外文翻译---高速铣加工中轴转矩的模糊控制

外文翻译---高速铣加工中轴转矩的模糊控制 设计巴巴工作室www.88doc88.com 附录A:英文资料 Technical Briefs Fuzzy Control of Spindle Torque in High-Speed Milling Processes Rodolfo Haber-Guerra Instituto de Automática Industrial (CSIC), kin. 22800 N-III, La Proveda, 28500 Madrid, Spain Steven Y. Liang The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Drive, N.W., Atlanta, GA 30332-0405 Jose R. Alique Instituto de Automática Industrial (CSIC), km. 22800 N-III, La Proveda, 28500 Madrid, Spain Rodolfo Haber-Haber Universidad de Oriente, Ave. Americas s/n., Santiago de Cuba, 90400 Cuba This paper presents the design and implementation of a two-input/two-output fuzzy logic-based torque control system embedded in an open architecture computer numerical control ( CNC) for optimizing the material removal rate in high-speed milling processes.The control system adjusts the feed rate and spindle speed simultaneously as needed to regulate the cutting torque using the CNC's own resources. The control system consists of a two-input (i.e., torque error and change of error), two-output (i.e.,feed rate and spindle speed increment)fuzzy controller; which is embedded within kernel of a standard open control. Two approaches are tested, and their performance is assessed using several performance measurements.These approaches are a two-input/two-output fuzzy controller and a single-output (i.e.,feed rate modification only)fuzzy controller. The results demonstrate that the proposed control strategy provides better accuracy and machining cycle time than other strategies, thus increasing the metal removal rate.[DOI: I0.11 15/1.2194063] Keywords:fuzzy control, torque, high-speed milling 设计巴巴工作室www.88doc88.com Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received August 19,2005: final revision received February 14, 2006. Review conducted by C.J. Li. Introduction In order to improve machining efficiency in a high-speed milling process through a higher material removal rate, this study focuses on the design and implementation of a two-input/two-output (TITO) fuzzy control system for spindle torque. The major issue to be dealt with is the new development and application of fuzzy logic (FL) using the CNC's own resources. No additional hardware overhead is required, since the control algorithm is embedded within the kernel of a standard open control. Fuzzy logic(FL) was selected out of all the available techniques, because it has proven useful as a highly practical optimizing tool for control and industrial engineering. To the best of our knowledge, the main advantage of this approach is that it includes: (i) a fuzzy controller embedded in an open architecture CNC to deal with the production environment; (ii) a simple computational procedure for fulfilling the time requirements; and (iii) no restrictions in terms of sensor cost (the torque signal is provided by the open CNC),wiring, or synchronization with the CNC. This paper is organized as follows: In Sec.?we present a brief study of a mechanistic model for predicting cutting force and spindle torque; in Sec. III we describe the design of the