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1 链 式自动换刀臂的多阶段优化设计 KIM Jae-Hyun, LEE Choon-Man 韩国昌原国立大学机电学院，昌原 641-773， 中南大学出版社和柏林海德堡施普林格出版社 2012 摘要：为了提高加工效率，刀具更换时间需要有所减少。因此，用于连接到一个自动换刀加工中心的换刀时间将减少。同时自动换刀系统是加工中心的一个重要部分，作为驱动源。使用商业代码 ANSYS Workbench V12试 图解释自动换刀装置的静态属性，对和自动换刀臂的优化设计进行了多级优化设计。依靠自动换刀的形状的优化建议，并对结 果进行了验证，获得可接受的改进。它是能够获得一个与初始模型的比较，最大变形，最大应力和质量分别减少 10.46%， 12.89%和9.26%的优化模型。同时，实验设计方法也与常规的实验设计方法进行了多级优化比较。 关键词：自动换刀装置；优化设计；结构分析；交换臂 1 引言 最近，在机械制造行业中，模具和机械零件已经变成了小批量生产系统。同时， 需要 提高生产率和切割速度。然而， 从实践观点看 ，高质量和低成本是有针对性的实际的立场。因此，对于这样的目标追求实现机床高速加工，实现自动化，缩短交货时间。结果，它是可 能的检查状态的工具和工件在机床使用适当的传感器。此外，加工中心的自动换刀装置 （ ATC） 和一个托盘自动交换装置 （ APC）旨在操作无人值守厂 24 小时，自动换刀装置存储用于加工中心的杂志和变化的工具自动为所需的工具。改变这样的管制 的 工具正是安装在主轴 1。 同时，它代表了一种先进的优势，由于对机床的干扰少，加工中心操作者可以从事其他工作。也就是说，运营商可以控制其他机床或准备下一个工件，从而减少生产时间。 在这项研究中使用的链式 ATC 代表着许多工具都存储在一个特征模块。在工具的改变，两个臂移动到旋转 180在 直接转换的方式配置工具更改到下一个工具 2。因此，有必要同时确保结构特点和设计轻量化。 在实际的工业领域，优化设计是非常重要的。因此，提出了各种机械零件优化各种优化方法 3。 宋等人 4 提出的轴承短优化设计提高学报的人工生命算法。阿莱尔等人 5 结合结构优化的拓扑和形状的进行推导。 BAGCI和艾库特 6 提出田口优化验证数控铣削的最佳表面粗糙度。兰博迪 7 提出了一种基于模拟退火算法的桁架结nts 2 构优化设计。塞库尔斯基 8 表明，遗传算法是一种有效的多目标优化工具的拓扑结构同时设计优化的 设计工具。 SEO等人 9 提出的形状优化和基于 ISO几何分析的设计的延伸。 在优化空管部门，其结构特点的因素和轻量化是彼此相反 10 。它显示一个权衡，如果追求提高结构轻巧，结构特点，将是一个弱点，如果改进的结构特点，对轻量化的实现是很困难的。因此 ， 为了满足这些相反的因素和优化，以不同方式等臂形状优化是通过实验设计了 11 。 在这项研究中，获得更为优化的模型比以前的研究 11 ，一个多阶段进行的优化设计。优化设计是利用商业分析程序， CATIA V5和 ANSYS Workbench，和分析 的有效性是通过比较初始和传统优化模型在这项研究中实现的优化模型研究。 2 AT 的结构 ATC由三个元素组成，如杂志，更换部分，和臂部。部分杂志是一种装置，储存大量的工具和修改工具使用伺服电机。该变换器部分配备伺服电机，旋转臂。臂部的啮合工具在加工中心的旋转 180主轴和杂志显示臂形变化的工具。 图 1说 明了 ATC模拟利用 CATIA V5 R17的整个结构。 图 1 就 ATC链式结构图 手臂的初始模型进行结构分析。在进行有限元分析的参考，使用商业分析程序进行了初步的有限元分析模型，利用 ANSYS Workbench的 V12。分析是通过nts 3 最小化在臂的附加部分进行。在分析方法上，一个十六进制主导的方法应用于一个有限元分析 共 51794个节点和 13496元素。图 2显示了手臂的初始有限元模型。 图 2 初始有限元模型的手臂 在分析的边界条件，在 ATC 臂中心孔的支持，和重力加速度的应用到整个身体。在负载条件下，负载 147 N适用于夹两端考虑工具的最大重量。 结构分析的结果示于图 3。在夹具的初始模型两端的最大变形量为5.7487M。同时，最大应力在截面边缘产生，这推动了空管部门后 4.1762 兆帕的的手指 。 图 3 结构分 析：（一）臂的变形分布；（ b）的应力分布 3 ARM的多阶段优化 nts 4 静态顺应性 FX（ = D / F）可通过静刚度的得出了。特别是，在一些机械结构的机床和工业机器人要求高精度和加工效率，就成为最重要的静态特性以及结构的重量，这些因素是综合评价，同时。正如上面提到的，静态优化的问题被确定为这两个目标函数的静态特性和重量最小化的问题 12。 因此，在这项研究中，优化是为满足每个目标函数的一个多级的方式进行。第一阶段为提高静态特征的阶段。通过定义设计因素，减少变形，可诱导的最佳模型。第二阶段是确定为实现其轻量化的一 个阶段。基于第一阶段提出的优化模型，形状优化是针对它的重量减少了 10%行。 3.1第一阶段的臂优化设计 在第一阶段的优化设计，优化设计的目的是最大限度地减少臂的变形。 图 4说明了手臂的设计变量。 图 4 ATC臂因素 臂 的尺寸和形状优化设计的一般形式可以通过定义目标函数和约束条件下的函数 13-15。为实现对 ATC臂的优化设计，形式化定义如下： 其中 X代表的设计变量，并 和 显示应力和变形，分别。同时， a和 b为 显示的应力和变形的允许值，分别为。一方面， A， B，和 C 的设计变量。设计变量的配 置 30毫米，不到目前的碰撞干涉的影响在结构上的设计 。 在最佳设计，最佳的解决方案可以最大限度地减少臂的变形使用 CATIA V5的产品工程优化。表 1给出了优化结果。 nts 5 图 5说明了该优化设计的臂的结构分析结果。在分析中的边界条件被配置为作为初始模型同样存在。 表 1 优化结果减小变形 图 5 减少变形的结构分析优化的 ARM的：（ a）变形分布；（ b）的应力分布 3.2第二级臂优化设计 实现手臂的轻量化是降低工件成本的重要因素。同时，可以通过引入一个轻量级的结构 16 改善经济。因此，实现手臂的轻量 化优化设计是在第二阶段进行。在降低质量的目标是 10%的基础上的最佳设计的第一阶段提出的模型的手臂。为nts 6 减少手臂的质量，形状优化采用 ANSYS Workbench进行形状优化功能。为优化设计的形式化可以如下： 在 Z是一个设计变量 ,和显示压力和变形 ,分别和 a和 a津贴的应力和变形值。同时 ,设计变量 r是 配置 找到所有部分的质量减少可能除了部分 ,它有一些局限性在设计。 图 6说明了最佳的解决方案，最大限度地减少臂的变形结果。如图 6所示，提出了 “部分删除 “代表一个质量可部分去除它。根据研究结果，可部分 除去到最高水平。图 7显示了基于形状优化结果的臂轻量化提出的最佳形状。 图 6 基于 ANSYS的形状优化结果 图 7臂设计 结构分析使用所提出的优化设计进行。同时，在分析中的边界条件被施加作为现有的初始模型相同的。 nts 7 图 8显示了结构分析的结果，这是通过施加的最佳形状进行。 图 8结构分析的轻量化优化臂：（ a）变形分布；（ b）的应力分布 该模型的最大变形采用优化设计，从 5.748 减少 7M 在初始模型提出了55.147 m高达 10.46%，产生在夹子端作为初始模型相同的。同时，最大应力降低 4.176 2兆帕 在初始模型 3.637 9兆帕高达 12.89%。此外，质量从 7.871 2公斤的初始模型，提出了减少到 7.142 5公斤，多达 9.26%。 表 2给出了比较的结果的优化设计 11 采用多级优化设计实现了在这项研究中进行的实验设计。 表 2 结果比较 nts 8 在本研究中得到的结果与实验设计的结果比较，最大变形，最大应力，和质量的 1.38%， 12.61%，和 5.63%的降低，分别为。因此，可以看出，使用 CATIA、ANSYS 进行本研究多级设计使得有可能吸引更多的改进优化设计比现有的研究。 