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附件B：外文翻译英文原资料：（原文为pdf格式） 译文：端铣削自适应切削力的模糊控制策略U. Zuperl , F. Cus, M. MilfelnerFaculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia摘要这篇文章讨论了在高速端铣削时的切削力的模糊适应的控制策略。这项研究是关于运用标准计算机数字控制装置来忧化金属切削过程的整合自适应性控制。它被设计成服务于允许在刀具上对长时间复杂成形加工很有益的切削力时适应性地使切削速度最大化的控制.目的是产生一个可靠的，强有力的人工神经控制器协助自适应协调切削速度来防止过分的刀具磨损，即刀具的磨损量和保持高的排屑率。许多的仿真和实验用来肯定这个体系的功效。关键词：端铣；自适应力控制；模糊1.诸论一个CNC系统遗留下来的缺点是加工参数，如进给速度，切削速度和深度，被离线编程。加工参数通常在加工前根据编程者的经验和加工手册被选择。为了防止损害和避免加工失败。运行的条件通常被设置的很保守。结果是，有很多的CNC系统运行于远远低于忧化标准运行条件下效率差。即使加工参数在离线时通过忧化计算法忧化了，在加工过程中它们也不能被协调起来。为了确保加工产品的质量，为了降低加工成本和提高加工的效率，协调实时加工的参数来符合忧化的加工标准是有必要的。由此，提供在线运行下协调的自适应控制，被有兴趣地研究起来。在我们的自适应控制系统中，不管是在切削条件下变化时，进给速度总是在线协调下来保持一个常数切削力。在这篇文章中，一个简单的模糊控制策略被在智能系统和一些运用模糊控制策略的实验性的仿真中发展起来。结果证明这个目标系统有效地控制在一般端铣削条件下的峰值切削力。力的控制运算法则已经被众多的研究者开发和评估了。被固定的增加比例积分控制器，先前是为铣削现为了一个可协调的增加比例积分控制器，在那里控制器根据变化的切削条件被协调。完整的自适应参考模拟，自适应控制装置方法最初是被Cusand Balic研究的。这些控制器被模拟和求解及实际上地被实现。两项研究发现全布三参数自适应控制器执行得比已固定的递增积分器要好。关于模糊控制系统，Huang and Lin提供了一个先驱活动的介绍性调查，另一个系统性观念被提出。模糊系统对照比例积分微分控制和模糊系统的稳定性分析及管理模糊控制在3中反映.被提到。关于为铣削的自适应切削力控制很多的工作已经被做。然而，很多以前的工作把问题简单化在一个自由度运动上。这次投稿中，我们将考虑到三个自由度上铣削的切削力。文章的组成如下。第二部分主要描述全面的力控制策略。第三部分包括了CNC加工模拟1.第五部分描述了仿真/实验和目标控制计划执行的方法。最后，第六和七部分展现实验结果，结论和以后研究的建议。2.自适应模糊控制器结构一个新的在线控制计划，这个计划被称作自适应模糊控制，是通过使用模糊集合论开发的。这个方法的基本思想是合并人操作者在控制设计中的经验。这个控制策略是用公式表达成许多的规则，这些规则手工执行很简单但是对于用一般的数学运算法则来实现很困难。基于这个新的控制策略，很多复杂的过程能够标准方法似的更容易地和更精确地被控制。模糊控制的目标是保持金属切除率，能可能的高和保持切削力尽可能地接近一个给定的参照值。此外，计算任务和时间可能就像金典或者现代控制理论那样被减少。示意性的控制规则通过使用真实的实验数据被构造出。模糊自适应控制确保了连续地忧化进给速度的控制。这个控制是自动被协调到每一个特殊的切削情况。当轴的负载低的时候，系统增加切削进给到或者超过预先编程的进给速度，直接导致循环周期和产品成本相当大的减少。当轴的负载高时，进给速度就被降低，以保护工作母机不损害和损坏。当系统侦测到极端的切削力时，它会自动停机来保护切削工具。它减少了一定的操作者的监督管理。在线铣削忧化的步骤次序如下：1.预编程进给速度被送到铣床CNC控制器。2.测量出的切削力被送到模糊控制器。3.模糊控制器使用输入的规则来找到（协调）忧化的进给速度，将它送回到机器。4.第一步和第三步被重复直到加工结束。自适应切削力控制器协调进给速度是基于一个测量出的峰值切削力通过布置一个进给速度超过CNC控制器在四轴上的百分比， 真实的切削速度是超过部分和已编程的进给速度。如果进给速度忧化模拟是完美的，忧化的进给速度也将总是等于参照的峰值力。在这种情况下，超出部分的正确率将是100%。为了控制器调整峰值力，力的信息必须在每个采样时间对控制运算法则是有用的。一个探测软件被用来提供这些信息。2.1一个模糊控制器的结构在模糊过程控制中，专门技术被压缩成一个根据关于人操作标准和输入输出关系的系统。运算法则是基于操作者的知识但考虑到过程编辑通过改写误差，它也包括了控制理论。 从而，控制器有输入切削力误差F和第一次不同误差2F，输出变化的进给速度f。模糊控制变化和规则创基础创建从专家操作者那带走。切削力误差和第一次误差的差异被计算，在每一个采样时间k，如_F(k) = Fref F(k)和_2F(k) =_F(k)_F(k1),这里F是测量的切削力，Fref是力的设定点。3.CNC加工模拟在进行实验测试之前，一个CNC加工模拟模拟器被用来估算控制者的设计。过程模拟由人工神经力模拟和进给驱动模拟。人工神经力模拟基于切削条件和已描述的形状切削估算切削力。进给驱动模拟模拟机器对已指定进给速度变化的反应。进给驱动模拟通过检查步的已指定速度的改变被决定。最好的模拟被发现是一个频率为3Hz和节拍时间为0.4s的二级命令系统。对比实验和仿真从7到22mm/s图3显示的速度步调改变结果。进给驱动和人工神经力模拟被结合形成CNC加工模拟。模拟输入是已指定的进给速度，输出是X、Y合成的切削力。切削形状在人工神经力模拟中被定义。模拟器通过比较实验和模拟仿真结果被修改。伴随进给速度改变的各种切削被确定。从0.05到2mm/tooth每一步改变，实验和仿真合力展现如图4。实验结果与在平均和峰值力方面模拟结果联系的很好。明显的差异可能是因为人工神经模拟和没有模拟的系统编辑器的错误。3.1切削力模拟为明白在线切削力模拟，基于流行的反馈原理，一个标准BP人工神经网络（NN）被提出在预备实验期间，它被证明是很有可能直接从实验加工数据提取力模拟。它被用来模拟切削过程。用来模拟的NN需要为进给速度f，切削速度vc 切削轴向深度AD 和切削径向深度RD 4个输入人工神经元。NN的输出是切削力的要素，因此需要两个输出神经元。带优化参数使用的NN详细的布局和神经元的数学原理如图5所示。最好的NN配置包含5，3和7在隐藏层隐藏的神经元。3.2神经网络的布局和其模拟问题的自适应性布局的效果也通过考虑不同的情况而被研究。通过改变在隐藏层的人工神经元的个数来改变布局。为估计个别与神经网络性能有关程序参数的效果，40个不同网络被训练，测试和分析。网络性能使用ETstMax, ETst, ETrn, and ETrnMax四个不同标准和程序周期数来估计。在输入输出层的神经元数通过输入输出参数的数量来决定。由结果得到的如下所述结论： 0.3比率给出可接受的预期误差而掌握比率必须在0.01到0.2之间来最小化程序周期数。为了最小化判断误差，比率在0.001到0.005之间是好的。然而，如果程序周期数也是最小化，掌握比率应该不超过0.004 最佳的隐蔽层节点数是3或6.节点数在2到12或不是3或6的网络也表现的好但是导致更高的程序周期。用正弦函数的网络需要最低的程序周期数，紧跟的是正切函数而用双曲线切线那些需要更高的程序周期。4.