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黄河科技学院本科毕业设计（论文）任务书 工 学院 机械 系 机械设计制造及其自动化 专业 08 级 1 班学 号 080105032 学生 杜腾飞 指导教师 闫存富 毕业设计（论文）题目 MJ50型数控车床电动刀架设计 毕业设计（论文）工作内容与基本要求（目标、任务、途径、方法，应掌握的原始资料（数据）、参考资料（文献）以及设计技术要求、注意事项等）（纸张不够可加页）一、设计技术要求、原始资料（数据）、参考资料（文献） 1、设计要求：主要是电动刀架整体的结构设计，以结构设计和关键零部件设计为主线，以理论计算及计算机辅助设计为辅的综合性题目。 2、参考文献：数控机床构造，机械设计,金属工艺学及热处理、机械设计手册、机械工程手册等资料。 二、设计目标与任务 1、查阅文献资料不少于12篇，其中外文资料不少于2篇；编写文献综述（不少于3000字）。2、翻译外文科技资料，不少于3000字。3. 完成开题报告。4、完成电动刀架系统总体结构设计，绘制装配图、部件图和零件图，折合A1图纸3张以上。5、编写设计说明书，不少于8000汉字。 三、时间安排13周 完成开题报告、文献翻译、文献综述等411周 完成总体设计、撰写说明书等1213周 修改论文、资格审查等14周 毕业答辩毕业设计（论文）时间： 2012 年 2 月 13 日至 2012 年 5 月 15 日计 划 答 辩 时 间： 2012 年 5 月 19 日专业（教研室）审批意见：审批人签名： 黄河科技学院毕业设计(文献翻译 ) 第9 页集成制造单元面向多产品类型的形成和多样化批量生产技术摘要：追求的类型和不同多产品体积(MPTVV)生产快速反应、快速的切换，使结构线的转移制造系统不再是一成不变的。细胞形成(CF)算法的关键技术是细胞制造系统(CMS)。目前，CF手段主要扩展理念的成组技术(GT)，涵盖了许多资源能力匹配分析及其算法。不同的约束都认为，但很少利用综合运用。摘要针对生产单元的问题(MC)下形成MPTVV生产模式，形成典型的MC技术集成分组类型的细胞(GC)、流量类型(FC),即继承了细胞(IC)技术分析提出了基于自由度。面向实际生产像交货期约束、生产批号、设备能力、关键设备、关键部分和机共享等，是一个完整的地层模型的建立和内部相互关系综合分析这些约束。进而，形成目标的类型及其生成程序变换器在联合作用形成的约束和规则是传播。在案例研究，形成三个高度平衡气相色谱第一；然后俱乐部实施形成基于相同的数据表明良好的平衡作用荷载和flow-style细胞生产关键任务；当任务是调整，通过使用IC形成方法在FC配置的结果上构建了一项新计划，更优的性能flow-style生产的表现。该制度的研究比较不同类型的细胞强烈说明验证的综合MC形成快速制造资源的支持下MPTVV转变生产方式。关键词：多产品批量生产类型和变异，细胞的形成，流式制造单元，继承制造单元1介绍 如今，多种类型和可变的生产模式已成为必然选择，对于大多数企业来说，在适应或正在适应不断变化的需求，我们的社会当然这是一个自然的结果。几个类型和流动的专用生产线和多种类型和离散柔性生产线，多类型和变量生产的小型或中型生产批量生产之间的定位模式，定位系统的变异性和快速反应的效率和灵活性两者的优点。细胞制造业是一个可容纳这种类型的生产模式，并支持快速响应制造系统的生产组织形式，细胞制造的核心是制造资源的重组和重用;自治，协作为特征的制造单元和灵活性的核心组成部分，因此，细胞的形成和重构技术是执行CMS的钥匙。目前，细胞的形成（CF）技术主要集中在它的构造算法。可能形成的规则和约束方面。如多路线和设备类型，工作时间分配，批量生产，设备共享，单位平衡和机容量已全部参与，但有没有这些因素的综合应用。研究人员像安田等人1和GARBIE等人2用于细胞分析的相似性系数。TB根据不断变化的市场解决集群和细胞形成的问题，皮莱等3，提出了一个健全的设计方法，结果细胞结构竟然是相对稳定的。智能算法被引入到CF求解之中，智能算法考虑的因素，如部分数量、路线加工时间、设备容量设备的状态和细胞的平衡和细胞间移动的最小化的目标。ASOKAN等4，普拉巴卡兰等5，和其他许多学者采用蚁群算法。VENKATARAMANAIAH等6，构建了一个单元的配置与使用特殊元素的混合启发式算法。MAHAPATRA等 7，细胞负载平衡和细胞间移动和就业，解决遗传算法的最小化集中。曼苏里等8，使用GA算法来研究瓶颈设备、特殊零部件和设备共享的约束。韩元等9，采用了模糊ART神经网络算法来解决复杂的零部件和设备的分组问题。DEFERSHA等10，引入CF遗传算法的并行运算，在许多实际限制，如单元的配置，替代工艺，设备共享，容量和负载考虑平衡或和生产费用。FTS等11，切割CF卡的过程分为两个步骤。首先，构建了多目标函数为尺度单位，然后采用一个单一的目标函数来优化内部和单位之间的移动。白氏等12，他们的注意力主要在FC形成的理论和紧急动员一批批量生产的技术上。作为一个整体，上述研究主要集中在静态细胞形成，而在可持续发展的动态形成的研究很少，甚至不说综合形成多类型的细胞。本文的主要是制造单元（MC）的多产品类型的多样化和变异量（MPTVV）生产模式下的问题，从多类型的MC，综合形成模型和基于方法的综合建设。统一的约束，制定规则和算法将会是研究和决策的工具。 2 技术上的挑战 根据MPTVV生产模式，产品类型及数量是在不断变化，在不同时期的时间或阶段，不同时期市场需求的变化所引起的，因此使制造系统的配置变化的直接原因是动态的要求。在细胞制造系统（CMS），这种变化将最终归咎于细胞形成，或由细胞进行和实现。 细胞的形成和重构的实质是优化制造资源的分配，它是一种能力制造资源的分配或再分配。资源分配的需要之间的极端情况下的权衡“一台机器是一个细胞”和“整条生产线也是一个细胞” 。此外，以实现快速的资源转化，而不是任务与资源的相匹配，条件下的资源分配和优化比如一批关键设备、关键零部件、设备共享，也是必须要考虑的。执行不成功的重要原因是传统的CF技术的应用，缺乏综合考虑。 CF的结果是逻辑细胞，所以有细胞间和细胞中的机器有没有固定的归属、没有明确的界限。机器可以作为一个独立的“设备单位” ，对于分散的任务，或者他们可以分享它们的能力与其他细胞，并完成任务与其他机器一起。在这种情况下，它可能会出现离散单元。结果配置是一个多元结构的“完整”的单位和一个独立的单位组成。传统的分析，强调太多，可能会导致部分家庭和细胞的独立性不和谐的生产。因此，瓶颈业务和设备，将出现和交配组装生产的要求不能得到满足。此外，细胞负荷分布不均，机器不能被细胞间的共享。 MPTVV生产模式下的细胞类型应灵活多样。在生产多种产品的类型和体积小或试生产中，细胞（GC）组类型的细胞形态是必需的，而在大规模批量生产中，需要实现连续生产。此外，在不同时期，继承可持续重构将是一个基本要求，努力推进传统组技术（GT）为基础的技术的直接原因是，由于需求的快速变化频繁布局调整是难以实现的。作为一个刚性的形成技术，有太多的努力是物理布局调整的成本。设备及零件的相似性为基础的施工方法上存在的资源要求，只强调适应，而不是生产流程的流畅性，认为这也是一个重要的任务路线组织者同意的问题。流线式运行在MC的形成也是一个重要的目标。打击周期性变化的需求，以减少成本和机器调整的影响，CF程序具有继承原有的生产线。