数控加工路径的最优调度模型设计与实现
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摘要
以往设计出的数控加工路径最优调度模型往往过于注重对数控加工能耗的优化,导致模型收敛性较差,对数控加工效率的优化能力不强。基于上述原因,在保证合理的加工能耗基础上,设计数控加工路径最优调度模型。通过构建数控加工任务DAG模型,对数控加工中的重要路径进行有效提取。对数控加工任务DAG模型进行优化,获取到数控加工路径最优调度模型的顺序模型、并列模型和支线模型。三个模型分别针对任务量较少、任务量较多以及单任务且多路径的数控加工重要路径进行最优调度,并进一步给出以微处理器为中心的硬件架构,对模型进行实现。实验结果表明,所设计的模型具有较好的收敛性和较强的数控加工效率优化能力。
The previously-designed optimal scheduling model of numerical control machining path often pays too much attention on the optimization of numerical control energy consumption,which causes the poor model convergence,and weak optimization ability for numerical control machining efficiency. Because of the above reasons,an optimal scheduling model of numerical control machining path was designed,which can still ensure the reasonable process energy consumption. The DAG model of numerical control machining task is optimized to acquire the sequence model,parallel model and branch model of the optimal scheduling model for numerical control machining path. The three models are used to carry out optimal scheduling for the important paths of multi-path numerical control machining with light task,heavy task and single task. The hardware architecture taking the microprocessor as its center is given. The experimental result shows that the designed model has good convergence,and strong optimization ability for numerical control machining efficiency.
The previously-designed optimal scheduling model of numerical control machining path often pays too much attention on the optimization of numerical control energy consumption,which causes the poor model convergence,and weak optimization ability for numerical control machining efficiency. Because of the above reasons,an optimal scheduling model of numerical control machining path was designed,which can still ensure the reasonable process energy consumption. The DAG model of numerical control machining task is optimized to acquire the sequence model,parallel model and branch model of the optimal scheduling model for numerical control machining path. The three models are used to carry out optimal scheduling for the important paths of multi-path numerical control machining with light task,heavy task and single task. The hardware architecture taking the microprocessor as its center is given. The experimental result shows that the designed model has good convergence,and strong optimization ability for numerical control machining efficiency.
引文
[1]周光辉,苗发祥,李彦广.数控加工中心任务与刀具集成调度模型及改进自适应遗传算法[J].西安交通大学学报,2014,48(12):1-7.
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[10]黄裕春,王宏,文福拴.含区间数的电力应急网络最优调度路径的确定[J].电力系统及其自动化学报,2015,27(1):19-25.
[2]薛明.恶劣天气因素下最优车辆路线调度路径选择[J].计算机仿真,2015,32(3):210-212.
[3]樊凯,梁志宏.云计算信息安全平台的均衡设计方法[J].现代电子技术,2016,39(4):81-85.
[4]薛明,许德刚.基于云网格集成调度的防拥堵车辆路径规划算法[J].计算机科学,2015,42(7):295-299.
[5]雷伟军,程筱胜,戴宁,等.基于改进遗传算法的多模型加工路径规划[J].机械工程学报,2014,50(11):153-161.
[6]杨梅,赵克全.基于多目标规划模型的校车最优配置与调度方案[J].西南师范大学学报(自然科学版),2014,39(6):50-54.
[7]禹美凤,蒋美仙,张烨,等.基于供应链协同的物流配送路径调度优化[J].成组技术与生产现代化,2014,31(1):29-33.
[8]王维哲,赵伟.引入神经网络的应急调度系统设计[J].现代电子技术,2016,39(4):41-43.
[9]黄国兵,金勇,贾荣兴,等.某电能量远方终端双平面网络接口设计[J].西安工程大学学报,2016,30(1):102-106.
[10]黄裕春,王宏,文福拴.含区间数的电力应急网络最优调度路径的确定[J].电力系统及其自动化学报,2015,27(1):19-25.