4 结论 1） 采用多级优化设 计，可以获得一个优化模型，与初始模型的比较最大变形，最大应力和质量分别减少 10.46%， 12.89%， 9.26%，。 2）在多级优化设计和进行实验设计与优化设计的比较，最大变形，最大应力和质量分别减少了 1.38%， 12.61%和 5.63%。 3）通过比较常规方法的结果的实验设计方法，提出了采用多级优化设计，验证了优化设计是否正确进行。 4） 基于 CATIA、 ANSYS 商业软件使用多级优化设计验证，预计可应用于机床的结构优化设计。 nts 9 参考文献 1 LEE S W, LEE H K. Reliability evaluation of ATC for high-speed line center J. Journal of Korean Society for Precision Engineering, 2006, 23(6): 111 118. (in Korean) 2 BARK T Y. The design of automatic tool changer M. Korea Advanced Institute of Science Univ Press, 1977: 1 11. (in Korean) 3 ROY R, HINDUJA S, TETI R. Recent advances in engineering design optimization: Challenges and future trends J. CIRP Annals Manufacturing Technology, 2008, 57: 697715. 4 SONG J H, YANG B S, CHOI B G, KIM H J. Optimum design of short journal bearings by enhanced artificial life optimization algorithm J. Tribology International, 2005, 38(4): 403 412. (in Korean) 5 ALLAIRE G, JOUVE F, DE GOURNAY F, TOADER A. Combining topological and shape derivatives in structural optimization C/ European Conference on Computational Mechanics. 2006: 644. 6 BAGCI E, AYKUT S. A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy J. The International Journal of Advanced Manufacturing Technology, 2006, 29(9): 940 947. 7 LAMBERTI L. An efficient simulated annealing algorithm for design optimization of truss structures J. International Journal of Computers and Structures, 2008, 86: 1936 1953. 8 SEKULSKI Z. Multi-objective topology and size optimization of high-speed vehicle-passenger catamaran structure by genetic algorithm J. Marine Structures, 2009, 22: 691 711. 9 SEO Y D, KIM H J, YOUN S K. Shape optimization and its extension to topological design based on isogeometric analysis J. International Journal of Solids and Structures, 2010, 47(11): 1618 1640. (in Korean) 10 JIA S, XIN W, XIACONG J, TAEKESHI I. Multi objective optimization based fast motion detector J. Lecture Notes in Computer Science, 2011, 6523: 492502. 11 KIM J H, LEE M J, LEE C M. Geometric optimal design of an ATC arm using design of experiments C/ Proceedings of the IASTED International Conference. 2010: 357 362. (in Korean) 12 LEE Y W, SUNG H G. Multi-phase optimization of quill type machine structures (1) J. Journal of Korean Society Precision Engineering, 2001, 18(11): 155160. (in Korean) 13 LEE M J, LEE C M. A study on structural analysis and optimum shape design of tilting index table J. Journal of Korean Society Precision Engineering, 2009, 27(2): 86 93. (in Korean) 14 ARORA J S. Introduction to optimum design, M. McGraw-Hill, 2003: 89. 15 KIM H S, LEE Y S. Optimization design technique for reduction of sloshing by evolutionary methods J. Journal of Mechanical Science and Technology, nts 10 2008, 22(1): 2533. (in Korean) 16 KIM J S. A study on the weight-saving design of the boom in high ladder vehicle J. Transactions of the Korean Society of Machine Tool Engineers, 2007, 16(2): 813. (in Korean) nts J. Cent. South Univ. (2012) 19: 174178 DOI: 10.1007/s1177101209883 Multi-stage optimum design of magazine type automatic tool changer arm KIM Jae-Hyun, LEE Choon-Man School of Mechatronics, Changwon National University, Changwon 641-773, Korea Central South University Press and Springer-Verlag Berlin Heidelberg 2012 Abstract: To enhance machining efficiency, tool change time has to be reduced. Thus, for an automatic tool changer attached to a machining center, the tool change time is to be reduced. Also the automatic tool changer is a main part of the