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玻璃横切结构及人机界面系统设计【7张机械CAD图纸+毕业论文】

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关键词：: 玻璃横切切结人机界面系统设计机械 cad图纸毕业论文玻璃横切结构

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玻璃横切结构及人机界面系统设计
40页 17000字数+说明书+外文翻译+开题报告+7张CAD图纸【详情如下】
主传动轴.dwg
刀架.dwg
外文翻译--关于装载适应性神经模糊系统的有两足行走的机器人的零刻点弹道造型中文版.doc
外文翻译--关于装载适应性神经模糊系统的有两足行走的机器人的零刻点弹道造型英文版.doc
控制系统电路原理图.dwg
摘要目录.doc
支架.dwg
机箱A1.dwg
法兰盘.dwg
玻璃横切机装配图A0.dwg
玻璃横切结构及人机界面系统设计开题报告.doc
玻璃横切结构及人机界面系统设计论文.doc
程序2.txt

QQ截图20150523232726.gif

玻璃横切机装配图A0.gif

刀架.gif

法兰盘.gif

机箱A1.gif

控制系统电路原理图.gif

全部文件.gif

支架.gif

主传动轴.gif

摘要
本次毕业设计的题目是设计一种基于单片机控制的玻璃横切机。主要设计内容为：设计玻璃横切机的机械结构及人机界面系统。机械结构部分的设计确定为步进电动机带动的同步带传动系统。控制系统采用AT89C51为CPU。由矩阵键盘和RT12864M汉字图形点阵液晶显示模块组成人机界面系统。
首先，分析了玻璃横切机的市场前景及其在玻璃生产中的重要作用。然后，确定机械结构。根据现有的玻璃横切机的结构将其分类，并归纳出每一类玻璃横切机的机械机构特征、运行特征、切割原理等。本设计最终选用了其中的斜置、速度控制式的机械结构。基于这种机械结构，对步进电动机、同步带进行了选型和计算。绘制了机械结构装配图及部分零件图。
对于玻璃横切机的人机界面系统，本设计主要大致完成其程序的编写。即液晶显示四组控制参数，并可以通过键盘修改这些参数。本文中包括了各级程序流程图及其对应的说明。大致包括LCD驱动、键盘监控、液晶显示三个程序块。详见正文。
关键字玻璃横切机; 步进电动机; 同步带; 单片机; 人机界面系统 ;液晶???????
This graduation project's topic is the design of one kind of the auto glass-cutter which controls based on the Microcontroller. The main content of the design is: the machine mechanism and man-machine contact surface system of the glass-cutter’s design .The mechanism part's design I determined it as that Pulse motor gives the power and the Transmission system transfer the power to the Ambulacrum.The control system uses AT89C51 as CPU. Forms the man-machine contact surface department of the matrix keyboard and the RT12864M Chinese character graph lattice liquid crystal display module.
First, has analyzed the glass-cutter’s market prospect and its influential role in the glass production. Then, determination of the machine mechanism. According to the existing glass-cutter’s structure I staple then to same types, and then concluded the mechanism character, sport character, cutting principle of each of the types. And so on.
This design finally has selected the tilts, speed control-like mechanism. Based on this kind of mechanism, I did the choice of the Pulse motor, ambulacrum’s and the count, checkout of them. I had drawn up the drawing of mechanism assembly and some part drawings.