数据获得系统和实验设备用在这个获取系统的数据获取设备由测力计，固定模块，硬件和软件如图1所示。切削力使用安在工件和工作台压电测力计测量。当刀具正在切削工件时，力将通过刀具施加到测力计。在测力计上的压电石英产生形变，电荷将会产生。电荷然后通过连接电缆传递到多通道电荷放大器。电荷然后使用多通路放大器放大。在多通路电放大器中，不同参数能被调整以完成必需解决的。在放大器的输出端，电压将对应于取决于设置在放大器中参数的力。接口硬件模块由连接设计块，模拟信号协调模块和一个16通道A/D接口板(PC-MIO-16E-4)。在A/D板里，模拟信号将转变成数了信号，以使LabVIEW软件能读和接收数据。用LabVIEW电压将转变成在X，Y和Z方向的力。用这个程序，三个轴向力要素能同时获得，并能为分析力的变化而显示在屏幕上。选R216-16B20-040型直径16mm10度螺旋角带双刃可互换球状端立铣刀来加工。前角12度R216-1603 M-M型立铣刀被选。立铣刀的材料是P10-P20涂上TiC/TiN ，GC4040。冷却液RENUS FFM用来冷却。模糊控制被智能操纵器模块（Labview），修正进给速度被递到力控制软件和NC机床之间CNC通信设备。控制器能通过存储器共享。在频率1KHz时，超出部分的进给速度，可变DNCFRO对分配力控制软件有用。5.模拟和模糊控制铣实验为检查自适应模糊控制策略的稳定性和耐用度，通过用Simulink and Labview fuzzy Toolset模拟来检查系统。然后，通过在一个CNC铣床的对Ck45和Ck45钢工件改变切削深度的不同实验来改变系统（如图6）R216-16B20-040型直径16mm10度螺旋角带双刃可互换球状端立铣刀被选来进行实验。切削条件为：铣削宽度RD = 3 mm，铣削深度AD = 2mm和切削速度vc = 80m/min.模糊控制的参数相同于对传统系统性能的实验。用模糊控制结构如图1，忧化进给速度，想要的切削力是Fref = 280 N,预编程的进给是0.05mm/teech，允许调整率为0150%。当切削深度改变时，图7是切削力和进给速度的反映。它显示出实验结果，结果中进给速度在线调整来保持切削力在最大想要值。模拟控制器响应在轴向深度一步改变，显示如图8.模拟代表了一个16mm，两面铣刀，在2000rpm时,正遇到一步从轴向深度从3到4.2mm的改变。这步改变发生在2s，在0.5s内控制器返回峰值成参考峰值力在这项研究中模糊控制器的稳定性通过模拟被估算。用在过程增益中小和大步改变测试模拟是为确保系统稳定在一定范围条件内。小的过程增益改变用一个在2000rpm转速下从3到4.2mm轴向深度改度来模拟。大的增益改变用一个轴向深度在2000rpm时从3到6mm改变来模拟。伴随很少的性能降低系统在全布模拟仿真中保持稳定。6.结果和讨论在用不变进给速度（常用切削，如图7a）的第一次实验中,MRR仅仅在最后一步时达到它的固有值。然而，在第二次测试中，使用模糊控制加工相同的工件，平均完成的MRR很接近固有的MRR值。对比图7a和b，人工神经控制铣削系的在切削力是保持在240N左右，自适应铣削系统的进给速度接近于传统CNC铣削系统从C点到D点。从A点到C点，自适应铣削系统的进给速度高于正统CNC系统，因此 ，自适应铣削系统铣削效率提高了。实验结果显示出MRR可能提高高到27%。相比于大多数的现有端铣削控制系统，目标模糊控制系统有下列优势：1.多参数调整。2.对工件形状、刀具形状和工件材料的改变敏感；3.合算和容易实现；4.数学建模方便模拟仿真结果显示使用设计的模糊控制器的铣削过程耐用度、稳定性，比标准的控制器有更高加工效率。实验显示模糊控制器比传统控制器有重大的优势。主要的优势是一个控制器快速响应复杂传感输入而在传统控制器上老的控制运算法则下运行速度受限制。当前研究显示模糊控制比传统控制器有很大的优势。第一个优势是一个模糊控制器能有效率地利用在计划和执行一个控制动作方面比一个工人更巨大的感官信息。第二个优势是模糊控制器快速响应复杂的传感输入而在传统控制器的传统控制法则下的执行速度受到严格的限制。7.结论这次投稿的目的是为介绍一辅助自适应调整进给速度来防止过度刀具磨损，刀具破损和保持高的金属去除率的可靠而耐用的模糊力控制器。带自适应控制策略的智能铣削实验结果表明模糊控制器有高的耐用度和完全稳定性。方法成功应用于实验Heller铣削加工中。目标在线最佳切削条件决定系统在这篇文章中应用于球端铣削，但显然此系统也可延伸到其它的机床上来提高切削效率。Journal of Materials Processing Technology xxx (2005) xxxxxxFuzzy control strategy for an adaptive force control in end-millingU. Zuperl, F. Cus, M. MilfelnerFaculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, SloveniaAbstractThis paper discusses the application of fuzzy adaptive control strategy to the problem of cutting force control in high speed end-millingoperations. The research is concerned with integrating adaptive control with a standard computer numerical controller (CNC) for optimising ametal-cuttingprocess.Itisdesignedtoadaptivelymaximisethefeed-ratesubjecttoallowablecuttingforceonthetool,whichisverybeneficialfor a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjustingfeed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments areconducted to confirm the efficiency of this architecture. 