The underwater tapping machine is composed of a center bit, a tapping cutter, a seal box, a main drive box, a boring bar assembly, a envelop, a gear case, a counter and so on. The drive system in underwater tapping ma...The underwater tapping machine is composed of a center bit, a tapping cutter, a seal box, a main drive box, a boring bar assembly, a envelop, a gear case, a counter and so on. The drive system in underwater tapping machine consists of a worm drive, a gear drive system and a screw drive. The worm drive is in the main drive box. The worm is connected with a hydraulic motor and driven by the hydraulic motor. The gear drive system is a combined gear train which is the combinations of the fixed axes and differential gear train in the gear case. On the one hand, by means of the fixed axes gear trains the turn and power of transmission shaft are transferred to the boring bar and the screw rod, causing differential turn between the boring bar and the screw rod. On the other hand, the turns of the boring bar and the screw rod are transferred to the differential gear train. The differential gear train is used to drive a special counter to count axial travel of the boring bar. The screw drive is composed of a feed screw and a nut on the boring bar. There is the differential turn between the boring bar and the feed screw. By means of the nut, the boring bar can feed automatically. With the movement of the sliding gear 7 in the gear case, the designed drive system can also be provided with the ability of fast forward and fast backward movement of the boring bar in its idle motion, resulting in the increase of the tapping efficiency.展开更多
露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模...露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。展开更多
基金supported by the National High Technology Research and Development Program of China(863 Program, Grant No.2002AA602012-2)
文摘The underwater tapping machine is composed of a center bit, a tapping cutter, a seal box, a main drive box, a boring bar assembly, a envelop, a gear case, a counter and so on. The drive system in underwater tapping machine consists of a worm drive, a gear drive system and a screw drive. The worm drive is in the main drive box. The worm is connected with a hydraulic motor and driven by the hydraulic motor. The gear drive system is a combined gear train which is the combinations of the fixed axes and differential gear train in the gear case. On the one hand, by means of the fixed axes gear trains the turn and power of transmission shaft are transferred to the boring bar and the screw rod, causing differential turn between the boring bar and the screw rod. On the other hand, the turns of the boring bar and the screw rod are transferred to the differential gear train. The differential gear train is used to drive a special counter to count axial travel of the boring bar. The screw drive is composed of a feed screw and a nut on the boring bar. There is the differential turn between the boring bar and the feed screw. By means of the nut, the boring bar can feed automatically. With the movement of the sliding gear 7 in the gear case, the designed drive system can also be provided with the ability of fast forward and fast backward movement of the boring bar in its idle motion, resulting in the increase of the tapping efficiency.
文摘露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。