Regarding the glass-cutter’s man-machine contact surface system, this design mainly completes its program’s compilation. Namely the liquid crystal display shows the four groups of controlled variables, and may revise these parameters through the keyboard. This article has included all levels of program flow diagram and the corresponding explanation. Includes the LCD actuation, the keyboard monitoring, and the liquid crystal display three blocks approximately. For details sees the main text.
Key-words glass-cutter ; Pulse motor ;ambulacrum ; Microcontroller ; man-machine contact surface system ; liquid crystal display module
目录
摘要（中文）…………………………………………………………………………………?
（英文）…………………………………………………………………………………П
第一章概述……………………………………………………………………………………1
1.1我国玻璃市场现状………………………………………………………………………………………1
1.2 我国浮法玻璃技术与国际先进水平的差距…………………………………………………………1
1.3 高精度玻璃切割的必要………………………………………………………………………………1
1.4 玻璃横切机的分类……………………………………………………………………………………1
第二章设计方案……………………………………………………………………………7
2.1研究内容………………………………………………………………………………………………7
2.2实现方法…………………………………………………………………………………………………7
2.3设计任务…………………………………………………………………………………………………9
2.4 总体方案的确定………………………………………………………………………………………10
第三章机械部分设计计算…………………………………………………………………11
3.1 机械传动部件的计算与选型…………………………………………………………………………11
3.2 机械装配图的绘制……………………………………………………………………………………17
第四章控制系统设计、编程………………………………………………………………19
4.1控制系统硬件电路设计………………………………………………………………………………19
4.2 人机界面的软件设计…………………………………………………………………………………19
结束语………………………………………………………………………………………36
参考文献……………………………………………………………………………………37
横梁导轨安装在玻璃带输送辊道两边, 与玻璃带运动方向平行。切刀小车及其传动机构与导轨安装在横梁上, 横梁与玻璃带输送辊道 (玻璃带运动方向) 垂直放置。横梁在横梁传动机构的带动下做纵向往复运动, 切刀小车在其传动机构的带动下做横向往复运动。
垂直式横切机的基本工作原理是: 根据实际生产的工况与要求, 运动控制系统与机构分别对横梁和切刀小车传动机构进行控制, 使横梁前行运动速度V Z= V L ; 同时, 在电机转矩、转速及负载情况允许的条件下, 应尽可能提高切刀小车的工作速度V H , 以减少横切机整体的工作循环时间。
　　垂直式横切机的特点是控制方式简单易行, 但机械运动机构的组成方式较为复杂,并由于横梁的运动惯量较大, 因而不适用于玻璃带运动速度较高的生产场合。在实际生产中, 垂直式横切机在平拉或格法玻璃生产线上应用较多。
由于垂直式横切机的横梁速度应与玻璃带速度保持一致, 所以垂直式横切机又称为垂直随动式横切机。
1.4.2斜置式横切机
由玻璃带在线切割时所必须具有的横向运动和纵向运动可知, 切刀的实际工作运动, 应该是这两个相互垂直方向上运动的合成。反之, 若控制切刀进行该合成运动, 则可以对应地分解为横向与纵向运动。斜置式横切机, 就是通过把决定切刀小车运动方向的横梁与玻璃带输送辊道 (玻璃带运动方向) 倾斜放置, 并对切刀小车沿横梁的工作运动进行控制, 而实现玻璃带切割时所需的横向与纵向运动。