2005 Elsevier B.V. All rights reserved.Keywords: End-milling; Adaptive force control; Fuzzy1. IntroductionA remaining drawback of modern CNC systems is thatthemachiningparameters,suchasfeed-rate,speedanddepthof cut, are programmed off-line. The machining parametersare usually selected before machining according to program-mersexperienceandmachininghandbooks.Topreventdam-age and to avoid machining failure the operating conditionsare usually set extremely conservative.Asaresult,manyCNCsystemsareinefficientandrunun-dertheoperatingconditionsthatarefarfromoptimalcriteria.Even if the machining parameters are optimised off-line byan optimisation algorithm 5 they cannot be adjusted duringthe machining process.Toensurethequalityofmachiningproducts,toreducethemachining costs and increase the machining efficiency, it isnecessary to adjust the machining parameters in real-time, tosatisfy the optimal machining criteria. For this reason, adap-tive control (AC), which provides on-line adjustment of theoperatingconditions,isbeingstudiedwithinterest3.InourAC system, the feed-rate is adjusted on-line in order to main-tain a constant cutting force in spite of variations in cuttingconditions. In this paper, a simple fuzzy control strategy isdeveloped in the intelligent system and some experimentalCorresponding author. Tel.: +386 2 220 7623; fax: +386 2 220 7990.E-mail address: uros.zuperluni-mb.si (U. Zuperl).simulations with the fuzzy control strategy are carried out.The results demonstrate the ability of the proposed system toeffectively regulate peak forces for cutting conditions com-monly encountered in end-milling operations.Force control algorithms have been developed and eval-uated by numerous researchers. Among the most commonis the fixed gain proportional integral (PI) controller origi-nally proposed for milling by 4. Kim et al. 4 proposedan adjustable gain PI controller where the gain of the con-troller is adjusted in response to variations in cutting con-ditions. The purely adaptive model reference adaptive con-troller (MRAC) approach was originally investigated by Cusand Balic 2. These controllers were simulated and evalu-ated and physically implemented by 1. Both studies foundall three-parameter adaptive controller to perform better thanthe fixed gain PI controller. As regards fuzzy control sys-tems, an introductory survey of pioneering activities is givenby Huang and Lin 3, and a more systematic view is pre-sented by in 4. Comparisons of fuzzy with proportional in-tegral derivative (PID) control and stability analysis of fuzzysystems and supervisory fuzzy control are addressed in Ref.3.Much work has been done on the adaptive cutting forcecontrol for milling 2. However, most of the previous workhas simplified the problem of milling into one-dimensionalmotion. In this contribution, we will consider force controlfor three-dimensional milling.0924-0136/$ see front matter 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.jmatprotec.2005.02.1432U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxxThe paper is organised as follows. Section 2 briefly de-scribes the overall force control strategy. Section 3 coversthe CNC machining process model. Section 5 describes thesimulation/experiments and implementation method of pro-posedcontrolscheme.Finally,Sections6and7presentexper-imentalresults,conclusions,andrecommendationsforfutureresearch.2. Adaptive fuzzy controller structureA new on-line control scheme which is called adaptivefuzzy control (AFC) (Fig. 1) is developed by using the fuzzysettheory.Thebasicideaofthisapproachistoincorporatetheexperience of a human operator in design of the controller.The control strategies