Automation and modeling of the technological process

be economical;

have a small mass;

ensure easy matching with the load.

According to the type of power energy used, drives are distinguished: electric, pneumatic, hydraulic, mechanical, electromechanical, combined.

Pneumatic drives use the energy of compressed air with a pressure of about 0.4 MPa, obtained from the workshop pneumatic network through an air preparation device.

1.2.1 Technical specifications for device design

At the technical specification stage, the optimal structural and layout solution is determined and technical requirements for equipment are drawn up:

name and scope of application – device for installing electrical electronics on a printed circuit board;

the basis for development is the assignment for the CCP;

the purpose and purpose of the equipment is to increase the level of mechanization and automation of the technological operation;

sources of development - using the experience of introducing technological equipment in the industry;

technical requirements:

the number of mobility steps is at least 5;

maximum load capacity, N 2.2;

static force at the operating point of the equipment, N not more than 50;

MTBF, h, not less than 100;

absolute positioning error, mm +0.1;

movement speed with maximum load, m/s: - along a free trajectory no more than 1; - along a straight path no more than 0.5;

Calibrating the position of the manipulator links.

At the lower control level, the tasks of processing specified movements by the manipulator links, which are formed at the upper level, are solved. Program positions are worked out at specified parameters (speed, acceleration) using digital electromechanical modules that drive the manipulator links. The control system consists of the following devices: central processing unit (CPM); RAM; ROM; an analog input module (MAV), where signals from potentiometric coarse computational position sensors are supplied; serial interface module (SIM); input/output module (IOM); communication module (MC).

Information exchange between top-level modules is carried out using the system bus.

The lower level of management has:

Drive processor modules (MPM);

Drive control modules (MCM).

The number of MPP and MUP modules corresponds to the number of manipulator links and is equal to 6. The MPP is connected to the communication module using system highways. The electric motors of the manipulator links are controlled using transistor pulse-width converters (PWC), which are part of the power supply unit (PSU). The MCP is based on the K1801 microprocessor and has:

Single-chip processor;

Initial start register;

System RAM, capacity 3216 – bit words; system ROM, with a capacity of 2x16 bit words;

Resident ROM with a capacity of 4x16 bit words;

Programmable timer.

The performance of the MCP is characterized by the following data:

Summation with register addressing means – 2.0 µs;

Summation with mediocre register addressing means – 5.0 µs;

Fixed point multiplication – 65 µs.

The operator panel is designed to perform operations on and off the PR, to select its operating modes.

The main elements of the panel are:

mains power switch (NETWORK);

emergency shutdown button (.EMERGENCY). The mains power turns off when the button is pressed. The button is returned to its initial position by turning it clockwise;

control system power button (CK1);

control system power off button (CK0);

Drive power button (DRIVE 1). At the push of a button
the drive power is turned on, and at the same time the electromagnetic brakes of the motors are unlocked;

Drives power off button (DRIVE 0);

Mode selection switch. It has three positions ROBOT, STOP, RESTART. In ROBOT mode the system works normally. In STOP mode, program execution will stop at the end of the line step.

Moving the switch to ROBOT mode will continue the program execution to the beginning of the next step. RESTART mode is used to restart the execution of a user program from its first step;

Automatic start button (AUTOSTART). Pressing the button starts the system so that the robot begins executing the program without issuing commands from the keyboard. The button is pressed after the SC power is turned on. The mode is activated after turning on DRIVE 1.

The hand control panel is used to position the manipulator during teaching and programming. The remote control provides 5 operating modes:

computer control of the manipulator (COMP);

manual control in the main coordinate system (WORLD);

manual control of degrees of mobility (JOINT);

manual control in the tool coordinate system (TOOL);

Disabling mobility gauge drives (FREE).

The selected mode is identified by a signal light.

The speed of movement of the manipulator is adjusted using the “SPEED”, “+”, “-” buttons. To compress and decompress the manipulator’s gripping device, use the “CLOSE” and “OPEN” buttons.

Button " S TER" is used to record the coordinates of points when tasking the trajectory of movement. The "STOP" button, located at the end of the manual control panel, is intended to interrupt the execution of the program by turning off the power to the drives. It is used to stop movement in a normal situation. The "OFF" button has a similar purpose , like “STOP.” The difference is that the power to the manipulator drives is not turned off.

Moving the joints of the manipulator using the hand control panel is carried out in three modes: JOINT, WORLD and TOOL.