切刀小车的工作运动速度V Q 与横向运动速度V H 和纵向运动速度V Z 的关系, 可以由式 (1) 和式(2) 表示, 其中Α为V Q 与V H 之间的夹角。
V H= V Q·cosΑ (1)
V Z= V Q·sinΑ (2)
在玻璃切割过程中, 为了保证切痕的平直, 切刀的纵向运动必须与玻璃带的运动保持同步, 即必须保证V Z= V L。当V L 为恒量或基变量时, 由式(2) 可知, 可以通过分别控制Α和V Q 来实现V Z 与V L 相等的要求。若仅变化Α , 则称为角度调节方式;若仅变化V Q , 则称为速度控制方式。此外, 由于机械运动机构实现困难, 通常都不会采用对 Α和V Q同时调控的方式。
(1) 角度调节式横切机
采用角度调节方式的斜置式玻璃横切机, 称为角度调节式横切机。它的机械运动机构由横梁、切刀小车、小车导轨、小车传动机构和角度调节装置等所组成 (如图3 所示)。在角度调节时, 可令
V Z= V Q·sinΑ = V L (3)
则有
Α = arcsinV LV Q(4)
若保持V Q = 常量, 则角度Α与V L 之间的关系可由式 (4) 确定。
在实际生产中, 由于产品规格与实际工况的改变, 将会引起V L 的改变。因此, 需要根据实际确定的或实际测出的V L , 由式 (4) 中求出对应的Α值[Α ∈ (0, 90° ) ], 并据此调节横梁的实际斜置角度。第二章设计方案
2.1、研究内容
研究方向、内容
随着单片机、PLC技术的发展，传统的控制系统逐渐被新型智能控制系统取代。鉴于PLC比单片机成本高，且输入/输出点数受到限制。本次毕业设计我主要研究单片机技术的全自动玻璃横切结构，分别对其机械结构和人机界面系统进行设计。
以下为欲设横切机的功能设定：
1）机械系统功能：
切割速度方向要求：玻璃带为运动的带状物体, 运动速度为V L。为了保证成品玻璃板为矩形, 横切机的切刀必须同时具有纵向与横向两个方向的运动 (如图1 所示)。纵向运动使切刀与玻璃带保持运动同步, 即纵向运动速度V Z 与玻璃带运动速度V L 保持一致; 而横向运动则使切刀完成切割工作, 其运动速度为V H。
刀架运动要求；接到单片机控制信号后，落刀，由同步带带动沿横梁方向切割玻璃，抬刀，返回原落刀点。其中落刀刀口压力要控制在指定厚度的玻璃的承载范围之内。要保证其对玻璃的冲击不至于使玻璃损坏。
横梁的直线度不低于对玻璃的直线度的要求。
2）人机界面系统功能：
手动输入所要切割的玻璃的长度，切片数量，落刀位置，抬刀位置等参数，并可以通过键盘修改相关参数。键盘设置急停键，抬刀键、回车键，以便切割出现问题时手动处理。
2.2、实现方法
2.2.1机械结构方案
对综述中提到的几种横切机的结构的比较本次毕业设计我也决定采用斜置式速度控制式机械结构。机械结构简图如图6.结束语
本文设计了一种玻璃横切机的机械结构及其人机界面系统。首先详细了解了各种玻璃横切机的机械结构，及其相应的切割特征。最终选择了斜置式速度控制式的机械结构。对传动系统的主要部件的同步带、步进电动机、轴承、主传动轴进行了选型、计算、校核。绘制了机械结构装配图、支架、刀架、机箱、法兰盘、主传动轴的零件图。进行控制系统设计，绘制了电路原理图。人机界面系统设计，其硬件由矩阵键盘、RT12864M ST7920 汉字图形点阵液晶显示模块组成。实现功能为：液晶显示切割玻璃长度、切片数量、落刀位置、抬刀位置四项参数，并可以通过键盘修改这些参数。做了软件设计、程序编写包括LCD驱动程序、键盘监控程序、显示程序三大模块。画出了软件流程图，包括总流程图及各个子程序流程图，并对程序进行了详细的说明。大致完成了任务书的要求。
由于技术、时间有限存在很多不足之处，望老师批评指导。
参考文献
1.尹志强.机电一体化系统设计课程设计指导书.机械工业出版社.2007.7
2.胡汉才.单片机原理及其接口技术.清华大学出版社.2004.2
3.纪名刚.机械设计.高等教育出版社.2001.6
4.银尧城.简明实用机械手册.机械工业出版社.1987.6
5.王建华.机械制图与计算机绘图.国防工业出版社.2004.9
6. 龚振邦，等机器人机械设计【M】. 北京：电子工业出版社，1995
7. 殷际英编著光机电一体化理论基础. 化学工业出版社
8. 薛万鹏等译 C程序设计教程. 机械工业出版社.
9. 聂刚. 平板玻璃横切机的分类及类别特征. 《玻璃》,　2002 年　第 2 期　总第161 期
10. 杨清翔李文江.单片机在玻璃自动计数系统中的应用. 《辽宁工程技术大学学报》2005年4月第24卷增刊
11. 欧耀海.机电一体化全自动横切机. 《玻璃》　2007 年第 6 期总第 195 期
12. 刘克福李晓虹。基于单片机技术的全自动横切机研制。《微机算计信息》(嵌入式与SOC) 2008年第24卷第1－2期
13. 叶文才.自动玻璃切割机控制系统的设计
14. 张瑞张宇干谈军费晓勇.浮法玻璃横切机智能控制系统的开发. 中国建材装备
15. 孟正大　郝立　戴先中. 开放式玻璃自动切割机计算机控制系统. 电气传动　2003 年　第 3 期　