are formulated as a number of ruleswhich are simple to carry out manually but difficult to im-plement by using conventional algorithm. Based on this newcontrol strategy, very complicated process can be controlledmoreeasilyandaccuratelycomparedtostandardapproaches.The objective of fuzzy control is keeping the metal removalrate (MRR) as high as possible and maintaining cutting forceas close as possible to a given reference value. Furthermore,the amount of computation task and time can be reduced ascompared to classical or modern control theory. Schematiccontrol rules are constructed by using real experimental data.Fuzzy adaptive control ensures continuous optimising feedrate control that is automatically adjusted to each particularcutting situation. When spindle loads are low, the system in-creasescuttingfeedsaboveandbeyondpre-programmedfeedrates, resulting in considerable reductions in cycle times andproduction costs. When spindle loads are high the feed ratesare lowered, safeguarding machine tools from damage frombreakage. When system detects extreme forces, it automati-cally stops the machine to protect the cutting tool. It reducestheneedforconstantoperatorsupervision.Sequenceofstepsfor on-line optimisation of the milling process are presentedbelow.1. Thepre-programmedfeedratesaresenttoCNCcontrollerof the milling machine.2. The measured cutting forces are sent to the fuzzy con-troller.3. Fuzzy controller uses the entered rules to find (adjust) theoptimal feed-rates and sends it back to the machine.4. Steps1and3arerepeateduntilterminationofmachining.The adaptive force controller adjusts the feed-rate by as-signingafeed-rateoverridepercentagetotheCNCcontrolleron a four-axis Heller, based on a measured peak force. Theactual feed-rate is the product of the feed-rate override per-centage and the programmed feed-rate. If the feed-rate opti-misation models were perfect, the optimised feed-rate wouldalways be equal to the reference peak force. In this case thecorrect override percentage would be 100%. In order for thecontroller to regulate peak force, force information must beavailable to the control algorithm at every controller sam-ple time. A data acquisition software (Labview) is used toprovide this information.2.1. Structure of a fuzzy controllerIn fuzzy process control, expertise is encapsulated intoa system in terms of linguistic descriptions of knowledgeabout human operating criteria, and knowledge about theinputoutput relationships. The algorithm is based on theoperators knowledge, but it also includes control theory,throughtheerrorderivative,takingintoconsiderationthedy-namics of the process. Thus, the controller has as its inputs,the cutting force error ?F and its first difference ?2F, andas outputs, the variation in feed rate ?f. The fuzzy controlvariables fuzzification (see Fig. 2) as well as the creationof the rules base were taken from the expert operator. Thecutting force error and first difference of the error are calcu-lated, at each sampling instant k, as: ?F(k)=FrefF(k) and?2F(k)=?F(k)?F(k1), where F is measured cuttingforce and Frefis force set point.Fig. 1. Comparison of actual and model feed-rate.U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxx3Fig. 