In mode JOINT (selected by the corresponding button on the control panel) the user can directly control the movement of individual links of the manipulator. This movement corresponds to pairs of buttons “-” and “+”, respectively, for each link of the manipulator (i.e. column, shoulder, elbow, and three grip movements).

In mode WORLD is actually fixed relative to the main coordinate system and moved in certain directions of this system (respectively X,Y,Z).

It should be noted that work in WORLD mode can be carried out at low speeds to prevent the robot from entering the robot's space within the hand boundary. We also point out that movement is provided automatically using all parts of the manipulator simultaneously.

LLP mode L provides movement in the active coordinate system.

The 12-bit line indicator is designed to display information about operating modes and errors:

-N OKIA AOX - is displayed for a short time at startup;

-ARM PWR OFF - power supply to the manipulator drives is turned off;

-MANUAL MODE - allowed to control the robot from the control panel;

SOMR MO D E - the manipulator is computer-controlled;

-L IMIT S TOR - the joint is moved to the extreme position;

LLP CLOSE - the specified point is very close to the manipulator;

LLP FAR - the given point is outside the robot's working area;

TEACH MOOE - TEACH mode is activated, the manipulator moves along arbitrary trajectories;

-S TEACH MOD E - TEACH-S mode is activated, the manipulator moves along straight trajectories;

-ERROR - buttons on the hand control panel are pressed simultaneously, which form an unacceptable operation, etc.

3 Technology and automation of electronic equipment production: Textbook for universities / Ed. A.P. Dostanko.-M.: Radio and Communications, 2009.

4 Computer production technology – Dostanko A.P. and others: Educational-Mn.: Higher School, 2004.

5 Technological equipment for the development of electronic accounting services: Head. Pos_bnik/M.S.Makurin.-Kharkiv: KhTURE, 1996.

Currently, in the conditions of market relations, the primary, fundamental tasks in the field of agricultural production are the intensification of existing production processes, improving product quality, saving materials and energy and, ultimately, increasing the energy efficiency of technological systems. Identification of production reserves or a specific process, as a rule, is associated with its analysis based on modern research methods and modern technical means (in particular, using the MATCAD software package). At the same time, special attention is paid to models of technological processes and methods of their construction.

Process modeling

When solving a number of problems related to the design, preparation and operation of technological processes, the agro-industrial complex resorts to their modeling, i.e., to the study of individual aspects, characteristics, and properties of technological processes not on a real object, but on its model. A model is understood as such a mentally represented or materially realized system, which, reflecting the object of research, is capable of reproducing its functions with varying accuracy and replacing it at a certain stage of the study.

Thus, a model is a certain system that preserves the essential properties of the original and allows the study of certain properties of the latter by physical or mathematical methods . In other words, a model is a representation, a description of a technological object (process or equipment) using some language, developed to achieve a specific goal. To date, a general theory of modeling complex systems has been developed, which indicates the possibility of using various types of models to describe technical and technological objects.

The model plays an active role in the study of TP: with its help, it is possible to determine various characteristics of TP, such as energy costs, consumption of raw materials and yield of the finished product, quality indicators of this product, amount of waste, defective products, design parameters of elements, with minimal cost and in a short time. equipment. You can outline and test an effective technology management strategy, perform an optimization procedure, etc.

The feasibility of TP modeling is determined by two main conditions:

Research on a model is cheaper, simpler, safer, faster than on the original object;

The rule for recalculating the characteristics and parameters of the model into the corresponding values ​​of the original is known, since otherwise the modeling loses its meaning.

The goal set when developing a model determines its type, information content and degree of correspondence to the real object, i.e. when formulating the goal, it is necessary to carefully select those essential properties that fully characterize the object in question, determine the required degree of correspondence of the model to the real object (model accuracy ). This allows, in some cases, to simplify the model, eliminate insignificant, insignificant relationships between quantities from consideration, and reduce modeling costs.

When describing technological processes, full-scale, physical and mathematical modeling are more often used.

Full-scale modeling involves conducting an experimental study of a real technological object and subsequent processing of the results using similarity theory, regression analysis, and correspondence tables. This makes it possible to obtain qualitative or quantitative dependencies that describe the functioning of the object with varying accuracy. However, empirical dependencies based on representing the process in the form of a “black box,” although they allow solving specific technological problems, have significant drawbacks:

Empirical dependencies cannot be extended to the entire possible range of changes in the regime parameters - they are valid only under the conditions and restrictions under which the full-scale experiment was carried out;

Such dependencies reflect past experience, so on their basis it is not always possible to identify and justify ways to improve the efficiency of relevant technologies.