machining center as a driving source. The static attributes of the automatic tool changer using the commercial code, ANSYS Workbench V12, were tried to interpret. And the optimum design of automatic tool changer arm was proposed by performing the multi-stage optimum design. The shape optimization of the automatic tool changer was proposed and the result was verified to obtain acceptable improvements. It is possible to obtain an optimized model in which the maximum deformation, maximum stress, and mass are reduced by 10.46%, 12.89% and 9.26%, respectively, compared with those of the initial model. Also, the results between conventional method by the design of experiments and proposed method by the multi-stage optimum design method were compared. Key words: automatic tool changer; optimum design; structural analysis; exchange arm 1 Introduction Recently, in machine manufacturing industries, molds and machine parts have been changed to small quantity batch production system. Also, improvements in productivity and cutting rate are required. Whereas, it is true that high quality and low cost are to be targeted from a practical standpoint. Therefore, the machine tools for such aims pursue to achieve high-speed processing, implement automation, and reduced lead time. As a result, it is possible to check the states of tools and workpieces using proper sensors in the machine tools. In addition, a machining center based on an automatic tool changer (ATC) and an automatic pallet changer (APC) aims to operate an unattended operation factory for 24 h. The automatic tool changer stores the tools used in a machining center to its magazine and changes the tools automatically as required. The tool changed by such ATC is precisely equipped to a spindle 1. Also, it represents an advantage that an operator of the machining center is able to engage in other works due to the less interference for the machine tools. That is to say, the operator can control other machine tools or prepare the next workpieces, which leads to reduced production time. The magazine type ATC used in this study represents a feature that many tools are stored in the magazine. In the change of tools, two arms move to change the equipped tool to the next tool by rotating them by 180 in a directly changed manner 2. Thus, it is necessary to ensure the technologies for both the structural characteristics of arms and the design of lightweight simultaneously. In actual industrial fields, design optimization is very important. Therefore, various optimization methods are presented for the optimization of various mechanical parts 3. SONG et al 4 presented optimization design of the short journal bearing by using enhanced artificial life optimization algorithm. ALLAIRE et al 5 combined the topological and shape derivations on the structural optimization. BAGCI and AYKUT 6 presented Taguchi optimization to verify the optimum surface roughness of the CNC milling. LAMBERTI 7 presented a design optimization algorithm based on simulated annealing for truss structures. SEKULSKI 8 presented that the genetic algorithm can be an efficient multi-objective optimization tool for simultaneous design of the topology and sizing of ship structures. SEO et al 9 presented shape optimization and its extension to topological design based on isogeometric analysis. In optimizing the ATC arm, the factors of the structural characteristics and the lightweight are contrary to each other 10. It shows a