存在的CF分析气相色谱仪，研究主要集中在类型的细胞（FC）和继承细胞（IC）的相对很少，相同的地位，形成约束分析和不同类型的管委会统一建设提出。 选区、功能界别和IC分别代表不同的目标和CF的目标，并在制造系统中是典型的细胞形态。面向他们将是进行整合形成多类型细胞的技术分析。3 MC的形成和其约束分析的综合模型 MC形成多个相关的约束下，在MC的形成，如生产的要求，交货时间和作为批处理内部外部因素，处理时间，机器的能力，关键设备和可选择的机器一并考虑。此外，在生产周期时间目标，成本，设备必须满足实用，因而MC的形成过程又是一个多目标优化过程，没有最佳，但许多次优的解决方案，其中普遍存在。在这里，约束和目标分解和它们的相互关系，以不同类型的MC如图1所示。图1 MC形成和约束分析的综合模型 在CF中资源、路线和任务是三个主要的需要数据，还继承了CF使用前进行比较的数据源配置模式。CF卡规则，包括路由选择，机器（类型）选择和机器任务分配规则。这里主要的CF约束是机器能力，关键还是质量和关键设备。输出政策的细胞和设备，在机器的能力，在资源分配中，三个加班的情况下，外包和设备采购，可以选择短缺的情况下潘氏规则分区组成。在不同类型，甚至MC的形成阶段的过程中，必须采取量化指标，包括相似系数，操作数的细胞间，细胞负载和生产节拍平衡，所有这些规则，约束和目标，构建形成多类型的限制细胞，其中大部分是蹒跚，但一些特殊用途，例如，为FC的形成，是有规则，强调操作平衡的路线和设备的生产节拍时间，和目标细胞中的节拍，压力平衡。 形成三种类型的细胞，是不是独立的，还是相互关联的，主要的阶段：设备集群的资源选择与分配，细胞的优化和调整，电池的输出都可以被重用集群和输出的过程是完全通用的，而不同的分配和优化。规则、约束和目标需求可以选择不同类型的细胞。 根据图1，选区和功能界别有两个基本的细胞（BC），而且FC是GC和IC的扩展也可以看作是基本细胞BC的一个扩展，作为一个整体，GC和FC构造一样，就像BC和IC一样。4一体化形成的目的和步骤 为方便起见，采取流动的符号表达图约束。 （1）基础数据集：S0=D1，D2，D3，D4 DL表示生产任务；D2表示部分航线； D3表示设备资源；D4表示配置方案。 （2）形成规则的设置：S1 = E1E11，E12，E13，E14，E15，E2E21，E22，E3E31，E32 。E1表示路线的选择，包括规则，E1L表示我的第一条路线； E12。表示至少机器类型；E13表示最短的加工时间；E14表示至少细胞间运行时间；E15表示最高操作平衡指数E2的代表机（类型）选择规则；E21表示当前机器类型； E22表示最短的加工时间；E23加工节拍时间限制表示E3的代表机任务分配规则。E31表示任务分散机平均使用；E32表示任务集中和集中使用的机器。 （3）形成的制约：S2 = F1，F2 F3，F4，F5 。 F1表示机器的能力；F2表示批次； F3键表示关键零部件； F4键表示质量部分； F5表示关键设备。 （4）设置的输出规则：S3 = G1，G2 G3 。G1的表示细胞的分区规则；G2表示部分分区规则；G3表示共享设备的分区规则。 （5）形成一套目标：S4= H1h11，h12，H2，H3h31，h32，H4 。H1的表示相似系数，h11表示相似系数；h12表示联合国的相似性系数。H2表示至少间细胞的细胞操作数；H3的表示细胞负载平衡； h31表示细胞的负荷率；h32代表比例平衡细胞。H4表示节拍时间平衡。 （6）设备短缺的对待：S5=I1，I2，I3。 I1表示加班；I2表示外包；I3表示设备的采购。 （7）对CF的四个步骤描述如下：第1步表示设备集群； 第二步表示将代表资源分配；第3步表示细胞的优化和调整；第四步表示模式输出。 在本文中，四个不同约束条件和目标的步骤，标记清楚，使用步进目标的启发式算法，将是最合适的解决问题的方法。5示例分析 以上启发式算法已经实现的VC+环境案例研究中使用表1和表2中的设备资源数据的任务数据它是指出，在表1括号中的数量后，一部分是我们的任务数、序列部分操作数路线和它的操作数;由三部分P2，P8，P11组成的选择路线，他们用阴影标注出来;加工时间以分钟为单位，这个任务的交付时间为两个月。表1 任务资源列表表2 设备资源列表6结论 （1）综合模型的构建，对MC形成约束进行了分析。MC形成受多个相关的约束。它的目标可以被容易的扩展，使它可以用于优化一种多元化的“更多”的目标的问题中。MC形成不同类型的影响及其形成过程相互之间的内在联系。在细胞形成的不同时期、不同的约束和规则逐步付诸实施。 （2）形成一体化的目标和程序提出。细胞形成约束是数学符号和不同阶段细胞 的类型来表示的。形成目标的公式描述。应用这种约束和目标的过程也是详细的。 （3）示例分析表明，综合MC的形成是一个有价值的方法，适应MPTVV生产模式。参考文献1 LI Liang，LI Hongzhi，SONG lian，et a1Road friction estimation under complicated maneuver conditions for active yaw controlJChinese Journal of Mechanical Engineering,2009，22(4)：5145202 VAN ZANTEN A TControl aspect of Bosch-VDCCThe3rd International Symposium on Advanced Vehicle Control Aachen, Germany1 996：5736073HAITTOR1 H，KOIBUCHI K，YOKOYAMA TForce and moment contr01 with nonlinear optimum distribution for vehicle dynamicsCThe 6th International Symposium on Advanced Vehicle Control, Hiroshima，Japan2002：595-6004LI Liang，SONG Jiang，WANG Huiyi，et a1Fast estimation and compensation of the tire force in real time control for vehicle dynamic stability control systemJInternational Journal of Vehicle Design，2008，48(3-4)：208-2295KIN K，RYU H，IKEDA T，et a1Enhanced vehicle stability and stecrability with VSACThe 6th International Symposium on Advanced Vehicle Control HiroshimaJapan2002：75-806TSENG H E，AsHRAFI B，MADAU DThe development of vehicle stability control、 at fordJ IEEE，ASME Transactio on Mechatronics，1999，4(3)：223-2347RAY LAuRA RNonlinear state and tire force estimation for advanced vehicle controlJ1EEE