In a number of cases, empirical dependencies are of a qualitative nature, that is, they establish only the nature of the influence of some quantities on others, without establishing quantitative patterns.

Physical modeling also involves conducting experimental studies with subsequent processing of the results. However, such studies are carried out not on a real technological object, but on special laboratory installations that preserve the nature of the phenomena and have a physical similarity. Thus, physical modeling is based on the similarity of processes of the same nature occurring in the original object and in the physical model, and consists of the following:

Establish the basic parameters of the technological process that are subject to numerical determination and characterize its quality;

One or several physical models are calculated and manufactured in the form of laboratory or semi-production (experimental, pilot) installations. The calculation of these settings is carried out on the basis of the theory of similarity, which guarantees the possibility of transferring the results to a real object;

As a result of the experiment on the model, numerical values ​​and relationships of the selected parameters are obtained and recalculated for the original.

With physical modeling, it is possible to obtain extensive information about the individual processes that determine the structure of a given technology.

Analog modeling is associated with the similarity of processes of different nature and is based on the fact that for various physical phenomena there are identical patterns of their description. Objects or processes described by equations of the same form are considered similar. As an example, we can cite the Fourier equations (8.2.6) and Fick equations (8.2.9). Despite the difference in the physical quantities included in them, all operators coincide and follow the same sequence. Consequently, by studying one process, we will obtain dependencies that are valid (up to notation) for another. For analog modeling, both experimental methods and analog computers are used.

Analytical modeling provides the most powerful tool for their study and involves obtaining and studying various mathematical models. Thus, structural models are used for a general or preliminary description of an object and make it possible to identify and define its elements, their properties and the relationships between the elements and the properties of the elements. Typically, the apparatus of set theory is used to construct a structural model. Classification models allow you to organize the objects under study, identify common features in them and rank them according to these features. Such models are necessary when building control automation systems, creating data banks and developing computer-aided design systems, information retrieval systems, and in a number of other cases. Cognitive models are used to quantitatively describe the patterns of various processes or the functioning of equipment. They establish relationships, relationships between quantities characterizing a process or laboratory equipment.

A cognitive model, as a rule, describes the physical and chemical mechanism of the process and may not contain technological parameters or characteristics of the object.

There are relationships between particular models that describe individual processes or other structural components of the object being studied. Taking such relationships into account, i.e., jointly solving equations that describe individual unit processes, leads to the construction of a generalized model of a method or processing method.

Technological models differ from cognitive models in that the purpose of their construction is to find quantitative relationships between mode parameters, operating conditions - inputs of a technological system and indicators of its technical level, i.e., system outputs. The construction of technological models is always associated with assessing the level of quality and increasing the efficiency of functioning of technological systems. Typically, technological models are built on the basis of mathematical models of individual processes or on the basis of a generalized model of an object. However, in some cases, a complete analytical description of an object is impossible, and when constructing technological models, some empirical dependencies are used. As a rule, technological models are built to study individual aspects of the functioning of a technological system, i.e. they are of a private nature.

For most technological processes, due to their complexity, the construction of a single generalized model that adequately describes all aspects and features of their occurrence is difficult or impossible. Therefore, when modeling TP, the principle of decomposition and solving local problems is used, which makes it possible to identify and model individual aspects and properties of TP. As a result of this approach, the TP is represented as a set of models that describe individual patterns of its functioning and are intended to solve a certain range of problems. This view follows naturally from the systems analysis described above. The hierarchy of technology gives rise to the hierarchy of models (models of TP, TO, TM), the multidimensionality of technologies - a variety of models (models of physical and chemical processes, technologies, equipment).

Example. As an example of the variety of models, consider the technology of electrochemical dimensional processing (ECM). The models used in the study and description of such technology are shown in Fig. 8.2.35.

Particular cognitive models in this case include the following:

kinematic (description of the kinematics of mutual movement of the electrodes);

hydraulic (description of fluid movement in a narrow interelectrode channel);

electrical (description of the electric field in the interelectrode gap);

thermal (description of the temperature field);

electrochemical (description of electrode processes and transfer processes in an electrochemical system);

chemical (description of the chemical stages of the total electrode process, chemical transformations of a substance in solution).

Technological models include the model of shaping (description of the movement of the anode boundary during electrochemical dissolution of its surface), the model of the electrode-tool, and a number of others.