trade-off that if it pursues to improve the lightweight in structures, the structural characteristics will represent a weakness, and if the Foundation item: Work(RTI04-01-03) supported by Grant from Regional Technology Innovation Program of the Ministry of Knowledge Economy (MKE), Korea Received date: 20110426; Accepted date: 20111010 Corresponding author: LEE Choon-Man, Professor, PhD; Tel: +82552133622; E-mail: cmleechangwon.ac.kr ntsJ. Cent. South Univ. (2012) 19: 174178 175structural characteristics are improved, the achievement of the lightweight is difficult. Therefore, for satisfying these contrary factors and optimizing them, the optimization of such arm shapes in different way is presented by using the design of experiments 11. In this study, for achieving a more improved optimization model than the previous study 11, a multi-stage optimum design was performed. The optimum design was presented using the commercial analysis programs, CATIA V5 and ANSYS Workbench, and the analytic validity was investigated through comparing the initial and conventional optimized models with the optimized model implemented in this study. 2 Structure of ATC ATC consists of three elements, such as magazine part, changer part, and arm part. The magazine part is a device that stores many tools and changes tools using servo motors. The changer part is equipped with servo motors, which rotate arms. The arm part shows an arm shape and changes tools by gearing the tools in the spindle and magazine in a machining center by rotating them by 180. Figure 1 illustrates the entire structure of the ATC modelled by using the CATIA V5 R17. Fig. 1 Structure of magazine type ATC The structural analysis of the initial model of the arm was performed. Regarding the reference of the performed finite element analysis, the finite element analysis of the initial model was carried out using the commercial analysis program, Ansys Workbench V12. The analysis was performed by minimizing the additional part employed in the arm. In the analysis method, a hex dominant method was applied in which a finite element analysis had totally 51 794 nodes and 13 496 elements. Figure 2 shows the initial finite element model of the arm. Fig. 2 Initial finite element model of arm For the boundary conditions in the analysis, the hole at the center of the ATC arm was supported, and the gravitational acceleration was applied to the entire body. In the load conditions, a load of 147 N was applied to the clamps at both ends for considering the maximum weight of the tools. The results of the structural analysis are presented in Fig. 3. The maximum deformation of the initial model at the clamps is 5.748 7 m and occurs at both ends. Also, the maximum stress is generated at the edge of the section, which pushes the rear finger of the ATC arm, and is presented by 4.176 2 MPa. Fig. 3 Structural analysis of arm: (a) Deformation distribution; (b) Stress distribution 3 Multi-stage optimization of arm The static compliance, fx(=D/F), can be presented by an inverse number of the static stiffness. In particular, in some machine structures like machine tools and industrial robots that require high accuracy and machining efficiency, it becomes the most important static characteristic as well as the structure weight where these factors are to be comprehensively and simultaneously evaluated. As mentioned above, the optimization of the static issue is determined as the static characteristic of