MOTION ERROR ESTIMATION OF 5-AXIS MACHINING CENTER USING DBB METHOD

In order to estimate the motion errors of 5-axis machine center, the double ball bar （DBB） method is adopted to realize the diagnosis procedure. The motion error sources of rotary axes in 5-axis machining center comprise of the alignment error of rotary axes and the angular error due to various factors, e.g. the inclination of rotary axes. From sensitive viewpoints, each motion error is possible to have a particular sensitive direction in which deviation of DBB error trace arises from only some specific error sources. The model of the DBB error trace is established according to the spatial geometry theory. Accordingly, the sensitive direction of each motion error source is made clear through numerical simulation, which is used as the reference patterns for rotational error estimation. The estimation method is proposed to easily estimate the motion error sources of rotary axes in quantitative manner. To verify the proposed DBB method for rotational error estimation, the experimental tests are carried out on a 5-axis machining center M-400 （MORISEIKI）. The effect of the mismatch of the DBB is also studied to guarantee the estimation accuracy. From the experimental data, it is noted that the proposed estimation method for 5-axis machining center is feasible and effective.

A Real-time NURBS Surface Interpolator for 5-axis Surface Machining

A real-time non-uniform rational B-spline （NURBS） surface interpolator is proposed and 5-axis machining method with a flat-end cutter is discussed. With the Taylor expansion and the coordinate transformation, the algorithms of NURBS interpolation, cutter effective machining radius, cutter offsetting and.inverse kinematics are deduced and implemented, respectively. Different from the conventional free-form surface machining, the proposed interpolator can real-time generate the motion commands of computer numerical control （CNC） machines with CC feedrate, rather than that of CL. An example part surface is demonstrated and the results of simulation show that the proposed method can be applied in actual 5-axis surface machining.

Development of tool-path generation module for 5-axis control machining of impellers

An impeller is difficult to machine because of severe collision due to the complex shape,overlapping and twisted shape of the impeller blades.So,most computer aided manufacturing(CAM)software companies have developed CAM module for manufacturing impeller according to their CAM software.But these dedicated modules are difficult to use for inexperienced users.The purpose of this work is to develop a tool-path generation module for impellers.For this purpose,it is based on Visual Basic language and used CATIA graphical environment.The result of simulation for generated tool-path by the module is satisfactory.And it has slow processing speed compared to other commercial modules,but it is easy to use.

Tool Orientation Optimization and Path Planning for 5-Axis Machining

Tool path generation is a fundamental problem in 5-axis CNC machining, which consists of tool orientation planning and cutter-contact(CC) point planning. The planning strategy highly depends on the type of tool cutters. For ball-end cutters, the tool orientation and CC point location can be planned separately;while for flat end cutters, the two are highly dependent on each other. This paper generates a smooth tool path of workpiece surfaces for flat end mills from two stages: Computing smooth tool orientations on the surface without gouging and collisions and then designing the CC point path. By solving the tool posture optimization problem the authors achieve both the path smoothness and the machining efficiency. Experimental results are provided to show the effectiveness of the method.

Optimization of cutter-location for 5-axis NC machining freeform surface with a flat-end cutter

axis NC machining freeform surface with a flat end cutter can theoretically improve material removal rate and surface finish, but it is very difficult to generate a gouge free cutter location. A new method of generating optimal cutter path is proposed to define the effective radius of a flat end cutter and determine the optimal step forward distance and step over distance. Thereby improving the NC machining efficiency and quality of freeform surfaces.

Development of practical postprocessor for 5-axis machine tool with non-orthogonal rotary axes

In order to develop a practical postprocessor for 5-axis machine tool,the general equations of numerically controlled(NC) data for 5-axis configurations with non-orthogonal rotary axes were exactly expressed by the inverse kinematics,and a windows-based postprocessor written with Visual Basic was developed according to the proposed algorithm.The developed postprocessor is a general system suitable for all kinds of 5-axis machines with orthogonal and non-orthogonal rotary axes.Through implementation of the developed postprocessor and verification by a cutting simulation and machining experiment,the effectiveness of the proposed algorithm is confirmed.Compatibility is improved by allowing exchange of data formats such as rotational total center position(RTCP) controlled NC data,vector post NC data,and program object file(POF) cutter location(CL) data,and convenience is increased by adding the function of work-piece origin offset.Consequently,a practical post-processor for 5-axis machining is developed.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

Realization Methodology of a 5-axis Spline Interpolator in an Open CNC System

By making use of the advantages of non-uniform rational B-spline （NURBS） curves to represent spatial curves, an instruction format with double NURBS curves suitable for 5-axis coordinated real-time interpolation is presented to replace the current 5-axis coordinated linear interpolation method defective in low-speed, low-accuracy and enormous numerical control （NC） files in sculptured surface machining. A generation procedure of the NC files with the presented format is introduced and the method to realize the interpolation in an open computer numerical control （CNC） system is developed by ourselves. These illustrated the feasibility of the proposed method and its capability of avoiding all the shortages of 5-axis linear interpolation method.