Rice. 8.2.35. Types of models for describing processes of electrochemical processing of materials

Modeling is based on the basic concepts of the theory of similarity, according to which phenomena and processes are called similar if the data obtained from studying one of them can be extended to others. For such phenomena, the constancy of the ratios of certain quantities characterizing the process, or combinations of such quantities, called similarity criteria, is necessary [Table. P1,2,3]. For example, when studying the flow of liquid media, the Reynolds criterion is widely used:

,

Where v- fluid flow speed, m/s; d- hydraulic flow diameter, m; ν - kinematic viscosity of the medium, m 2 /s. The Reynolds number is a dimensionless quantity, the value of which determines the nature of fluid motion, the distribution of flow velocities over the channel cross-section and other flow parameters.

The main (third) similarity theorem states that for phenomena to be similar it is necessary and sufficient that their uniqueness conditions be similar. This means that geometric similarity, similarity of physical constants, initial and boundary conditions must be observed, and the similarity criteria, composed of quantities included in the conditions of uniqueness, would be the same. Consequently, all such phenomena differ from each other only in the scale of characteristic quantities. Thus, if phenomena or processes are similar, then the patterns obtained from studying one of them can be transferred to others, and the model results can be recalculated taking into account scale factors.

Summarizing the above, we can conclude that the main requirement for a model is that it corresponds to the object being modeled. The degree of correspondence of a model to the real phenomenon that it describes is called the adequacy of the model. Proving adequacy is one of the main stages of building any model. To quantify adequacy, the concept of “model accuracy” is used. Each model must be accompanied by information about its accuracy in order to reliably use the modeling results.

The accuracy of deterministic values ​​is determined by the deviation of the modeling result x* from the corresponding real value x, and the accuracy of stochastic models is assessed by probabilistic characteristics.

To ensure the adequacy of the model at the stage of its construction, the following rules are recommended:

choose a rational sequence for constructing the model;

use an iterative process of building a model, i.e. a multi-stage procedure for its development with assessment of intermediate results, analysis of their accuracy and correction of the model of the previous stage;

refine models based on available experimental data;

they refine the models based on obtaining expert assessments, the results of the operation of the object and other additional data.

The increasing complexity of technological processes in the agro-industrial complex, the increase in the number of parameters that are significant when constructing models, the tightening of modeling deadlines, the limitation of material resources allocated for these purposes - all these factors complicate, and in some cases exclude, subject-matter modeling. Therefore, mathematical modeling of TP using modern computer technologies comes to the fore.

Mathematical modeling of a TP is a study carried out by solving a system of mathematical relationships that describe a TP and has three stages:

drawing up a mathematical description of a process or its element;

choosing a method for solving a system of equations of mathematical description and implementing it in the form of an algorithm, program for obtaining quantitative quantities or relationships;

establishing the adequacy of the model to the original.

When constructing mathematical models, the real process is simplified, schematized, and the resulting scheme, depending on its complexity, is described by one or another mathematical apparatus. In a specific case, the mathematical description is presented in the form of a system of algebraic, differential, integral equations or a combination of them.

From the point of view of analyzing the mathematical model, it is advisable to highlight three aspects of it:

the semantic aspect reflects the physical description of the modeled object;

the analytical aspect is a system of equations that describe ongoing processes and the relationships between them;

computational - a solution method and algorithm implemented as a program in one of the programming languages.

Recently, for the study of complex systems, including technological processes, simulation modeling, which is based on machine experimentation, is increasingly used. To implement the mathematical model, a modeling algorithm is constructed that reproduces the process of system functioning over time. By changing the input data, information is obtained about the states of the process at given points in time, by which the characteristics of the object are assessed. Thus, in simulation modeling, we deal with models for which the result cannot be calculated or predicted in advance.

Example. Let us consider as an example the modeling of the process of electrochemical anodic processing of the material described earlier (Fig. 8.2.15, b). This technology has become widespread in the manufacture of spatially complex products in the energy sector, such as turbine and compressor blades. From a technological point of view, it is necessary to be able to calculate the time t required to remove a layer of metal of thickness z (machine processing time), or the amount of metal layer (allowance) zп removed during time t. To obtain the calculated dependencies, we will use a particular model of a plane-parallel interelectrode gap (IEG), the semantic aspect of which is clear from Fig. 8.2.36, a. As you can see, the electrode-tool (EI) moves translationally at a speed ve, and on the surface of the anode (A) a diagram of the local rates of electrochemical dissolution ve is formed, the interelectrode gap is filled with electrolyte, and a voltage U is applied between the electrodes.