2. Structure of a fuzzy controller.3. CNC machining process modelA CNC machining process model simulator is used toevaluate the controller design before conducting experimen-tal tests. The process model consists of a neural force modeland feed drive model. The neural model estimates cuttingforces based on cutting conditions and cut geometry as de-scribed by Zuperl 1. The feed drive model simulates themachine response to changes in commanded feed-rate. Thefeed drive model was determined experimentally by examin-ing step changes in the commanded velocity. The best modelfit was found to be a second-order system with a natural fre-quency of 3Hz and a settling time of 0.4s. Comparison ofexperimentalandsimulationresultsofavelocitystepchangefrom 7 to 22mm/s is shown on Fig. 3.The feed drive and neural force model are combined toform the CNC machining process model. Model input is thecommanded feed-rate and the output is the X, Y resultant cut-ting force. The cut geometry is defined in the neural forcemodel. The simulator is verified by comparison of experi-mental and model simulation results. A variety of cuts withfeed-rate changes were made for validation.The experimental and simulation resultant force for a stepchange in feed-rate from 0.05 to 2mm/tooth is presented inFig. 4. The experimental results correlate well with modelresults in terms of average and peak force. The experimentalFig. 3. Comparison of actual and model federate.4U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxxFig. 4. Structure of a fuzzy controller.results correlate well with model results in terms of averageand peak force.The obvious discrepancy may be due to inaccuracies inthe neural model, and unmodeled system dynamics.3.1. Cutting force modelingTo realise the on-line modelling of cutting forces, a stan-dardBPneuralnetwork(NN)isproposedbasedonthepopu-lar back propagation leering rule. During preliminary exper-iments it proved to be sufficiently capable of extracting theforce model directly from experimental machining data. It isused to simulate the cutting process.TheNNformodellingneedsfourinputneuronsformillingfederate (f), cutting speed (vc) axial depth of cut (AD) and ra-dial depth of cut (RD). The output from the NN are cuttingforce components, therefore two output neurons are neces-sary.ThedetailedtopologyoftheusedNNwithoptimaltrain-ing parameters and mathematical principle of the neuron isalsoshowninFig.5.BestNNconfigurationcontains5,3and7 hidden neurons in hidden layers.3.2. Topology of neural network and its adaptation tomodeling problemThe effect of topology is also studied by considering dif-ferent cases. The topologies are varied by varying the num-ber of neurons in hidden layers. To evaluate the individualeffects of training parameters on the performance of neuralnetwork 40 different networks were trained, tested and anal-ysed. The network performances were evaluated using fourdifferent criteria 5: ETstMax, ETst, ETrn, and ETrnMaxand the number of training cycles. The number of neurons inthe input and output layers are determined by the number ofinput and output parameters. From the results the followingconclusions can be