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摘要 3
Abstract 4
第1章 緒論 5
1.1課題的來源，目的及意義 5
1.2立體車庫概述 5
1.3幾種機械停車設備的特點及比較 7
1.4 總體方案確定 8
第2章 鏈驅(qū)動雙層升降橫移式車庫技術參數(shù) 12
2.1傳動機構的組成 12
2.2 傳動機構的主要參數(shù) 12
第3章 機械系統(tǒng)傳動設計 13
3.1傳動鏈和鏈輪的選擇 13
3.2鏈的設計計算 13
3.2.1設計標準 13
3.2.2傳動的計算 14
3.3 鏈輪的設計計算 16
3.3.1 設計鏈論尺寸 16
3.4 滾子鏈的靜強度計算 22
3.5 鏈條的使用壽命計算 23
3.6 鏈條的耐磨工作能力計算 24
3.7 電動機的選擇 25
3.8減速器的選擇 26
3.9軸承的選擇 27
3.10軸的選擇 27
3.10.1 選擇材料 27
3.10.2 初步估算軸徑 27
3.10.3軸的結構設計 28
3.10.4 軸上的受力分析 28
3.10.5軸的強度校核 30
第4章 經(jīng)濟分析 35
4.1 機會研究 35
4.1.1社會需求程度 35
4.1.2 開展的基本條件 35
4.2 初步可行性研究 35
4.2.1 投資機會是否有希望 35
4.2.2是否需要作詳細可行性分析 35
4.2.3 有待解決的關鍵性問題 36
4.2.4 初步經(jīng)濟效益預測 36
第5章 專題 37
自動化立體車庫管理系統(tǒng) 37
結 論 44
參考文獻 45
致謝 46
附錄1：翻譯（英文） 47
附錄2：翻譯（漢文） 68
2
摘要
隨著汽車工業(yè)和建筑業(yè)兩大支柱產(chǎn)業(yè)的快速發(fā)展，在一些大、中城市相繼出現(xiàn)了停車難和亂停車的現(xiàn)象。在解決城市城市停車難的問題中，機械式立體停車設備以其獨特的優(yōu)點，引起了各界的重視，得到了廣泛的應用。
車輛無處停放的問題是城市的社會、經(jīng)濟、交通發(fā)展到一定程度產(chǎn)生的結果，立體停車設備的發(fā)展在國外，尤其在日本已有近30~40年的歷史，無論在技術上還是在經(jīng)驗上均已獲得了成功。我國也于90年代初開始研究開發(fā)機械立體停車設備，距今已有十年的歷程。由于很多新建小區(qū)內(nèi)住戶與車位的配比為1:1，為了解決停車位占地面積與住戶商用面積的矛盾，立體機械停車設備以其平均單車占地面積小的獨特特性，已被廣大用戶接受。
機械車庫與傳統(tǒng)的自然地下車庫相比，在許多方面都顯示出優(yōu)越性。首先，機械車庫具有突出的節(jié)地優(yōu)勢。以往的地下車庫由于要留出足夠的行車通道，平均一輛車就要占據(jù)40平方米的面積，而如果采用雙層機械車庫，可使地面的使用率提高80％-90％，如果采用地上多層（21層）立體式車庫的話，50平方米的土地面積上便可存放40輛車，這可以大大地節(jié)省有限的土地資源，并節(jié)省土建開發(fā)成本。
論文以研究工作的進展順序為序，分章、節(jié)逐一論述了課題的來源，目的及意義，設計過程，在設計中遇到的問題與難點及其解決方法與措施。在設計過程部分，詳細論述了設備總體結構設計、橫移傳動系統(tǒng)設計、提升傳動系統(tǒng)設計、存取車結構設計、控制系統(tǒng)設計和安全防護設計 。
關鍵詞:立體停車設備 ； 傳動設計； 控制系統(tǒng) ； 升降機構； 安全性
Abstract
Along with the fast development of two major of pillar industry of the automobile industry and building industry, the city one after another appears to park the car the phenomenon of the difficult and disorderly parking in somely bigly,.In resolve the city city park the car difficult problem, stereoscopic parking equipments of the machine type with its special advantage, caused the value of the public, get the extensive application.