Transaction on Control System Technology,199513r11：117-1248LEE Chankyu，HEDRjCK Karl，YI KyongsuReal-time slipbasedIntegrated Manufacturing Cell Formation Technology orienting Multi-productType and Variant Volume ProductionAbstract：What is pursued by multi-product type and variant volume(MPTVV) production is rapid response and quick switching，so that structure of transferring line in manufacturing system is no longer unalterableCell formation(CF) algorithm is the key technology of cellular manufacturing system(CMS)Currently，CF methods are mainly extended on the idea of group technology(GT) that covers a lot on analysis of resource capability matching and its algorithmVarious constraints are considered，but seldom utilized comprehensivelyAimed to the problem of manufacturing cell(MC) formation under MPTVV production mode，integrated formation Technologies for typical MC as group type of cell(GC)，flow type of cell(FC) and inherited cell(IC) are presented based on technical analysis of CFOriented to practical production constraints like delivery time，product batch，equipment ability ,key machine，key part and machine sharing，etc，an imegrated formation model is constructed and intemal imterrelations of these constraints are analyzed syntheticallyUlteriorly，formation goals of types of MCs and their formation procedures under joint effect of formation constraints and rules are spreadIn casestudy，three highly balanced GC are formed first; then FC formation are implemented based on the same data which indicate good balancing effect of cell load and flow-style production for key tasks；When task is adjusted，a new scheme is constructed on the result of FC configuration by using IC formation method，and more optimal performance of flow-style production is manifestedThe proposed comparative study of different type of cells strongly explains the validation of integrated MC formation in support of rapid manufacturing resource transformation under MPTVV production modeKeywords：multi-product type and variant volume production，cell formation，flow style manufacturing cell， inheriting manufacturing cell1 Introduction Nowadays, multiple type and variable production mode has been an inevitable choice for most enterprises, which is a natural result in the course of adapting or being adapted to the ever changing needs of our society. As a mode positioned between few type and mass production of flowing dedicated production line and multiple type and small or medium production of discrete flexible production line, multiple type and variable production has both advantages of efficiency and flexibility targeting variability and rapid response of system. Cellular manufacturing is a form of production organization which can accommodate to such type of production mode，and well support rapid response of manufacturing systemThe core of cellular manufacturing is the reorganization and reuse of manufacturing resources；manufacturing cell characterized with self-government，collaboration and flexibility is the core componentTherefore，technologies of cell formation and reconfiguration are also the keys in implementation of CMS Presently,technologies of