drawn.U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxx5Fig. 5. Structure of a fuzzy controller. Learning rates below 0.3 give acceptable prediction errorswhile learning rates must be between 0.01 and 0.2 to min-imise the number of training cycles. To minimise the estimation errors, momentum rates be-tween0.001and0.005aregood.However,themomentumrate should not exceed 0.004 if the number of training cy-cles is also to be minimised. The optimum number of hidden layer nodes is 3 or 6. Net-works with between 2 and 12 hidden layer nodes, otherthan3or6,alsoperformedfairlywellbutresultedinhighertraining cycles. Networks that employ the sine function require the lowestnumber of training cycles followed by the ArcTangent,while those that employ the hyperbolic tangent require thehighest number of training cycles.4. Data acquisition system and experimentalequipmentThe data acquisition equipment used in this acquisitionsystem consists of dynamometer, fixture module, hardwareand software module as shown in Fig. 1. The cutting forceswere measured with a piezoelectric dynamometer (Kistler9255) mounted between the workpiece and the machiningtable. When the tool is cutting the workpiece, the force willbe applied to the dynamometer through the tool. The piezo-electric quartz in the dynamometer will be strained and anelectric charge will be generated. The electric charge is thentransmitted to the multi-channel charge amplifier throughthe connecting cable. The charge is then amplified using themulti-channel charge amplifier. In the multi-channel chargeamplifier, different parameters can be adjusted so that the re-quired resolution can be achieved. Essentially, at the outputof the amplifier, the voltage will correspond to the force de-pendingontheparameterssetinthechargeamplifier.Thein-terfacehardwaremoduleconsistsofaconnectingplanblock,analogue signal conditioning modules and a 16 channel A/Dinterface board (PC-MIO-16E-4). In the A/D board, the ana-logue signal will be transformed into a digital signal so thatthe LabVIEW software is able to read and receive the data.The voltages will then be converted into forces in X, Y and Zdirections using the LabVIEW program. With this program,the three axis force components can be obtained simulta-neously, and can be displayed on the screen for analysingforce changes. The ball-end-milling cutter with interchange-able cutting inserts of type R216-16B20-040 with two cut-ting edges, of 16mm diameter and 10helix angle was se-lected for machining. The cutting inserts R216-1603 M-Mwith 12rake angle were selected. The cutting insert ma-terial is P30-50 coated with TiC/TiN, designated GC 4040in P10-P20 coated with TiC/TiN, designated GC 1025. Thecoolant RENUS FFM was used for cooling. The fuzzy con-trolisoperatedbytheintelligentcontrollermodule(Labview)and the modified feed-rates are send to the CNC. Communi-cation between the force control software and the NC ma-chine controller is enabled through memory sharing. Thefeed-rate override percentage variable DNCFRO is avail-able to the force control software for assignment at a rateof 1kHz.5. Simulations and fuzzy control milling experimentTo examine the stability and robustness of the adaptivefuzzy control strategy, the system is first examined by sim-ulation using Simulink and Labview fuzzy Toolset. Thenthe system is verified by various experiments on a CNCmilling machine (type HELLER BEA1) for Ck 45 and Ck45 (XM) steel workpiece with variation of cutting depth(Fig. 6).The ball-end-milling cutter (R216-16B20-040) with twocutting edges, of 16mm diameter and 10helix angle wasselected for experiments. Cutting conditions are: milling6U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxxFig. 