内容简介：: Zero-moment point trajectory modeling of a bipedwalking robot using an adaptive neuro-fuzzy systemD. Kim, S.-J. Seo and G.-T. ParkAbstract: A bipedal architecture is highly suitable for a robot built to work in human environmentssince such a robot will find avoiding obstacles a relatively easy task. However, the complex dynamics involved in the walking mechanism make the control of such a robot a challenging task.The zero-moment point (ZMP) trajectory in the robots foot is a signicant criterion for the robotsstability during walking. If the ZMP could be measured on-line then it becomes possible to createstable walking conditions for the robot and here also stably control the robot by using the measured ZMP, values. ZMP data is measured in real-time situations using a biped walking robot and this ZMP data is then modelled using an adaptive neuro-fuzzy system (ANFS). Natural walking motions on at level surfaces and up and down a 10 slope are measured. The modellingperformance of the ANFS is optimized by changing the membership functions and the consequentpart of the fuzzy rules. The excellent performance demonstrated by the ANFS means that it can not only be used to model robot movements but also to control actual robots.1 IntroductionThe bipedal structure is one of the most versatile setups for a walking robot. A biped, robot has almost the same movement mechanisms as a human and it able to operate in environments containing stairs, obstacles etc. However, the dynamics involved are highly nonlinear, complex and unstable. Thus, it is difcult to generate a human-like walking motion. The realisation of human-like walking robots is an area of considerable activity 14. In contrast to industrial robot manipulators, the interaction between a walking robot and the ground is complex. The concept of a zero-moment point (ZMP) 2 has been shown to be useful in the control of this interaction. The trajectory of the ZMP beneath the robot foot during a walk is after taken to be an indication of the stability of the walk 16. Using the ZMP we can synthesise the walking patterns of biped robots and demonstrate a walking motion with actual robots. Thus, the ZMP criterion dictates the dynamic stability of a biped robot. The ZMP represents the point at which the ground reaction force is taken to occur. The location of the ZMP can be calculated using a model of the robot. However, it is possible that there can be a large error between the actual ZMP value and the calculated value, due to deviations in the physical parameters between the mathematical model and the real machine. Thus, the actual ZMP should be measured especially if it is to be used in a to parameters a control method for stable walking.In this work actual ZMP data taken throughout the whole walking cycle are obtained from a practical biped waling robot. The robot will be tested both on a at oor and also on 10 slopes. An adaptive neuro-fuzzy system (ANFS) will be used to model the ZMP trajectory data thereby allowing its use to control a complex real biped walking robot.2 Biped walking robot2.1 Design of the biped walking robotWe have designed and implemented the biped walking robot shown in Fig. 1. The robot has 19 joints. The key dimensions of the robot are also shown in Fig. 1.The height and the total weight are about 380mm and 1700 g including batteries, respectively. The weight of the robot is minimised by using aluminium in its construction. Each joint is driven by a RC servomotor that consists of a DC motor, gears and a simple controller. Each of the RC servomotors is mounted in a linked structure. This structure ensures that the robot is stable (i.e. will not fall down easily) and gives the robot a human-like appearance. A block diagram of our robot system is shown in Fig. 2.Out robot is able to walk at a rate of one step (48mm) every 1.4 s on a at oor or an shallow slopes. The specications of the robot are listed in Table 1. The walkingmotions of the robot are shown in Figs. 36.