fuzzy controller to optimize the milling process; in Sec. IV we describe how the fuzzy controller can be embedded in open architecture CNCs, and we discuss the key design and programming stages; in Sec. V we review the experimental results and explore some of the comparative studies. Finally, we present conclusions in Sec. VI. High-Speed Milling-Process Control Based on the Spindle Torque Signal The mechanistic model estimates cutting-force vectors and spindle torque on the basis of feed rate, spindle speed, and material constants. The mechanistic model of end milling implemented in this research is based on [1,2]. In order to make a simplified model, we used the following approximation [3,4]: If the feed-per-tooth value f is small with regard to the tool radius D/2, then t j for a tool fed in the positive X direction, the instantaneous chip thickness h is related to the j rotation angle of the cutting tool. As the cutting tool rotates, chip thickness varies as a function of the radial angle (Φ) and axial angle (κ) of the cutting tool h(Φ) = fsinΦsin κ ?jjij j (1) 设计巴巴工作室www.88doc88.com Therefore, on the basis of the above approximation, and for roughing and semifinished cutting,it is not necessary to extract the instantaneous chip thickness from the volume information.Certainly, a correct value of the feed per tooth f not only increases tool performance ij but also improves the efficiency of the machining process. For a cylindrical end mill, the following conditions are defined for finding the general solution. D r(z) = 2 κ= 90º (2) Ψ= kz 0 k= (2 tanθ)/D 0 where Ψ(z) is the lag angle that appears due to the helix angle θ of the cutting tool. This angle is constant in the case of a cylindrical end mill, and it varies for a ball-end mill. The tangential, radial,and axial force differentials (dF), (dF), (dF) act on an infinitesimal length dS of the tra cutting edge of the tool [4] dF= KdS + Kh( Φ, κ)db t tetcj dF = KdS + Kh( Φ, κ)db rrercj (3) dF = KdS + Kh( Φ, κ)db aaeacj It is also considered that dz db = sin κ (4) Furthermore, the characteristics of a point on the cutting surface are identified using the properties of kinematic rigidity and the displacements between the tool and the workpiece. The constants or cutting coefficients (KKKKKK) can be found experimentally using cutting tc,rc,ac,te,re,ae forces per tooth averaged for a specific type of tool and material [5]. The total cutting forces as a function of Φ along the axial depth of cut for all the cutting edges that are in contact with the workpiece can be calculated as N fN fz2 F(Φ) =?(F(Φ(z)) =?? [-dFsin Φsin κ - dFcos Φ- dFsin Φcos κ]dz xx jjr jj ji jj a jj j j=1j=1z1 N fN fz2 (5) F(Φ) =?(F(Φ(z)) =?? [-dFcos Φsin κ - dFsin Φ- dFcos Φcos κ]dz yy jjr jj ji jj a jj j 设计巴巴工作室www.88doc88.com j=1j=1z1 N fN fz2 F(Φ) = ?