A NEW ALGORITHM BASED ON TRIANGULATION FOR 5 AXIS MACHINING OF SCULPTURED SURFACES

NC machining path of sculptured surfaces in CAD/CAM system plays an important role on manufacture. This paper describes a new algorithm for 5 axis machining of sculptured surfaces and the algorithm is interference free. The approach includes: (1) the tesselation of the parametric surfaces into triangles; (2) building topological relations among triangles;(3) 5 axis tool path generation; (4) interference detection and tool position correction.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature.

Circular Interpolation Algorithms of 5-Axis Simultaneous CNC System

Spatial circular arc can be machined conveniently by a 5-axis NC machine tool.Based on the data sampling method,circular interpolation in two-dimensional plane is discussed briefly.The key is to solve the problem of circular center ex- pressed in the workpiece coordinate system by means of the transformation matrix.Circular interpolation in three-dimensional space is analyzed in detail.The method of undetermined coefficient is used to solve the center of the spatial circle and the method of coor- dinate transformation is used to transform the spatial circle into the XY-plane.Circular arc in three-dimensional space can be ma- chined by the positional 5-axis machining and the conical surface can be machined by the continuous 5-axis machining.The velocity control is presented to avoid the feedrate fluctuation.The interpolation algorithms are tested by a simulation example and the inter- polation algorithms are proved feasible.The algorithms are applied to the 5-axis CNC system software.

基于改进LeNet-5网络的堆芯燃料组件编码识别

在核电站堆芯核燃料组件水下组装作业中,需要通过视觉技术进行组件编码的识别以便准确定位组件的安装位置。针对水下环境中弱光照等问题导致了图像质量的降低,本文通过乘方增强算法、OSTU算法、CLAHE算法和拉普拉斯变换的方法来实现堆芯燃料组件编码字符水下图像的增强。为了提高编码识别效果,提出了一种整合LeNet-5网络和支持向量机(SVM)的模型,在网络中添加BN(Batch Normalization)层与Dropout层来加速网络的运行速度,并改进Sigmoid函数,增加函数的平滑性,以此来减少梯度消失。实验表明,在自定义数据集上的验证准确率为99.82%,识别率为100%,相比于其他模型有显著的提升。

A Collaborative Machine Learning Scheme for Traffic Allocation and Load Balancing for URLLC Service in 5G and Beyond

Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .

Advancing 5G Network Applications Lifecycle Security:An ML-Driven Approach

As 5th Generation(5G)and Beyond 5G(B5G)networks become increasingly prevalent,ensuring not only networksecurity but also the security and reliability of the applications,the so-called network applications,becomesof paramount importance.This paper introduces a novel integrated model architecture,combining a networkapplication validation framework with an AI-driven reactive system to enhance security in real-time.The proposedmodel leverages machine learning(ML)and artificial intelligence(AI)to dynamically monitor and respond tosecurity threats,effectively mitigating potential risks before they impact the network infrastructure.This dualapproach not only validates the functionality and performance of network applications before their real deploymentbut also enhances the network’s ability to adapt and respond to threats as they arise.The implementation ofthis model,in the shape of an architecture deployed in two distinct sites,demonstrates its practical viability andeffectiveness.Integrating application validation with proactive threat detection and response,the proposed modeladdresses critical security challenges unique to 5G infrastructures.This paper details the model,architecture’sdesign,implementation,and evaluation of this solution,illustrating its potential to improve network securitymanagement in 5G environments significantly.Our findings highlight the architecture’s capability to ensure boththe operational integrity of network applications and the security of the underlying infrastructure,presenting asignificant advancement in network security.