- Figures 3 and 4 are show front and side views of the robot, respectively when the robot is on a at surface. Figure 5 is a snapshot of the robot walking down a slope whereas Fig. 6 is a snapshot of the robot walking up a slope.The locations of the joints during motion are shown in Fig. 7. The measured ZMP trajectory is obtained from ten-degree-of-freedom (DOF) data as shown in Fig. 7. Two degrees of freedom are assigned to the hips and ankles and one DOF to each knee. Using these joint angles, a cyclic walking pattern has been realised. Our robot is able to walk continuously without falling down. The joint angles in the four-step motion of our robot are summarised in the Appendix.2.2 ZMP measurement systemThe ZMP trajectory in a robot foot is a signicant criterion for the stability of the walk. In many studies, ZMP coordinates are computed using a model of the robot and information from the encoders on the joints. However, we employed a more direct approach which is to use data measured using sensors mounted on the robots feet.The distribution of the ground is reaction force beneath the robots foot is complicated. However, at any point P on the sole of the foot to the reaction can be represented by a force N and moment M, as shown in Fig. 8. The ZMP is simply the centre of the pressure of the foot on the ground, and the moment applied by the ground about this point is zero. In other words, the point P on the ground is the point at which the net moment of the inertial and gravity forces has no component along the axes parallel to the ground 1, 7.Figure 9 illustrates the used sensors and their placement on the sole of the robots foot. The type of force sensor used in our experiments is a FlexiForce A201 sensor 8. They are attached to the four corners of the plate that constitutes the sole of the foot. Sensor signals are digitised by an ADC board, with a sampling time of 10ms. Measurements are carried out in real time.The foot pressure is obtained by summing the force signals. Using the sensor data it is easy to calculate the actual ZMP values. The ZMPs in the local foot coordinate frame are computed using (1).Where each fi is the force at a sensor ri is the sensor position which is a vector. These are dened in Fig. 10. In the gure, O is the origin of the foot coordinate frame which is located at the lower-left-hand corner the left foot. Experimental results are shown in Figs. 1116. Figures 11, 13 and 15 show the x-coordinate and y-coordinate of the actual ZMP positions for the four-step motion of the robot walking on a at oor and also down and up a slope of 10 , respectively. Figures 12, 14 and 16 shown the ZMP trajectory of the one-step motion of the robot using the actual ZMP positions shown in Figs. 11, 13and 15. As shown in the trajectories, the ZMPs exist in a rectangular domain shown by a solid line. Thus, the positions of the ZMPs are with in the robots foot and hence the robot is stable.3 ZMP trajectory modellingIn many scientic problems an essential step towards their solution is to accomplish the modelling of the system under investigation. The important role of modelling is to establish empirical relationships between observed variables. The complex dynamics involved in making a robot walkmake the control of the robot control a challenging task. However, if the highly nonlinear and complex dynamics can be closely produced then this modelling can be used in the control of the robot. In addition, modelling, can even be used in robust intelligent control to minimise disturbances and noise.3.1 ANFSFuzzy modelling techniques have become an active research area in recent years because of their successful application to complex, ill-dened and uncertain systems in which conventional mathematical models fail to give satisfactory results 9. In this light we intend to use a system to model the ZMP trajectory.The fuzzy inference system is a popular computing framework that is based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. We will use the Sugeno fuzzy model in which since each rule has a crisp output, the overall output is obtained via a weighted average, thus avoiding the time-consuming process of defuzzication. When we consider fuzzy rules in the fuzzy model, the consequent part can be expressed by either a constant or a linear polynomial. The different forms of polynomials that can be used in the fuzzy system are summarised in Table 2. The modelling performance depends on the type of consequent polynomial used in the modelling. Moreover, we can exploit various forms of membership functions (MFs), such as triangular and Gaussian, for the fuzzy set in the premise part of the fuzzy rules. These are another factor that contributes to the exibility of the proposed approach.The types of the polynomial are as followsA block diagram of the modelling system is shown in Fig. 17. The proposed method is rst used to model and then control a practical biped walking robot.To obtain the fuzzy rules for the fuzzy modelling system we must notes that the nonlinear system to be identied is a biped walking robot with ten input variables and each input variables has two fuzzy sets, respectively. For the fuzzy model, the if-then rules are as follows:where Ai，Bi，, Ji in the premise part of the rules have linguistic values (such as small or big) associated with the input