The vehicle has no a the society, economy, transportation that the problem for park is a city to develop output result to certain extent, the development of the stereoscopic parking equipments is abroad, particularly at Japan already is close to 30~40 years of history, all have already acquired the success on the technique still on the experience regardless.The our country also starts study to develop the stereoscopic parking equipments of machine at the beginning of in 90's, being apart from to already have the process of the decade now.Because a lot of set up the little inhabitant inside the area to go together with the car to compare to 1 lately:1, for solving the parking lot to cover the area and inhabitant's company to use the antinomy of the area, stereoscopic machine parking equipments with it average the bicycle covers the small special characteristic of area, have already been accept by the large customer.
The machine garage and traditional get off the database to compare naturally, in many respect display the superiority.First, the machine garage has an advantage of outstanding stanza.Before underground garage because of toing stay to go the car passage enough, average car will occupy 40 areas of the square meters, but if a layer of adoption machine garage, can make the utilization rate of the ground raise 80%-90%, if an up many type garages with stereoscopic layer(21 layers) of adoption, can deposit 40 cars then on 50 areas of the land of the square meters, this resources that can save the limited land consumedly, and save the soil to set up to develop the cost.
Currently the society contain a lot of mature and technical parking equipmentses for example:Ascend and descend horizontal move the type;Perpendicular and circulating type;Level circulation type;The flat surface moves the type;A of tunnel;The perpendicular rise and fall type;Simple rise and fall.Pass the comparison and analysises to these garages type characteristicses, I was end to choose a garage type that is the design.
Thesis with research the progress of the work is in proper order for the preface, dividing the chapter, stanza to discuss the source of the topic one by one, purpose and meaning, the design process, problem meet in the design with a little bit difficult and it resolve method and measure.At design the process part, detailed discussed the total structure of equipments design, horizontal move to spread to move the system design and promote to spread to move the system design, access the car structure design, control the system design and safe protection design.
Keyword: stereoscopic parking equipments; The structure design; Control the system; PLC; Safety
5
附錄1：翻譯（英文）
Modeling and specifcations of dynamic agents in fractal manufacturing systems
Kwangyeol Ryua, Youngjun Sonb, Mooyoung Junga,*
a Department of Industrial Engineering, Pohang University of Science and Technology, Pohang, South Korea Systems and Industrial Engineering Department, The University of Arizona, Tucson, AZ, USA
b Received 9 September 2002; accepted 16 April 2003
Abstract In order to respond to a rapidly changing manufacturing environment and market, manufacturing systems must be flexible, adaptable, and reusable. The fractal manufacturing system (FrMS) is one of the new manufacturing paradigms that address the need for these characteristics. The FrMS is comprised of a number of ‘‘basic components’’, each of which consists of five functional modules: (1) an observer, (2) an analyzer, (3) an organizer, (4) a resolver, and (5) a reporter. Each of these modules, using agent technology, autonomously cooperates and negotiates with others while processing its own jobs. The resulting architecture has a high degree of self-similarity, one of the main characteristics of a fractal. Despite the many conceptual advantages of the FrMS, it has not been successfully elaborated and implemented to date because of the difficulties involved in doing so. In this paper, the static functions and dynamic activities of each agent are modeled using the unified modeling language (UML). Then, relationships among agents, working mechanisms of each agent, and several fractal-specific characteristics (selfsimilarity, self-organization, and goal-orientation) are modeled using the UML. Then, a method for dealing with several types of information such as products, orders, and resources in the FrMS is presented. Finally, a preliminary prototype for the FrMS using AgletsTM is presented. # 2003 Elsevier B.V. All rights reserved. Keywords: Fractal manufacturing system (FrMS); Agent technology; UML; Modeling
Abbreviations: FrMS, fractal manufacturing system; BFU, basic fractal unit; DRP, dynamic restructuring process; UML, uni?ed modeling language; HMS, holonic manufacturing system; BMS, bionic/biological manufacturing system; CNP, contract net protocol; MANPro, mobile agent-based negotiation process; NMA, network monitoring agent; EMA, equipment monitoring agent; SEA, schedule evaluation agent; DRA, dispatching-rule rating agent; RSA, real-time simulation agent; SGA, schedule generation agent; GFA, goal formation agent; TGA, task governing agent; NEA, negotiation agent; KDA, knowledge database agent; DMA, decision-making agent; FSM, fractal status manager; FAM, fractal address manager; REA, restructuring agent; NCA, network command agent; ECA, equipment command agent; STA, system agent; NTA, network agent; MP, material processor; MH, material handler; MT, material transporter; BS, buffer storage; MRP, material removal processor; MFP, material forming processor; MIP, material inspection processor; PD, passive device; FMH, ?xed material handler; MMH, movable material handler; FMT, fixed material transporter; MMT, movable material transporter; ABS, active buffer storage; PBS, passive buffer storage E-mail address: myjung@postech.ac.kr (M. Jung).