cell formation(CF)are mainly focusing on its construction algorithmLikely aspects of formation rules and constraintssuch as multi-routes and equipment types，work time assignment，batch production， equipment sharing, balancing of unit and machine capacity have all been involved，but there is an absence of integrated application of these factorsResearchers like YASUDA，et al1 and GARBIE，et al2 used similarity coefficient for cell analysisTb solve the problem of formation of clusters and cells under changing market，PILLAI，et al3，proposed a robust design method based on demand forecast and the result cell structure turned out to be relatively stableIntelligent algorithm was introduced for CF solvingConsidering factors like part number, route，processing time，equipment capacity equipment status and objectives of cell balancing and minimization of inter-cell movingASOKAN，et al4，PRABHAKARAN，et al5,and many other scholars adopted ant colony algorithm VENKATARAMANAIAH, et al6, constructed an unit configuration with exceptional elements using hybrid heuristic algorithmMAHAPATRA，et a7，concentrated on cell load balancing and minimization of inter-cell moving and employed GA for solutionMANSOURI, et al8， studied constraints of bottleneck equipments，exceptional parts and equipment sharing using GA methodWON，et al9，adopted fuzzy ART N-N algorithm to solve grouping problem of complicated parts and equipmentsDEFERSHA，et al10，introduced GA parallel arithmetic into CF, in which many practical restriction like cell configuration，substitute process，equipments sharing and capacity and load balancing or them，and production fees were taken into accountFTS，et al11，cut CF procedure into two steps Firstly， multi-objective function was constructed for scaling units，and then a single objective function was employed targeting optimization of moving within and between unitsBAI，et al12，paid their attention to FC formation theory and technique for emergency mobilization batch volume productionAs a whole，above researches are mainly focused on static cell formation，while studies on sustainable dynamic formation are seldom， even not to say integrated formation of multicell types Main focus of this article will be on the problem of manufacturing cell(MC) diversification under multi-product type and variant volume(MPTVV) production mode From the view of integrated construction of multi-type MCs，integrated formation model and method based on unified constraints，rules and algorithms will be studied，and decision-making tools will be developed2 Technical Challenge Under MPTVV production mode，product type and volume is on constant change at different period of time or stages of different period stemming from market requirement changeSo the direct reason bringing configuration change of manufacturing system is dynamic requirement. In cellular manufacturing system(CMS)，such change will finally impute to cells formed，or be undertakenand realized by cells The essential of cell formation and reconfiguration is optimizing assignment of manufacturing resourceIt is a kind of allocation or reallocation of ability of manufacturing resource Resource assignment needs