(F(Φ(z)) =?? [-dFcos κ - dFsin κ]dz zz jjr jja jj j=1j=1z1 where z and zare the integration limits and F(Φ),F(Φ),F(Φ)are the resulting forces for each 12 xyz axis. Cutting torque T is estimated on the basis of the tangential force differential (dF) and the qet tool diameter (D). The overall cutting torque is T = F?D/2 qet (6) Fuzzy Control of Spindle Torque in a High-Speed Milling Process The manipulated (action) variables we selected were the feed rate increment (?f as a percentage of the initial value programmed into the CNC) and the spindle speed increment (?s as a percentage of the initial value programmed into the CNC). The three basic tasks known as fuzzification, decision making, and defuzzification were used. The error and output vectors were T2 e=[KE?TKCE??T] ?qq 2Fig.1 Fuzzy partitions and membership functions for (a)?T,?T,(b)?f, qq and (c) s ? u = GC?[?f ?s ] 设计巴巴工作室www.88doc88.com (7) where KE, KCE, and GC are scaling factors for inputs (error and change in error) and outputs (change in the feed rate and change in the spindle speed), respectively. The torque values were acquired from an open architecture CNC. The reference torque value Twas estimated from the model described in Sec. 2. For each sampling period k, torque error and r the change in torque error were calculated as ?T(k) = T- T(k) qr q (8) 2 ?T(k) =?T(k)-?T(k -1) qqq (9) 2where ?Tis the torque error (in N m) and ?Tis the change in torque error (in N m). q q The fuzzy partition of universes of discourse and the creation of the rule base were based on prior knowledge and experimental results. Figure I shows the resulting fuzzy partition. Seven fuzzy sets were used for inputs and outputs: NB, negative big; NM, negative medium; NS, negative small; ZE, zero; PS, positive small; PM, positive medium; and PB, positive big. These membership functions are essential to achieving good control performance. When trapezoidal membership functions are used, the resulting system is the sum of a global nonlinear controller (which is the static part) and a local nonlinear PI controller (which changes dynamically with regard to the input space) [6]. We considered a set of rules consisting of' linguistic statements linking each antecedent with its respective consequent. The syntax followed the pattern below: 2 if ?T is PB and ?T is PB, then ?f PB and ?s is NB qq A total of 49 control rules for each output (?f and ?s) were developed, summarized in Table 1. These fuzzy rules provide important principles and relevant information about the process. Under normal cutting conditions, the constant feed rate and spindle speed values are set conservatively according to information in machining, cutting tool, and material handbooks. However, the feed rate values are manually adjusted in real time depending on the cutting parameters, in order to optimize the machining process. The spindle speed is also modified, but only slightly compared to the adjustment of the feed rate. Therefore, in order to maintain a constant cutting torque, the feed rate should be reduced and the spindle speed should be slightly increased when the torque increases (i.e., due to the increased depth of the cut). On the other hand, when the torque decreases due to air gaps in the part, the feed rate should be increased to maximize the rate of metal removal, and the spindle speed should be reduced slightly. 