Let's make some assumptions to simplify the model. Let the rate of electrochemical dissolution be the same for all points of the anode surface and the properties of the electrolyte also be the same for all points of the MEP. Then, to describe the process, you can use Ohm’s and Faraday’s laws:

where U is the voltage at the electrodes; i - current density; a - current interelectrode gap; χ - specific electrical conductivity of the electrolyte; c is the electrochemical equivalent of the metal; η is the current output of the metal dissolution reaction; ρ is the density of the metal being processed.

From the calculation scheme it follows that da/dt = ve - vи, since the dissolution of the surface is compensated by the displacement of the EI towards the workpiece. From here we obtain a differential equation describing the change in MEP over time:

(8.2.26)

under initial condition t= 0; a = a0.

The analysis of the model is greatly simplified if we take A = const. This assumption is correct for many practically important problems. Let's consider two cases implemented in most electrochemical shaping schemes: vi = 0 (case of stationary EI) and vi = const (movement of EI at a constant speed). Integrating the above differential equation, we obtain for the first case:

(8.2.27)

and for the second:

By transforming the obtained expressions, it is possible to obtain the dependence of time on the magnitude of the MEP.

Despite the simplified nature of the proposed model, it is successfully used in technological calculations and in many cases describes experimental data well.

However, in cases where the ratio of the length of the interelectrode gap to its width
is quite large (in real processes k reaches values ​​of 200–1000), the properties of the electrolyte along the length of the MET change greatly due to the accompanying release of heat and gas, and the assumptions made above are unacceptable.

It is necessary to build models that take into account the dependence of process parameters on the coordinates of the hydraulic path and time.

Physical modeling is widely used to obtain such dependencies. In Fig. 8.2.36, b shows a physical model of a long MEP, which makes it possible to obtain distributions of current density, electrolyte temperature, gas content, effective electrical conductivity of the interelectrode medium, local metal removal rate and other parameters along the length of the MEP by direct experiment.

Pump 1 pumps electrolyte through a hydraulic path formed by plane-parallel electrodes 2 and 3 built into dielectric plates 4. The size of the interelectrode gap is determined by the thickness of the replaceable gasket 5 and varies within 0.2-2 mm. Variable parameters of the electrolysis mode are: gap size, voltage on the electrodes, input pressure of the electrolyte, its composition, initial temperature, feed rate of the cathode to the anode, length of the electrolyte, electrode material. Gas release and the profile of electrolyte flow rates were studied using high-speed filming of the process; a sectional anode was used to obtain the distribution of local current densities along the length of the MEP; pressure and temperature distributions were recorded by pressure strain gauges and thermocouples; electrode potentials were measured in various sections of the MEP by special probes. The change in metal removal along the length of the channel was recorded by direct measurements.

The analysis shows that there is a correspondence between the presented physical model and the original: geometric, hydraulic, electrical similarity, similarity of physical constants, initial and boundary conditions are observed. Therefore, the experimental data obtained made it possible not only to refine the mathematical model, but also to obtain technological results suitable for direct use in production conditions.

Rice. 8.2.36. Scheme for constructing a mathematical model (a) and installation for physical modeling of the ECM process in a narrow long gap (b)

Thus, the above example shows that different types of models complement and clarify each other, collectively providing reliable data for practical use. To date, it is difficult to find areas in which there would be no developed apparatus for mathematical modeling of basic processes.

MINISTRY OF EDUCATION AND SCIENCE OF RUSSIA

Federal State Budgetary Educational Institution

higher education

NIZHNEVARTOVSK OIL TECHNIQUE (branch)

federal state budgetary educational institution

higher education

"Yugra State University"

MDK 04.01 “Theoretical foundations for the development and modeling of simple automation systems, taking into account the specifics of technological processes”

Guidelines for the course project

for students educational institutions

secondary vocational education

everyone forms of education (full-time, part-time)

by specialty 02/15/07. Automation of technological processes and production

Nizhnevartovsk 2016

Reviewed

At a meeting of the PCC ETD

Protocol No. 5 of May 24, 2016

Chairman of the PCC

M. B. Ten

I APPROVED

Deputy Director of HR

NNT (branch) FSBEI HE "YUGU"

R.I. Khaibulina

« » 2016

Complies with:

1. Federal State Standard (FSES) in the specialty 02/15/07. Automation of technological processes and production (by industry) approved on April 18, 2014 (order No. 349)

Developer:

Ten Marina Borisovna, highest qualification category, teacher of the Nizhnevartovsk Oil College (branch) of the Federal State Budgetary Educational Institution of Higher Education "Southern State University".