these two objective functions and the minimization issue of the weight 12. ntsJ. Cent. South Univ. (2012) 19: 174178 176 Thus, in this study, the optimization is performed as a multi-stage manner for satisfying each objective function. The first stage is configured as a stage that improves the static characteristics. By defining design factors that minimize the deformation, an optimum model can be induced. The second stage is determined as a stage for implementing its lightweight. Based on the optimum model presented in the first stage, the shape optimization is performed by aiming a reduction in its weight by 10%. 3.1 First stage of optimum design of arm In the first stage of the optimum design, the optimum design aims to minimize the deformation of the arm. Figure 4 illustrates the design variables of the arm. Fig. 4 Factors of ATC arm The general formalization for the dimension and the optimum shape design can be presented by defining objective functions and limitation condition functions 1315. For implementing the optimum design for the ATC arm, the formalization is determined as follows: Find X Minimize deformation (X) Subject to a aL A, B, C U(=A, B, C) X=A, B, C where X represents one of the design variables, and and show the stress and deformation, respectively. Also, aand ashow the allowance values for the stress and deformation, respectively. The terms of A, B, and C are the design variables. The design variables are configured by 30 mm in order not to present the influences of the collision and interference in structures on the design. In the optimum design, the optimum solution can minimize the deformation of the arm using the CATIA V5 Product engineering optimizer. Table 1 gives the results of the optimization. Figure 5 illustrates the results of the structural analysis of the optimal designed arm. The boundary conditions in the analysis are configured as the same as the existing initial model. Table 1 Results of optimization for reducing deformation Factor Initial model Optimal designed modelA/mm 25 3.396 B/mm 70 73.68 6 C/mm 27 32.68 6 Maximum deformation/m 5.748 7 4.668 3 Maximum stress/MPa 4.176 2 3.607 2 Fig. 5 Structural analysis of optimized arm for reducing deformation: (a) Deformation distribution; (b) Stress distribution 3.2 Second stage of optimum design of arm Achieving the lightweight of the arm is an important factor for reducing the cost of workpieces. Also, it is possible to improve the economy by introducing a lightweight structure 16. Therefore, the optimum design for implementing the lightweight of the arm is performed in the second stage. The target in reducing the mass is 10% of the arm based on the model proposed in the first stage of the optimum design. For reducing the mass of the arm, the shape optimization is carried out using the ANSYS Workbench shape optimization function. The formalization for the optimum design can be presented as follows: Find Z Minimize mass (Z) Subject to a aLrUZ=r where Z is one of the design variables, and show the stress and deformation, respectively, and a and aare the allowance values for the stress and deformation, respectively. Also, the design variable, r, is configured ntsJ. Cent. South Univ. (2012) 19: 174178 177to find all sections in which the mass reduction is possible except for the sections, which have some limitations in the design. Figure 6 illustrates the results of the optimum solution that minimizes the deformation of the arm. As shown