设计巴巴工作室www.88doc88.com Table 1 Rule bases for manipulating (a) feed rate and (b) spindle speed The sup-product compositional operator was selected for the compositional rule of inference. Using the algebraic product operation, developing the fuzzy implication, and applying the maximum union operation, we obtained 49 222,,,,RqqTqq(,,)max{prod{(),(),()}},,,,,,,TTfTTf,,,qiTqifi (10) i=1 49 222,,,,RqqTqq(,,)max{prod{(),(),()}},,,,,,,TTsTTs,,,qiTqifi (11) i=1 The center-of-average (COA) strategy was selected as the defuzzification strategy because of its suitable performance at steady state and its use as a standm-d defuzzification method in experimental and industrial fuzzy controllers. The crisp controller outputs are obtained by dcfuzzilication ,Rii(),,,ss,i,,s Ri(),s,,i (12) 设计巴巴工作室www.88doc88.com ,Rii(),,,ff,i,,f Ri(),f,,i (13) 2where ?f (?s) is the crisp value of ?f (?s) for a given crisp input (?T,?T ). iiqq The output-scaling factor (GC) multiplied by the crisp control action (generated at each sampling instant) provides thc final actions that will be applied to the CNC f (k) =f (k - 1) + GC?f (k) ? (14) s(k) = s (k- 1) + GC??s (k) Feed rate and spindle speed values were generated on line by the embedded controller and fed in with the set point for the torque Tand measured value T from the internal torque signal r q provided by the open architecture CNC, as detailed in Sec. 4. Open CNC and New Add-On Functions This section briefly explains how the fuzzy controller is embedded in the open architecture CNC (see Fig. 2). Further details about software development and how to embed control and monitoring systems in an open CNC are provided in [7]. The application was developed on the basis of a Sinumerik 840D CNC [8]. First, the fuzzy controller was programmed in C/C+ +, and then it was compiled, and as a result a dynamic link library (DLL) was generated. The control kernel (NCK) was modified to enable tile real-time modifcation of the spindle speed and feed rate. A PC, the WINDOWS XP operating system, and Visual C+ + were used to program the MMC. lntermtxlule communications between the MMC and the NCK were established through dynamic data exchange (DDE). DDE caused a delay that was considered in our work but was not relevant tor this case study. Finally, the user interface was programmed in Visual C+ +,for the sake of simplicity. The general outline of the control system is depicted in Fig. 3. An internal data-acquisition system was developed and used to measure the internal torque signal. The sampling frequency was 500 Hz, defined by the servosystems' control cycle. The software consists of a data-acquisition module in the NCK that records the selected data into an internal buffer and a background task running on the MMC that receives the completed 设计巴巴工作室www.88doc88.com measurement and stores it in the hard drive of the user-interlace PC. Experimental Validation Milling tests were carried out on the HSI000 Kondia highspeed milling machine, which was equipped with a Sinumerik 840D open CNC. A two-fluted end mill 12 mm in diameter was used as the tool for rough milling operations. All measurements were taken machining dry and supplying high-pressure air at the cutting zone. The workpiece material was 220-HB F-1140 steel (DIN CK45, ASTM 1045). The maximum depth of cut was 0.5 mm. The nominal spindle speed and the nominal feed tale were set at f = 1600 mm/min and s= 10,000 rpm, respectively, as 0o suggested by the handbook for these tool and material specifications. The profile and the workpiece clamped onto the machine tool table are depicted in Fig. 4. 