基于机器学习优化建模的GF-5影像土壤总氮量预测填图

[目的/意义]大范围快速检测土壤养分并实现基于GF-5影像对土壤总氮量精准填图。[方法]基于实测土壤光谱和GF-5星载高光谱数据,引入偏最小二乘回归(Partial Least Squares Regression,PLSR)、反向神经网络(Back Propagation Neural Network,BPNN)和以核函数Poly为驱动支持向量机(Support Vector Machine,SVM)的机器学习算法,构建3种土壤总氮(Total Nitrogen,TN)反演模型,并以十折交叉验证方法确定各模型的最优解。采用多元散射校正(Multiple Scattering Correction,MSC)获取的波段特征值使模型表现更佳。[结果和讨论]MSCPoly-SVM模型经测试集样本检验,其决定系数(R2)、均方根误差(Root Mean Squared Error,RMSE)和相对分析误差(Residual Prediction Deviation,RPD)分别是0.863、0.203和2.147。将该模型用于星载GF-5号影像数据进行土壤总氮含量的反演填图。由填图结果可见,黑龙江省富锦市建三江垦区86.1%的土地总氮量均在2.0 g/kg以上,土地氮含量以一等地块和二等地块为主,而三等地块和四等级地块仅占总面积的11.83%。研究区内土壤氮要素储备充足,总氮高背景值主要集中在中部靠近河流两岸、呈北东东向分布。本研究土壤总氮预测成图结果与前人1∶25万地球化学插值和航空高光谱影像(Compact Airborne Spectrographic Imager,CASI)和(Shortwave Infrared Airborne Spectrographic Imager,SASI)填图效果具有很好的一致性。[结论]研究表明星载GF-5高光谱数据在土壤全氮含量监测填图和可视化分析上具有极高的潜力,本研究提出方法可为今后大范围开展定量检测土壤养分状况以及合理施肥提供技术支撑。

MTS-05 PDM多频代播机核心技术及应用

深入探讨MTS-05 PDM 5 kW多频代播机的研发背景、核心技术、应用价值及其对广播技术领域的影响。在中波广播技术面临频率固定性和调频机制局限的挑战背景下,MTS-05 PDM 5 kW多频代播机作为技术创新的产物,通过集成复杂可编程逻辑器件(Complex Programmable Logic Device,CPLD)逻辑频率选通技术、直接数字合成(Direct Digital Synthesizer,DDS)高频激励机制和智能多频网络切换技术,实现了多频次高效、安全播出。设备不仅克服了传统发射机的调频烦琐和空间限制问题,还提升了播出质量和安全播出效率,展示了智能化运维与资源优化的典范。

整体叶轮5轴数控加工工艺仿真技术研究

分析了整体叶轮的造型轮廓和加工特点,依据叶轮的整体形状和加工工艺技术要求,确定了基于UG NX软件的5轴加工策略,生成刀路轨迹,并完成整体叶轮零件的数控仿真的加工。结果表明:经过加工工艺的优化,通过在5轴加工中心加工整体叶轮,零件的表面质量和加工效率得到全面提升,对叶轮类零件的加工有一定借鉴的意义。

基于NX Post Builder双转台5轴加工中心的3+2定轴后处理流程的研究

按照机床运动特性,可将5轴加工分为5轴联动加工和3+2定轴加工。5轴联动加工多用于复杂曲面的半精加工和精加工,3+2定轴加工多用于有多个空间成型面的定位加工。以适配Heidenhain TNC640数控系统的DMG DMU65加工中心为例,对NX后处理中与3+2定轴加工有关的Tcl命令进行分析,阐述了tcl代码的工作流程,推导了坐标系向量旋转矩阵公式,最后通过Vericut仿真验证后处理的正确性。

EQUIVALENT NORMAL CURVATURE APPROACH MILLING MODEL OF MACHINING FREEFORM SURFACES

A new milling methodology with the equivalent normal curvature milling model machining freeform surfaces is proposed based on the normal curvature theorems on differential geometry. Moreover, a specialized whirlwind milling tool and a 5-axis CNC horizontal milling machine are introduced. This new milling model can efficiently enlarge the material removal volume at the tip of the whirlwind milling tool and improve the producing capacity. The machining strategy of this model is to regulate the orientation of the whirlwind milling tool relatively to the principal directions of the workpiece surface at the point of contact, so as to create a full match with collision avoidance between the workpiece surface and the symmetric rotational surface of the milling tool. The practical results show that this new milling model is an effective method in machining complex three- dimensional surfaces. This model has a good improvement on finishing machining time and scallop height in machining the freeform surfaces over other milling processes. Some actual examples for manufacturing the freeform surfaces with this new model are given.