* Corresponding author. Tel.: t82-54-279-2191; fax: t82-54-279-5998.
0166-3615/$ – see front matter # 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0166-3615(03)00099-X
1. Introduction
Facing intensified competition in a growing global market, manufacturing enterprises have been reengineering their production systems to achieve computer integrated manufacturing (CIM). Major goals of CIM include, but are not necessarily limited to, lowering manufacturing costs, rapidly responding to changing customer demands, shortening lead times, and increasing the quality of products [1–3]. However, the development of a CIM system is an incredibly complex activity, and the evolution to CIM has been slower than expected [4,5]. This can be directly attributed to high software development and maintenance costs. Therefore, in order to achieve a competitive advantage in the turbulent global market, the manufacturing enterprise must change manufacturing processes from all angles including ordering, product design, process planning, production, sales, etc. As a control model for implementing CIM systems, hierarchical decomposition of shop floor activities has been commonly used in the shop floor control system (SFCS), the central part of a CIM system [2]. Generally, a central database provides a global view of the overall system, and controllers generate schedules and execute them. Hierarchical control is easy to understand and is less redundant than other distributed control architectures such as heterarchical control. However, it has a crucial weak point, which is that a small change in one level may significantly and adversely affect the other levels in the hierarchy. Therefore, it is normally said that hierarchical control of CIM systems is much more suitable for production in a steady environment than in a dynamically changing environment because it is so diffcult to apply control hierarchy changes immediately to the equipment. Furthermore, it is diffcult to meet dynamically changing customer requirements because the hierarchical control architecture is not flexible enough to handle the reconfiguration of the shop. Therefore, the manufacturing system of tomorrow should be flexible, highly reconfigurable, and easily adaptable to the dynamic environment. Furthermore, it should be an intelligent, autonomous, and distributed system composed of independent functional modules. To cope with these requirements, new manufacturing paradigms such as a bionic/biological manufacturing system (BMS) [6,7], a holonic manufacturing system (HMS) [8,9], and a fractal manufacturing system (FrMS) [10–13] have been proposed. Tharumarajahet al. [14] provide a comprehensive comparison among a BMS, a HMS, and an FrMS in terms of design and operational features. An FrMS is a new manufacturing concept derived from the fractal factory introduced by Warnecke [13]. It is based on the concept of autonomously cooperating multi-agents referred to as fractals. The basic component of the FrMS, referred to as a basic fractal unit (BFU), consists of five functional modules including an observer, an analyzer, a resolver, an organizer, and a reporter [10,11]. The fractal architectural model represents a hierarchical structure built from the elements of a BFU, and the design of a basic unit incorporates a set of pertinent attributes that can fully represent any level in the hierarchy [12]. In other words, the term ‘fractal’ can represent an entire manufacturing shop at the highest level or a physical machine at the bottom-level. Each BFU provides services according to an individual-level goal and acts independently while attempting to achieve the shoplevel goal. An FrMS has many advantages for a distributed and dynamic manufacturing environment. Automatic reconfiguration of a system through a dynamic restructuring process (DRP) is the most distinctive characteristic of the FrMS. In this paper, the scope of the reconfiguration does not include reconfigurable hardware [15] and external layout design. Rather, it focuses on the interior structure of software components that can be reorganized with software manipulations. The reconfiguration or restructuring in this paper considers both dynamic clustering of the agents and construction/destruction/cloning of agents, which affect the number of agents in the system. The function of a fractal is not specifically designated at the time of its first installation in the FrMS. The reconfiguration addressed in this paper also includes situations where the agents’ enrollments are changed, meaning that the agents are assigned a new goal and new jobs, but their composition does not change. This paper focuses on formal modeling of agents and fractal-specific characteristics that will provide a foundation for the development of the FrMS. Because associated difficulties have, to date, prevented a fractal-based system from being embodied, it is necessary to first explicitly define a concept, mechanisms, and characteristics.