设计巴巴工作室www.88doc88.com The reference torque value was derived from the model described in Sec. 2 and Eq. (6), using the following cutting coefficients (K,K, K, K, K, K)=(2178.23,879.65,798.44, r ca ct er ea et c 19.35,8.06,7.58). The torque value T=3.91 N m was set as the reference torque. Sampling r frequency was 500 Hz, the feed override range was 50-120%, and the spindle speed override range was 80-120 %. Initially the controller was tuned by modifying the scaling factors for inputs and outputs (i.e., KE=1, KCE=0.5, and GC=1)although we did have to apply a "cut and trial" procedure as well.The performance of self-tuning strategies was analyzed in [9,10].However, constraints of the open CNC (e.g., for real-time computation) and the characteristics of high-speed milling processes(i.e., stringent real-time requirements) severely restricted the implementation of a self-tuning algorithm. We verified the effectiveness of two fuzzy controllers. The first was a two-input/one-output (TISO) fuzzy controller where the spindle speed was set as a constant and the feed rate increment was adjusted accordingly. The second was a TITO controller with real-time modilication of both feed rate and spindle speed. We did not include linear controllers, because, according to previous study, fuzzy controllers yield better results than linear control loops for this type of case study [11]. The issue of stability was not addressed either. Some strategies for checking the stability of control systems of the type introduced herein were suggested in [12]. Control system behavior was evaluated by assessing accuracy and oscillations. Various performance indices, such as integral absolute errors (IAE), integral square errors (ISE), and integral of time per absolute errors (ITAE), were calculated in order to assess the inner loop control's performance. The cycle time t and the productivity improvement in machining mcch operations E were also calculated. The results are summarized in Table 2. Finally, surface ff roughness was computed according to the R, value, taking into account the type of operation a 设计巴巴工作室www.88doc88.com (roughing), using a Zeiss Z25 rugosimeter. The results are shown in Fig. 5. The behavior of the torque signal for all cases, including the CNC working alone, is depicted in Fig. 5(a). Control signals corresponding to feed rate and spindle speed are shown in Fig. 5(b). The TISO controller is represented as a gray line. The TITO controller is represented as a solid line.The T1TO fuzzy controller outperformed the others, as shown in Table 2. The decrease in the cycle time Ewas close to 10%, which clearly shows progress