6. Workpiece profile.widthRD=3mm,millingdepthAD=2mmandcuttingspeedvc= 80m/min.The parameters for fuzzy control are the same as for theexperiments for the traditional system performance.To use the fuzzy control structure on Fig. 1 and to opti-mise the feed-rate, the desired cutting force is Fref=280N,pre-programmed feed is 0.08mm/teeth and its allowable ad-justing rate is 0150%.Fig. 7 is the response of the cutting force and the feed-ratewhen the cutting depth is changed. It shows the experimentalresult where the feed-rate is adjusted on-line to maintain thecutting force at the maximum desired value.Simulated control response to a step change in axial depthis presented in Fig. 8. The simulation represents a 16mm,two-flute cutter, at 2000rpm, encountering a step change inaxial depth from 3 to 4.2mm. The step change occurs at2s and the controller returns the peak forces to the refer-ence peak force within 0.5s. In this research the stabilityof fuzzy controller is evaluated by simulation. Test simula-tions with small and large step changes in process gain arerun to ensure system stability over a range of cutting con-ditions. Small process gain changes are simulated with anaxial depth change from 3 to 4.2mm at a spindle speed of2000rpm. Large gain changes are simulated with an axialdepth change from 3 to 6mm at 2000rpm. The system re-mains stable in all simulation tests, with little degradation inperformance.Fig. 7. Experimental results with variable cutting depth. Response of MRR, resulting cutting force, feed-rate. (a) Conventional milling and (b) milling withadaptive fuzzy control.U. Zuperl et al. / Journal of Materials Processing Technology xxx (2005) xxxxxx7Fig. 8. Simulated fuzzy control response to a step change in axial depth.6. Results and discussionIn the first experiment using constant feed rates (conven-tionalcutting,Fig.7a)theMRRreachesitspropervalueonlyin the last step.However, in second test (Fig. 7b), machining the samepiece but using fuzzy control, the average MRR achieved ismuch more close to the proper MRR.Comparing Fig. 7a and b, the cutting force for the neuralcontrol milling system is maintained at about 240N, and thefeed-rate of the adaptive milling system is close to that of thetraditionalCNCmillingsystemfrompointCtopointD.FrompointAtopointCthefeed-rateoftheadaptivemillingsystemis higher than for the classical CNC system, so the millingefficiency of the adaptive milling system is improved.The experimental results show that the MRR can be im-proved by up to 27%. As compared to most of the exist-ing end-milling control systems, the proposed fuzzy controlsystem has the following advantages 3: 1. multi-parameteradjust
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