The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics an...The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.展开更多
In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarch...In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station.Moreover,existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes.Here,an improved clustering approach is anticipated to attain energy efficiency by implementingMapReduction for regulatingmapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping.In order to optimize the network performance,this work considers intelligent behaviors’to adapt with network changes and to introduce computational intelligence ability.In the proposed research,improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption.Node upgradation is performed by integratingMap reduction to attain modification in Hop size of nodes.This variation reduces communication complexities.Therefore,network lifetime is increased,and redundancy is reduced.While comparingwith existing approaches here,sleep and wake-up nodes are considered for data transmission.The proposed algorithm clearly demonstrates 50%,16%&12%improvement in nodes lifetime,residual energy and throughput respectively compared to other models.Also it shows progressive improvement in reducing average waiting time,average queuing time and average energy utilization as 30%,20%and 46%respectively.Simulation has been done in NS-2 environment for distributed heterogeneous networks.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52005078,U1908231,52075076).
文摘The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.
文摘In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station.Moreover,existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes.Here,an improved clustering approach is anticipated to attain energy efficiency by implementingMapReduction for regulatingmapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping.In order to optimize the network performance,this work considers intelligent behaviors’to adapt with network changes and to introduce computational intelligence ability.In the proposed research,improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption.Node upgradation is performed by integratingMap reduction to attain modification in Hop size of nodes.This variation reduces communication complexities.Therefore,network lifetime is increased,and redundancy is reduced.While comparingwith existing approaches here,sleep and wake-up nodes are considered for data transmission.The proposed algorithm clearly demonstrates 50%,16%&12%improvement in nodes lifetime,residual energy and throughput respectively compared to other models.Also it shows progressive improvement in reducing average waiting time,average queuing time and average energy utilization as 30%,20%and 46%respectively.Simulation has been done in NS-2 environment for distributed heterogeneous networks.