in ff 设计巴巴工作室www.88doc88.com productivity. Moreover, the IAE, ISE,and ITAE performance indices indicated more accurate behavior,which corroborated the advantage of using these two control variables. Finally, the roughness values were in the 0.34-0.79μm range (N4-N6), in accordance with ISO 1320:1992. Final Remarks This paper introduces a two-input/two-output fuzzy controller to regulate torque for the optimization of high-speed milling processes. The main advantages of the approach include a two-input/two-output fuzzy controller embedded in an open architecture CNC to deal with nonlinear and time-variant milling-process behavior. The results of the fuzzy control strategy show higher machining efficiency in actual industrial tests. The influence of the proposed control system on useful tool life, the appearance of chattering, and finished surface quality will all be analyzed in future research in order to assess the actual achievements (benefits) of the proposed method. 设计巴巴工作室www.88doc88.com Acknowledgment This work was supported in part by "Ramón y Cajal" Fellow Research Programme and DP12005-04298 COREMAV project of the Spanish Ministry of Education and Science. The authors wish to express their gratitude to Dr. Angel Alique and Dr. Salvador Ros for their assistance in providing useful comments and suggestions during the preparation of this paper. Finally, the authors would like to thank anonymous referees for their helpful suggestions and comments. References [I] Engin, S., and AItimas. Y., 2001, "Mechanics and Dynamics of General Milling Cutters Part 设计巴巴工作室www.88doc88.com 1: Helical End Mills," Int. J. Math. Tools Manuf. 41, pp.2195-2212. [2] Haber, R. E., Jiménez, J. E., Coronado, J. L., and Jiménez. A., 2004. "Cutting Force Model for a High-Speed Machining Processf,"Rev. Metal, Madrid,40(4), pp. 247-258. [3] Roth, D., Ismail, F., and Bedi. S.. 2003, "Mechanistic Model of the Milling Process Using an Adaptive Depth Buffer." Compul.-Aided Des. 35, pp. 1287-1303. [4] Martellotti, M., 1945. "An Analysis of the Milling Process, Part?--Down Milling," Trans. ASME, 67(1), pp. 233-251. [5] Budak, E., Ahintas, Y., and Armamgo, E. J. A., 1996, "Prediction of Milling Force Coefficients fi'om Orthogonal Cutting Data," ASME J. Eng. lnd.. 118,pp. 216-224. [6] Ying, H., 1999, "Analytical Structure of the Typical Fuzzy Comrnllers Employing Trapezoidal Input Fuzzy Sets and Nonlinear Control Rulesf Inf. Sci.(N,Y.), 116(2-4), pp. 177-203. [7] Haber, R. E., Alique, A., Alique, I. R,, Hem:iodcz, J., and Uribe-Elxabarria.R., 2003, "Embedded Fuzzy Control System for Machining Processes. Resuhs of a Case-Study,"Comput Ind., 50, pp. 353-366. [8] Sinumerik 840d. OEM,package NCK, software release 4, User's Manual, Siemens AG, 1999, [9] Haber, R. E., Haber, R. H., Alique, A.. and Ros, S., 2002, "Application of Knowledge-Based Systems for Supervision and Control of Machining Processes," in Handbook of Software Engineering and Kmm'ledge Engineering 2,S. K. Chang, ed., World Scientific. Singapere, pp. 673-710. [10] Haber, R. E., Haber-Haber, R.. and Alique, A., 2000, "HierarchicaJ Fuzzy Control of the Milling Process with a Self-Tuning Algorithm." in Proceedings of the IEEE International symposium on httelligent Control. Patras, Greecc,pp. 115-120. [11] Jiménez, J. E., Haber, R. E., and Alique, J. R.. 2004. "A MIMO Fuzzy Control System for High Speed Machining Processes. Results of a Case Study," in Proceedings of the IEEE Conference on Fuzzy Systems, Budapest, Hungary,pp. 901-905. [12] Haber, R. E., Schmiu-Braess, G., Haber-Haber. R., Aliquc. A., and Alique, J.R., 2003, "Using Circle Criteria for Verifying Asymplotic Slability in PI-Like Fuzzy Control Systems. An Application to the Milling Process," IEE Proc.:Control Theory Appl. 150(6), pp. 619-627. 设计巴巴工作室www.88doc88.com 附录B:英文资料翻译 高速铣加工中轴转矩的模糊控制 这篇论文介绍了把一个内含两输入/两输出基于逻辑转矩的模糊控制系统的开环数控系统用于使材料切除速率最优的高速铣加工的设计和执行。这个控制系统同时调整了流入速度和轴转速当做需要使用数控系统自身的资源调整切割扭矩。这个控制系统内含一个 标准 excel标准偏差excel标准偏差函数exl标准差函数国标检验抽样标准表免费下载红头文件格式标准下载 开放控制内核，由一个两输入(也就是，转矩误差和转变误差)，两输出(流入速度和轴速增量)模糊控制器组成。两个途径被试验过，并且使用单独的性能测量来评定他们的性能。这两种途径是分别用一个两输入/两输出模糊控制器和一个单输出(也就是，只有流入速度修正)模糊控制器。结果证明被提议的控制策略比其它策略提供更好的精确度和加工周期，因此增加了金属切除速度。 关键词:模糊控制，转矩，高速铣床 绪论 为了以更高的材料切除速度来提高高速铣加工的加工效率，这个研究集中在轴转矩的一个两输入/两输出的模糊控制系统的设计和执行。处理的主要问 题 快递公司问题件快递公司问题件货款处理关于圆的周长面积重点题型关于解方程组的题及答案关于南海问题 是新的发展和在使用数控特有资源的同时模糊逻辑的应用。不需要额外的硬件，因为这个控制运算法则是内含标准开放控制内核的。模糊逻辑是从所有可利用的技术中选出的，因为它证明了在对控制和工业 工程 路基工程安全技术交底工程项目施工成本控制工程量增项单年度零星工程技术标正投影法基本原理 方面做为一个非常实用的最优化工具是有用的。尽我们所知，这个途径的主要优势是它包括了:1)内含在开环数控的一个模糊控制器用来处理生产环境;2)实现时间要求的一个简单计算的程序;3)传感器成本范围(开环数控提供转矩信号)、配线、或者与数控系统同步没有限制。 这篇论文组织起来如下:在第二部分我们介绍关于一个机械论模型的简短研究来预言切削力和轴转矩;在第三部分我们描述使铣加工最优化的模糊控制器的设计;在第四部分我们描述模糊控制器如何才能被嵌入到开环数控里，并且讨论关键设计和规划发展的进程;在第五部分我们回顾实验结果并且探测一些比较的研究。最后，我们在第六部分介绍结论。 基于轴转矩信号的高速铣加工控制 这个机械论模型评估了切削力向量和以流入速度、轴转速、材料数量设计巴巴工作室www.88doc88.com 为基础的轴转矩。这个研究中最终铣执行的机械论的模型是基于文献[1，2]。为了做一个简单的模型，我们使用接下来的文献[3，4]:如果每齿流入值f小于刀具半径D/2，那么对于流入X正方向，即时碎片厚度h涉t jj及到刀具的旋转角。当切断刀具旋转，碎片厚度改变被看作一个关于切断刀具半径角(Φ) 和 轴向角(κ)的函数。 h(Φ) = fsinΦ?sin κ jjij j (1) 因此，在以近似值为基础上，对于粗磨和半完成切断，没有必要从大量信息获得即时的碎片厚度。当然，每齿流入值的校正值不仅增加了ft j 刀具性能，而且提高了机加工的效率。对于一个圆柱端铣刀，发现常规解决 方法 快递客服问题件处理详细方法山木方法pdf计算方法pdf华与华方法下载八字理论方法下载 需要以下条件。 D r(z) = 2 κ= 90º (2) Ψ= kz 0 k= (2 tanθ)/D 0 滞后角Ψ(z)的出现是由于切削刀具的螺旋角θ。在这个圆柱端铣刀的例子中，这个角是不变的，而它在球铣刀中是变化的。切线的、半径的、轴向的力的微分(dF), (dF), (dF)作用于刀具切削刃的一个无限小的长tra 度dS [4]。 dF= KdS + Kh( Φ, κ)db t t et cj dF = KdS + Kh( Φ, κ)db rr er cj(3) dF = KdS + Kh( Φ, κ)db aa ea cj 也可以写成 dz db = sin κ(4) 此外，切断表面的分数特征相当于刀具上运动学硬度和工件上的位移。这些常数或切断系数(KKKKKK)可以用试验方法建立，就是用同一种种型的刀t c,r c,a c,t e,r e,a e 具和材料的平均每齿的切削力[5]。 设计巴巴工作室www.88doc88.com 总切削力可以被看作关于和工件有联系的所有切削刃顺着轴向的切削深度Φ的一个函数，它可以这样被计算出 N fN fz2 F(Φ) =?(F(Φ(z)) =?? [-dFsin Φsin κ - dFcos Φ- dFsin Φcos κ]dz xx jjr jj ji jj a jj jj=1j=1z1 N fN fz2 F(Φ) =?(F(Φ(z)) =?? [-dFcos Φsin κ - dFsin Φ- dFcos Φcos κ]dz yy jjr jj ji jj a jj jj=1j=1z1 (5) N fN fz2 F(Φ(z)) =?? [-dFcos κ - dFsin κ]dz (Φ) = ?(Fzz jjr jja jjj=1j=1z1 z和z是积分区间，F(Φ),F(Φ),F(Φ)是每个轴的总切削力。 12 xyz 切削扭矩T以切线力微分和刀具直径D为基础估计。全部的切削扭qe 矩为 T = F?D/2 (6) qet 高速铣加工中的轴转矩模糊控制 我们挑选的巧妙处理的变量是流入速度增量(?f可以看作编程到数控机床中一个初值的百分比)和轴转速增量(?s可以看作编程到数控机床中一个初值的百分比)。模糊、做决定、不模糊是三个基本任务。误差和输出矢量为 T2 e=[KE??TKCE??T] qq u = GC?[?f ?s ] (7) KE、KCE和GC分别是输入(误差和变化中的误差)和输出(流入速度的变化和轴转速的变化)的比例系数。 转矩值从开环数控系统获得。