The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumpt...The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design.展开更多
针对机床的机械故障频发且装配因素难以识别的问题,提出了基于贝叶斯网络的机床装配情景异常推理识别方法。以机械零部件多尺度运动分析为切入点,建立了机床功能-元动作的多尺度映射模型,利用故障模式及影响分析(Failure Mode and Effec...针对机床的机械故障频发且装配因素难以识别的问题,提出了基于贝叶斯网络的机床装配情景异常推理识别方法。以机械零部件多尺度运动分析为切入点,建立了机床功能-元动作的多尺度映射模型,利用故障模式及影响分析(Failure Mode and Effect Analysis,FMEA)方法建立了机床元动作单元关键装配情景构成模型。基于装配情景构成模型建立了元动作单元装配情景的贝叶斯网络结构,利用证据推理法实现了元动作单元装配情景异常概率的智能推理。以蜗轮转动元动作单元为例,构建了蜗轮转动单元装配情景初始贝叶斯网络,获取了蜗轮转动元动作输出的异常概率(由装配因素引起)为2.35%;以蜗轮转动故障为起点进行了贝叶斯网络反向推理,识别出导致蜗轮转动故障的各装配情景异常概率。元动作装配情景的异常识别为实现机床故障装配因素的追溯提供理论依据。展开更多
针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时...针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时变能耗单元,分析了其工作状态及耦合关系对数控机床能耗的影响;基于BPMN2.0规范,构建了能耗单元工作状态BPMN流程模型和BPMN耦合模型,提出了能耗单元工作状态能耗数据及耦合关系时序数据与BPMN模型的数据集成方法,构建了数控机床能耗多源动态特性模型。以某数控铣床加工过程为例验证了所述模型及方法的有效性。展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Grant Nos.51975075 and 52105506)the Chongqing Technology Innovation and Application Program,China(Grant No.cstc2020jscx-msxmX0221).
文摘The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design.
文摘针对机床的机械故障频发且装配因素难以识别的问题,提出了基于贝叶斯网络的机床装配情景异常推理识别方法。以机械零部件多尺度运动分析为切入点,建立了机床功能-元动作的多尺度映射模型,利用故障模式及影响分析(Failure Mode and Effect Analysis,FMEA)方法建立了机床元动作单元关键装配情景构成模型。基于装配情景构成模型建立了元动作单元装配情景的贝叶斯网络结构,利用证据推理法实现了元动作单元装配情景异常概率的智能推理。以蜗轮转动元动作单元为例,构建了蜗轮转动单元装配情景初始贝叶斯网络,获取了蜗轮转动元动作输出的异常概率(由装配因素引起)为2.35%;以蜗轮转动故障为起点进行了贝叶斯网络反向推理,识别出导致蜗轮转动故障的各装配情景异常概率。元动作装配情景的异常识别为实现机床故障装配因素的追溯提供理论依据。
文摘针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时变能耗单元,分析了其工作状态及耦合关系对数控机床能耗的影响;基于BPMN2.0规范,构建了能耗单元工作状态BPMN流程模型和BPMN耦合模型,提出了能耗单元工作状态能耗数据及耦合关系时序数据与BPMN模型的数据集成方法,构建了数控机床能耗多源动态特性模型。以某数控铣床加工过程为例验证了所述模型及方法的有效性。