基于遗传算法的采煤机螺旋滚筒多目标优化设计

Shearer's Helical Drum Multi-objective Optimization Design Based on GA

为实现采煤机螺旋滚筒截煤和装煤时综合性能最优,基于虚拟样机技术和离散元理论,得到了滚筒各截割性能指标与装煤率随不同结构及运动参数的变化规律,依据机械优化设计理论建立了各性能指标的评价函数。选取螺旋升角、截线距、转速和牵引速度为设计变量,建立了以不同性能指标为分目标的多目标优化模型,利用遗传算法求解得到了最优的结构参数和运动参数。结果表明,利用遗传算法优化后滚筒的最大切削面积增大247mm2,截割比能耗减小0.014kW·h/m3,截割功率减小10.8kW,截割阻力减小7 085kN,装煤率提高1.7%,有效地提升了滚筒的综合性能。研究结果为滚筒结构参数和运动参数的选取提供了数据支撑,具有一定的工程应用价值。

In order to realize shearer's helical drum optimal comprehensive performances of cutting and loading,the changing of cutting performance indexes and the loading rate changed with the structure and motion parameters were obtained based on the virtual prototype technology and the theory of discrete element.On the basis of mechanical optimization design theory,the performance indexes of evaluation function were established.Helix angle,cutting line spacing,speed and traction speed were selected as design variables,multi-objective optimization model with different performance indexes were established,the optimal structure parameters and motion parameters were obtained by GA.The results show that the biggest drum cutting area increases 247 mm2,specific energy consumption reduces 0.014 kW·h/m3,cutting power reduces 10.8 kW,cutting resistance decreases 7085 kN and the loading rate improves 1.7%,it enhances the comprehensive performance of drum effectively.The research provides the data support of the structure and motion parameters'selection,it has certain engineering application values.