涉及到的转矩值T由第二部分所描述的r 模型估算得到。对每一个取样周期k，转矩误差和转矩误差中的变化可这样计算 () = - () (8)?TkTTk qr q 2 ?T(k) =?T(k)-?T(k -1) (9) qqq 2?T是转矩误差(单位Nm)，?T转矩误差中的变化(单位Nm)。 qq 论文的模糊分割领域和规则基础的创造基于先前的知识和实验结果。图1表明了作为结果的模糊分割。7个模糊设置用来做输入和输出:NB，负大;NM，负中;NS，负小;ZE，零;PS，正小;PM，正中;PM，正大。 这些函数基本能达到好的控制性能。当这些梯形的函数被使用，作为结果的设计巴巴工作室www.88doc88.com 系统是一个球形非线形的控制器(静态部分)和一个局部非线性PI控制器(改变关于在输入空间的动力)的总和[6]。 我们考虑一组由语言陈述组成的规则用各自结果连接各自先前的任务。模式上的句子如下: 2 如果是PB，是PB，那么是PB，是NB。 ?T?T?f ?sqq 对各个输出一共可以产生49个控制规则，概括如表1。这些模糊规则提供了重要的原则和加工的有关信息。在正常切割条件下，恒流入速率和轴转速值可适当参照加工、刀具和材料手册上的信息。然而，在实际情况下，为了使加工过程最优化，流入速率值是依靠切割参数手动调整。轴转速也是修正过的，但是相对于流入速率的调整甚微。因此，为了保持切削扭矩不变，当转矩增加(也就是，由于切削深度增加)，流入速率应该减小，轴转速应该稍微增加。另一方面，当转矩由于零件的空气间隙增加的时候，流入速率应该增加到金属切削的最大速率，轴转速应该稍微减少。 高生产的合成操作员是为推论的合成物标准而选取的。我们选择用代数的产品操作，发展模糊推断和应用最大量的合成操作。 49 222,,,,RqqTqq(,,)max{prod{(),(),()}},,,,,,,TTfTTf,,,qiTqifi (10) i=1 49 222,,,,RqqTqq(,,)max{prod{(),(),()}},,,,,,,TTsTTs,,,qiTqifi (11) i=1 平均中心被选作为当模糊控制下的方法是由于它在稳定状态下响应的性能和它在实验上和工业上模糊控制中作为标准模糊控制下的途径的应用。有波纹的控制器输出是由模糊应用中获取的，模糊应用中的?f (?)是作为给定波纹输入的 () 的波纹值。 ?f?ssii 由波纹控制效果(在每个采样间隙中产生的)增殖的输出缩放比例的要素提供了最终的成果将被应用到书空车床上。 ,Rii(),,,ss,i,,s Ri(),s,,i 设计巴巴工作室www.88doc88.com (12) ,Rii(),,,ff,i,,f Ri(),f,,i (13) f (k) =f (k - 1) + GC??f (k) (14) s(k) = s (k- 1) + GC?s (k) ? 伺服比率和轴转速值是由内含的控制器在线产生、伴随转矩Tr 设定点供给和由开放系统数控机床提供的内部转矩信号中的测量值Tq ，详见第4部分。 开放式数控机床和新加的功能 这部分大致解释了如何将模糊控制器嵌入开环系统数控机床中去的(见图2)。有关软件开发的更详细的情况和怎样在开环数控机床上嵌入控制和运行系统由文献[7]提供。 这些应用是在840D型数控[8]模拟上发展的。首先，模糊控制是用C/C++编程的，然后编译，和生成动态链接库的结果。控制核心是修正为能覆盖限制轴速度和伺服比率的实际时间。APC,WINDOWS XP运行系统，和Visual C++被用于MMC程序的编写MMC和NCK之间的模块联系通过动态数据交换被确定。动态数据交换在我们的操作中引起了一个延迟，但是这个延迟与我们对这种情况的研究没有关系。最后，为了简单的缘故，用户界面使用C语言编程。控制系统的大体轮廓描述见图3。 国际数据获取系统已经被发展并且用于测量内部转矩信号。取样频率是500Hz，被伺服系统的控制循环定义。在NCK中由数据获取模块组成的软件在MMC中运行，NCK将被选择的数据送入内部缓冲器，MMC接收已完成的测量数据并将它储存在PC机交错用户的硬件驱动中。 实验确认 磨床测试在水平位置测试器磨床上执行，它由840D开放式数控系统组装而成。一个具有两个直径为12mm的凹槽的磨床被用于粗略的磨床加工。在切割区域，所有的措施被用于使机器干燥和提供高压空气。加工材料是220-HB F-1140 钢(DIN CK45, ASTM 1045)，切割最大深度是0.5mm。这个机床和材料的 说明书 房屋状态说明书下载罗氏说明书下载焊机说明书下载罗氏说明书下载GGD说明书下载 建议主轴速度是1600 mm/min，主轴进给率是设计巴巴工作室www.88doc88.com 10,000 rpm。剖面和机床工作台上的加工件的夹钳见图4。 涉及的转矩值来源于第二部分和例6描述的模型，这个转矩值用到的以下的切削参数(K,K, K, K, K, K)=(2178.23,879.65,798.44, t cr ca ct er ea e 19.35,8.06,7.58)。转矩值T=3.91 N m由参考转矩设定。采样频率是500r 赫兹，伺服顾及的范围是50-120%，轴转速顾及的范围是80-120%。 最初的控制器根据输入和输出的缩放比例因素来调谐(也就是，KE=1, KCE=0.5, GC=1)，尽管我们应用的一个切割试验程序是好的。文献[9][10]中分析了性能的自我调节策略。然而，约束开放式数控系统(举例来说，对一个真实时间的估算)和典型的高速铣床(也就是，迫切的真实时间的需求)，他们的自我调谐规则严格受限制。 我们检验两个模糊控制器的效力。第一个是一个两输入/一输出模糊控制器，轴速设为常数，流入速率增加。第二个是一个带有流入速率和轴速真实时间方式的TITO控制器。我们没有包含线性控制器，因为，依照先前学习的，模糊控制器比线性控制回路产生更好的结果。这个论点的稳定性不符合任何一个。一些校验控制系统稳定性的策略在文献[12]里介绍。 控制系统根据评估精确性和震动来估算。多种性能索引，比如积分绝对误差、积分平方误差和每一完全误差的时间积分，被计算出用来估定内部回路控制的性能。周期t和加工操作中的生产力改进E也可被估算。mcchff表2总结了结果。最后，表面粗糙度的估计根据Ra的值，营业收入说明了粗磨的类型，使用Z25糙度计。 结果如图5。所有案例的扭矩信号行为，包括数控系统单独工作，如图5(a)。控制信号相应的流入速率和轴速如图5(b)。灰色线代表TISO控制器。实线代表TITO控制器。TITO模糊控制器胜过其他的，如表2所示。 一个周期减少的E接近10,，清楚地显示了生产力地进步。此外，ff IAE、ISE和ITAE的性能指数显示出更多的精确动作，确证了使用这两个控制变量的优势。最后，粗糙度在0.34-0.79μm范围内，和ISO1320:1992一致。 最后说明 这篇论文介绍了使用一个两输入/两输出模糊控制器来调节最优化高速铣加工扭矩。 这个途径的主要优势包括一个两输入/两输出模糊控制器内含一个开环数控系统来处理非线性的和铣加工时间变量。这个模糊控制器的结果显设计巴巴工作室www.88doc88.com 示了工业测试中的更高生产效率。这个在有用工具生活中被提议的控制系统的影响，如流水般出现，并且加工面质量将在将来的研究中被分析，用来评定被提议方法的成就。 设计巴巴工作室www.88doc88.com