively. The wear law of the blade is obtained through simulation analysis. Combined with the analysis of actual working conditions, a multi-objective optimization model was constructed based on blade wear and careful consideration of coal loading rate, productivity, cutting area, and cutting specific energy consumption. The optimal drum structure parameters and kinematic parameters were obtained through a genetic algorithm. The results show that: when the drum cuts coal with a firmness coefficient of 3.5 under the following condition: the spiral angle is 11.56°, the rotational speed is 80.65 r/min, and traction speed is 4.23 m/min, the coal loading rate is 41.65%, the productivity is 206.37 t/h, the maximum cutting area is 1217.0 mm2, the cutting specific energy consumption is 0.8612 kW·h/m3, and the blade wear reaches a minimum of 0.25416 nm. This method takes the wear amount as the main factor and comprehensively considers the drum performance to optimize the parameters of the spiral drum, which has strong theoretical and guiding significance for the design of the shearer drum and coal mining." @default.
- W3176660254 created "2021-07-05" @default.
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- W3176660254 date "2021-10-01" @default.
- W3176660254 modified "2023-10-17" @default.
- W3176660254 title "Wear analysis and performance optimization of drum blade in mining coal gangue with shearer" @default.
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- W3176660254 doi "https://doi.org/10.1016/j.engfailanal.2021.105542" @default.
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