The objective of this paper, therefore, is to clearly define and model fractal-specifc characteristics for a manufacturing system to have such characteristics. In order to develop the agents, interand intra-fractal activities are first clarified. Then, dynamic activities for each agent and relationships between agents are modeled. In order to more fully develop the FrMS, several fractal-specific characteristics are also modeled. To support embodiment of modeled characteristics, a method for dealing with information about products, orders, and resources in the FrMS is investigated. Through this research, mechanisms of agents and characteristics of the FrMS can be described with simple diagrams that make the system easier to understand. The work contained in this paper extends the FrMS from previous papers by emphasizing and detailing its characteristics. The activities of agents are specified using activity models so that the agents can use the activity models to forecast their next activities at run-time. The rest of this paper is organized as follows: Section 2 describes functions and dynamic activities of agents using functional and activity models of unified modeling language (UML). In Section 3, inter- and intra-fractal activities are specified. Several fractal-specific characteristics are described using UML models in Section 4. Section 5 describes a method for dealing with information about products and resources in the FrMS. Section 6 concludes the paper.
2. Agent-based fractal manufacturing system (FrMS) 2.1. Background of an FrMS An overview of the FrMS is depicted in Fig. 1. Every controller at every level in the system has a selfsimilar functional structure composed of functional modules. In addition, each of these modules, regardless of its hierarchical level, consists of a set of agents. After the initial setup of a system, the configuration of the system may need to be reorganized in response to unexpected events such as machine breakdown. The system will also need to be reconfigured when the set of parts to be produced in the system changes due to a change in customer needs. In these cases, fractals in the FrMS autonomously and dynamically change their structure, via the actions of agents for the appropriate working mechanisms of the fractals. Fig. 1 shows two facility layouts and the corresponding compositions of fractals before and after the restructuring process. When a machine (M) and a robot (R3) are added to the system, fractals reorganize their interior configurations with the mechanism of dynamic restructuring process in a way that the system continues to work with greatest efficiency. A fractal consists of five functional modules illustrated with their relationships in Fig. 2. The functions of each module can be defined depending upon the application domain.
Fig. 1. Reorganization of the system using a dynamic restructuring process in the FrMS.
However, when the target domain is determined, the main functions of each module will be consistent throughout the system. For example, the function of a resolver may be different depending upon whether it is defined for controlling a manufacturing system or for managing supply chains. However, the main function of a resolver in a manufacturing system is similar to other resolvers in that system regardless of their level in the hierarchy. A bottom-level fractal has similar functions to those of a conventional equipment controller in a SFCS. A fractal, which is directly connected to equipment (e.g. machine, robot, etc.), receives sensory signals of equipment and returns messages or commands. The function of an observer is to monitor the state of the unit, to receive messages and information from outer fractals, and to
Fig. 2. Functional modules and relationships of a fractal in an FrMS.
transmit composite information to correspondent fractals. The function of an analyzer is to analyze alternative job profiles with status information, to rate dispatching rules, and to simulate analyzed job profiles in real-time. The analyzer finally reports results to the resolver so that the resolver can use them to make decisions. A resolver plays the most important role in a fractal, generating job profiles, goal-formation processes, and decision-making processes. During goal-formation processes, the resolver may employ a variety of numerical optimization or heuristic techniques to optimize the fractal’s goal. If necessary, the resolver executes negotiations, cooperation, and coordination among fractals. The function of an organizer is to manage the fractal status and fractal addresses, particularly for dynamic restructuring processes. The organizer may use numerical optimization techniques to find an optimal configuration while reconfiguring fractals. The fractal status is used to select the best job profile among several alternatives, and the fractal address is used to find the physical address of the fractal (e.g. machine_name, port_number, etc.) on the network. The function of a reporter is to report results from all processes in a fractal to others. In the case of a bottom-level controller, the fractal is similar to a traditional equipment controller. Therefore, most of its messages are commands for controlling the hardware.