to tradeoff between extreme cases of “one machine is one cell” and “the whole line is also one cell”Moreovertorealize rapid resource transformation，other than matching between task and resource， resource assignment and optimization under conditions like delivery time，batch，keymachine， key part and equipment sharing have to be considered also Important reason of unsuccessful implementation applying traditional CF technologies is in1ack of comprehensive consideration The outcome of CF is logical cells，so there are no clear boundaries among cells and machines in cells have no fixed attributionMachines may act as a standalone “device unit” for scattered tasks，or they can share their capacity with other cells and accomplish tasks with other machinesIn this case，its possible that discrete units will emergeThe result configuration is a plural structure composed by complete” units and a discrete unitTraditional analysis emphasizes independence of part family and cells too much that may result in unharmonious production Thus， bottleneck operations and equipments will appear and requirement of mating production for assembly cannot be satisfiedMoreover, cell load distribution is uneven and machines cannot be shared among cells Cell types under MPTVV production mode should be flexible and various In multi-product type and small volume or trial production，cell form of group type ofcell(GC)is required，while in mass volume production， continuous production has to be realized Furthermore，during different period，inheriting sustainable reconfiguration will be a basic demandThe direct reason of hard advancing of traditional group technology(GT) based technologies is that frequent layout adjustment due to rapid change of requirement is difficult to realizeAs a rigidformation technology，too many efforts have to be cost on physical layout adjustmentConstruction method based on similarity of equipments and parts only emphasizes adaptation of existed resources to requirements，but not fluency of production flow in the view of routes of tasks which is also an important problem consented by organizersFlow line type running is also an important objective in MC formationAgainst periodical changing requirements， to lessen cost and influence of machine adjusting，CF procedure has to inherit original production lineExisted CF analysis mainly focuses on GC，researches on type of cell(FC)and inherited cell(IC)are comparatively seldom，and same status presents in formation constraint analysis and unified construction of different types of MCs GC，FC and IC represent different objectives and targets of CF and are typical cell forms in manufacturing systemTechnical analysis on integrate formation for multi-type cell will be carried out orienting them3 Integrated Model of MC Formation and Its Constraints Analysis MC is formed under multiple related constraintsDuring MC formation，external factors like production requirement，delivery time and internal ones as batch，processing time，machine ability, key equipment and selectable machine have to be considered togetherAlso，production goals in cycle time，cost，equipment utility have to be satisfied，thus the course of MC formation is again one multi-objective optimization procedure，in which no optimum but many suboptimum solution exists commonlyHere，constraints and goals are decomposed and their interrelations todifferent type of MCs are illustrated in Fig1 Resource.route and task are three main data needed in CF, yet inherited CF has to use former configuration schemas as source data for comparingCF rules include route selection。machine(type)selection and machine task assignment rulesMain CF constraints here are machine ability batch，key or mass pans and key equipmentOutput policies are composed of partition rules for cell and equipment and pan family In case of shortage of machine ability in resource assignment，three scenarios of overtime，outsource and equipment procurement can be selectedIn the process of different types or even stages of MC formation， quantitative indexes including similarity coefficient，inter-cell operation number, cell load and takt time balancing have to be adopted All these rules，constraints and goals construct formation constraints of multi-type cellMost of them are shamble，but some are for special purposeFor example，for FC formation，therere rules that emphasize operation balancing of route and production takt time of equipment，and goals that stress taktbalancing in cell Formation of three type of cell is not independent but interrelated， main stages： equipment cluster resource selection and assignment，cell optimization and adjustingand cell output can all be reusedCluster and output process are entirely universal， while assignment and optimization is different for different type of cell that rules，constraints and goals can be selected in demand According to Fig1，GC and FC are two basic cell(BC)，and that FC is extended on GC and IC can be seemed as extension of BCAs a wholeGC constructs the constraints of FC like that BC to IC4 Objective and Procedure of Integrated Formation For convenience，the flowing symbols are taken to express constraints in Fig1 (1)Base data set：S0=D1，D2，D3，D4Dl denotes production tasks； D2 denotes part routes； D3 denotes equipment resources；D4 denotes configuration schemes (2)Formation rules set：S1=Ele11，e12，e13，e14，e15，E2e2l，e22，E3e3l，e32El denotes routes selection rules，including；e1l denotes me first route; e12 denotes least machine type；e13 denotes shortest machining time；e14 denotes least inter-cell operation time；e15 denotes highest balancing index of operationsE2 denotes machine(type) selection rules； e2l denotes current machine type； e22denotes shortest machining time；e23 denotes machining time limited by takt time E3 denotes machine task assignment rules；e31 denotes task dispersed and averageuse of machines；e32 denotes task concentrated and focus use of machines (3)Formation constraints set：S2=F1,F2,F3,F4,F5F1 denotes machine ability；F2 denotes batch；F3 denotes key parts；F4 denotes mass parts；F5 denotes key equipment (4)Output rules set：S3=G1，G2，G3G1 denotes cell partition rules；G2 denotes part partition rules；G3 denotes sharable equipment partition rules (5)Formation goals set：S4=H1h11，h12，H2，H3h3l，h32，H4Hl denotes similarity coefficient，h11 denotes simil