variable, x1,x2,x10; respectively. Fj (x1, x2, x10); is the constant, or rst-order consequent polynomial function for the jth rule. As depicted in Fig. 18, two types of MFs were examined. One is the triangular and the other is Gaussian.Figure 19 is an adaptive neuro-fuzzy inference system 10 architecture that is equivalent to the ten-input fuzzy model considered here, in which each input is assumed to have one of the twoMFs shown in Fig. 18. Nodes labelled P give the product of all the incoming signals and these labelled N calculate the ratio of a certain rules ring strength to the sum of all the rules ring strengths. Parameter variation in ANFIS is occured using either a gradient descent algorithm or a recursive least-squares estimation algorithm to adjust both the premise and consequent parameters iteratively. However, we do not use the complex hybrid learning algorithm but instead use the general least-squares estimation algorithm and only determine the coefcients in the consequent polynomial function.3.2 Simulation resultsApproximately models were constructed using the ANFS. Then accuracy was quantied in terms of there mean- squared error (MSE), values. The ANFS was applied to model the ZMP trajectory of a biped walking robot using data measured from out robot. The performance of the ANFS was optimised by warying the MF and consequent type in the fuzzy rule. The measured ZMP trajectory data from our robot (shown in Figs. 3241A in the Appendix) are used as the process parameters.When triangular and Gaussian MFs are used in the premise part and a constant in the consequent part then, the corresponding MSE values are listed in Table 3. We have platted our results in Figs. 2025. The generated ZMP positions from the ANFS are shown in Figs. 20, 22 and 24 for a at level oor, walking down a 10 slope and walking up a 10 slope, respectively. In Figs. 21, 23 and 25, we can see the corresponding ZMP trajectories which are generated from the ANFS.For simplicity, the process parameter of both knees can be ignored. As a result, we can reduce the dimension of the fuzzy rules and thereby lower the computational burden. In this case the simulation conditions of the ANFS and its corresponding MSE values are given in Table 4.From the Figures and Tables that present the simulation results, we can see that the generated ZMP trajectory from the fuzzy system is very similar to actual ZMP trajectory of measured for our walking robot shown in Figs. 1116. The demonstrated high performance ability of the ANFS, means that ANFS can be effectively used to model and control a practical biped walking robot.3.3 ComparisonsWe now compare the performance of ANFS with numerical methods including three types of statistical regression models. For each statistical regression model, four different case types were constructed. Their general forms in the case of two inputs are given as:where the ci are the regression coefcients.The corresponding MSE values are given in Tables 57 which reveals that type 2 gives the best results for the x and y coordinates for all the considered walking conditions. The generated ZMP positions and their corresponding trajectons generated using the type 2 regression model are shown in Figs. 2631. We can conclude that the ANFS demonstrated a considerably better ZMP trajectory than the statistical regression models.4 ConclusionsThe ANFS modelling at the ZMP trajectory of a practical biped walking robot has been presented. The trajectory of the ZMP is an important criterion for the balance of a IEE Proc.-Control Theory Appl., Vol. 152, No. 4, July 2005 walking robot but the complex dynamics involved make robot control difcult.We have attempted to establish empirical relationships between process parameters and to explain empirical laws by incorporating them into a biped walking robot. Actual ZMP data throughout the whole walking phase was obtained from a real biped walking robot both on a at level oor andon slopes. The applicability of the ANFS depends on the MF used and the consequent part of the fuzzy rule. The generated ZMP trajectory using ANFS closely matches the measured ZMP trajectory. Then simulation results also show that the ZMP generated using the ANFS can improvethe stability of the biped walking robot and therefore ANFS can be effectively used to not only to model but also control practical biped walking robots. Figs. 3241A5 AcknowledgmentsThis work was supported by grant no.R01-2005-000-11-44-0 from the Basic Research Program of the Korea Science & Engineering Foundation.6 References1 Erbatur, F., Okazaki, A., Obiya, K., Takahashi, T., and K

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