in Fig. 6, the section presented by Remove represents a mass reducible section by removing it. Based on the results, the reducible sections are removed to a maximum level. Figure 7 shows the proposed optimum shape for lightweight of the arm based on the results of the shape optimization. Fig. 6 Result of shape optimization using ANSYS Fig. 7 Redesign of arm The structural analysis is performed using the proposed optimum design. Also, the boundary conditions in the analysis are applied as the same as the existing initial model. Figure 8 shows the results of the structural analysis, which is carried out through applying the optimum shape. Fig. 8 Structural analysis of optimized arm for lightweight: (a) Deformation distribution; (b) Stress distribution The maximum deformation of the model, which applies the optimal design, is reduced from 5.748 7 m presented in the initial model to 5.147 5 m by as much as 10.46% and generated at the end of the clamp as the same as the initial model. Also, the maximum stress is reduced from 4.176 2 MPa presented in the initial model to 3.637 9 MPa by as much as 12.89%. In addition, the mass is reduced from 7.871 2 kg presented in the initial model to 7.142 5 kg by as much as 9.26%. Table 2 presents the results of the comparison of the optimum design 11 using the design of experiments performed with the multi-stage optimum design implemented in this study. Table 2 Comparison of results PropertyInitialmodel(A) Conventional method (B) Proposed optimization method (C) Ratio of Ato C/% Ratio of Bto C/% Maximum deformation/m 5.748 7 5.219 7 5.147 5 10.46 1.38Maximum stress/MPa4.176 2 4.163 3.637 9 12.89 12.61Mass/kg 7.871 2 7.568 3 7.142 5 9.26 5.63In the comparison of the results obtained in this study with the results of the design of experiments, the maximum deformation, maximum stress, and mass are reduced by 1.38%, 12.61%, and 5.63%, respectively. Thus, it can be seen that the multi-stage design using the CATIA and ANSYS performed in this study makes possible to draw more improved optimum design than the existing study. 4 Conclusions 1) By performing the multi-stage optimum design, it is possible to obtain an optimized model in which the maximum deformation, maximum stress, and mass are reduced by 10.46%, 12.89%, and 9.26%, respectively, compared with those of the initial model. 2) In the comparison of the optimum design between the multi-stage optimum design and the previously performed design of experiments, the maximum deformation, maximum stress, and mass are reduced by 1.38%, 12.61% and 5.63%, respectively. 3) By comparing the results between conventional method by the design of experiments and proposed method by the multi-stage optimum design, it is verified whether the optimum design is carried out properly. 4) Based on verification of using commercial programs of CATIA and ANSYS for multi-stage optimum design, it is expected that it can be applied to the optimum design of machine tool structures. ntsJ. Cent. South Univ. (2012) 19: 174178 178 References 1 LEE S W, LEE H K. Reliability evaluation of ATC for high-speed line center J. Journal of Korean Society for Precision Engineering, 2006, 23(6): 111118. (in Korean) 2 BARK T Y. The design of automatic tool changer M. Korea Advanced Institute of Science Univ Press, 1977: 111. (in Korean) 3 ROY R, HINDUJA S, TETI R. Recent advances in engineering design optimization: Challenges and future trends J. CIRP AnnalsManufacturing Technology