2.2. Agents in an FrMS
Agent technology has been widely used for various applications including information filtering and gathering [16], knowledge management [17], supply chain management [18], manufacturing architecture, system and design [19–21]. While the features and characteristics of an agent vary depending on the application, some common features found across different applications are as follows: Autonomy: capability of controlling and acting for itself in order to achieve goals. Mobility: capability of migrating its location to other places (an agent with mobility is called a mobile agent, otherwise known as a software or stationary agent). Intelligence: capability of learning and solving problems. Cooperativeness: capability of helping others if requested and accepting helps from others. Adaptability: capability of being effectively used at various domains. Reliability: capability of dealing with unknown situations (disturbances) and continuing actions if committed, etc. The mobility of agents is a useful feature in a distributed and dynamic system. A mobile agent is not bound to the system where it begins execution. It can travel freely among the controllers in a network and transport itself from one system in a network to another. The following are some advantages of the use of mobile agents in a system [22]: (1) it reduces the network load, (2) it overcomes network latency, (3) it encapsulates protocols, (4) it executes asynchronously and autonomously, (5) it adapts dynamically, (6) it is naturally heterogeneous, and (7) it is robust and faulttolerant. The types and functions of agents that implement functional modules of an FrMS have been brie?y described, and their initial development has been published in the earlier literature [11]. This paper enhances and re?nes the previously defined types and functions of agents so that they can perform functions of fractals successfully in the system. The names, types, and functions of agents in the FrMS are described as follows. The terms ‘‘-M’’ and ‘‘-S’’ written after the abbreviated name of each agent represent mobile agents and software agents, respectively.
2.2.1. Agents for an observer
Network monitoring agent (NMA-S): It monitors messages from other fractals through TCP/IP. It receives messages from the upper/same/lower-level fractals, such as requests for negotiations, negotiation replies, job orders, status information, etc. The NMA delivers those messages to the resolver or the analyzer. Equipment monitoring agent (EMA-S): It monitors messages directly coming from equipment through a serial communication protocol such as RS232/ 422. Information on the status of equipment including signals indicating the start and completion of jobs are detected by the EMA. However, the fractal need not directly control equipment if it is not included in a bottom-level.
2.2.2. Agents for an analyzer
Schedule evaluation agent (SEA-S): A SEA evaluates job profiles generated by the resolver. It helps the resolver to select the best job profile with respect to the current situation of the fractal. Dispatching-rule rating agent (DRA-S): It chooses the best dispatching rule for achieving its goals among several rules, such as shortest processing time (SPT), earliest due date (EDD), and so on. Real-time simulation agent (RSA-S): It performs real-time simulations in the on-line state with the results of the analyzed job profiles and the best dispatching rule. The RSA reports the results of simulations to the resolver.
2.2.3. Agents for a resolver
Schedule generation agent (SGA-M): It generates operational commands or alternative job profiles for achieving the fractal’s goals. After evaluation and analysis of alternatives in the analyzer, the SGA selects the best job profile. It must have mobility in order to use SEA, DRA, and RSA in the analyzer.
Goal formation agent (GFA-S): It modifies incomplete goals delivered from the upper-level fractal, and tries to make the goals complete by considering the current situation of the fractal. GFA divides the goal of the fractal into several sub-goals, and sends them to the sub-fractals. Task governing agent (TGA-S): A TGA generates tasks from the best job profile and its goals. It also performs tasks after arriving at the target fractal. When it finishes performing tasks, it sends acknowledgement to its sender. Negotiation agent (NEA-M): It moves to other fractals to deliver negotiation messages or to gather negotiation replies created by participating agents. It filters out unreasonable replies by a pre-evaluation process and brings the rest back to the resolver. Knowledge database agent (KDA-M): KDA invokes knowledge data from the knowledge database to make decisions. It accumulates new knowledge or updates the existing knowledge. Decision-making agent (DMA-S): It performs several operations during the decision-making processes. A DMA creates NEAs to negotiate with other fractals and KDAs to use the knowledge database. After making decisions, the DMA generates several TGAs. Further, the DMA provides a context to agents for negotiation.
2.2.4. Agents for an organizer
Fractal status manager (FSM-S): The FSM collects and manages the information on the status of fractals that is used for analyzing job profiles in the analyzer. It also makes negotiation replies to the status requests from other fractals. Fractal address manager (FAM-S): The FAM manages information about the addresses of fractals in lower levels and at the same level. A fractal address is the fractal’s physical address on the network, such as an IP address. The reporter uses a fractal’s address to confirm the destination of tasks and messages. Restructuring agent (REA-M): It performs several operations related to dynamic restructuring processes, such as BFU generation, BFU deletion, and the evaluation of the fractal’s performance. The performance of a fractal is its utilization, e.g. total number of processed jobs or the portion of processing time within total time, etc. If the REA decides that a fractal needs to be restructured, it gathers information about fractal and network addresses, and fractal status. It moves to the DMA and lets it generate a series of jobs for a restructuring process. The cloning mechanism is used to create a new BFU. After creation, the REA tells the FAM to update the addresses of other fractals.
2.2.5. Agents for a reporter
Network command agent (NCA-M): All tasks or messages are deliver