INTRODUCTION

Guidelines for the course project on MDK 04.01 “Theoretical foundations for the development and modeling of simple automation systems taking into account the specifics of technological processes” for full-time and part-time students are developed in accordance withrequirements of the Federal State Standard (FSES) for the specialty 02/15/07. Automation of technological processes and production (by industry), work program of the professional module PM 04Development and modeling of simple automation systems taking into account the specifics of technological processes

The course project has the goal of consolidating and systematizing students’ knowledge, developing skills in independent work and teaching them to practically apply the theoretical knowledge they have acquired when solving issues of a production and technical nature.

The didactic goals of course design are: teaching students professional skills; deepening, generalizing, systematizing and consolidating knowledge on MDC; formation of skills and abilities of independent mental work; comprehensive testing of mastery of professional and general competencies.

This manual aims to assist students in completing a course project on MDK 04.01 “Theoretical foundations for the development and modeling of simple automation systems, taking into account the specifics of technological processes”

The course project is carried out after studying the theoretical part of MDK 04.01 “Theoretical foundations for the development and modeling of simple automation systems, taking into account the specifics of technological processes”

The goal of the course project is to master methods for developing and modeling automatic control systems, plotting time and frequency characteristics and researching automatic control systems, as well as acquiring skills in using technical literature, reference books, and regulatory documents. Work on a course project helps to systematize, consolidate, deepen the knowledge acquired by students during theoretical training, and apply this knowledge to comprehensively solve assigned problems. As a result of completing the course project, students must master professional competencies:

PC 4.1 Analyze automatic control systems taking into account the specifics of technological processes.

PC 4.2 Select instruments and automation equipment taking into account the specifics of technological processes.

PC4.3 Draw up diagrams of specialized units, blocks, devices and automatic control systems.

PC 4.4 Calculate parameters of typical circuits and devices

The topic of the course project is selected in accordance with the place of practical training

2 STRUCTURE of the course project

The course project consists of two parts: an explanatory note and a graphic part.

Structure of the explanatory note:

title page;

list of sheets of the graphic part;

list of symbols and accepted abbreviations;

introduction;

Chapter 1;

chapter 2;

chapter 3;

conclusion;

bibliography;

applications.

The graphic part consists of two sheets of A1 format, while drawings and diagrams can be developed in A1 or A2 format; a specific set of graphic part is determined in an individual assignment and may include the following diagrams and drawings:

functional automation scheme;

external wiring diagram;

electrical circuit diagrams;

electrical connection diagrams;

block diagram of the controller.

3 CONTENT OF THE COURSE PROJECT

Introduction

Introductioncontains the following sections:

A.Relevance of the project topic(justification of the need to study issues related to the subject of research), for exampleThe relevance of creating automated control systems has increased significantly due toccosts of maintaining maintenance personnel and maintaining the environment;

b.An object -(a set of connections and relationships of properties that exists objectively in theory and practice and serves as a source of information necessary for the researcher). The object of research is determined by the phenomenon or process of objective reality to which the subject’s research activity is directed, for example, for the topic “Development of a systemautomation of ESP, SRP and AGZU wells on a well cluster”, the object will be a well cluster;

V.Itemresearch (more specific and includes only those connections and relationships that are subject to direct study in a given project, sets the boundaries of scientific research). In each object, several subjects of research can be identified, but the work must indicate one subject of research. The subject of the research is determined by the specific properties of the object, for example, for the topic “Development of a systemautomation of ESP, SRP and AGZU wells on a well cluster”, the subject will be ESP, SRP and AGZU wells;

Its purpose and objectives follow from the subject of the research.

G.Target (formulated briefly and extremely precisely, semantically expressing the main thing that the researcher intends to do).

Examples: 1.The goal of the project is to develop an automation system based on optimally suitable automation tools. Modeling of a stable and high-quality automatic control system

The goal specifies and develops in the research objectives.

The task should be formulated using an infinitive verb, for example: develop, analyze, identify, etc.

First task, as a rule, is associated with the identification, clarification, deepening, methodological justification of the essence, nature, structure of the object being studied. For example, analyze the purpose of objects and develop a block diagram of a well cluster

Second– with an analysis of the real state of the subject of research, dynamics, internal contradictions of development. For example, analyze the operating technology and main technical characteristics of the AGZU, determine automation parameters and operating conditions for automation equipment.

Third and fourth– with methods of transformation, modeling, verification, or with identifying ways and means of increasing the efficiency of improving the phenomenon or process under study, i.e. with practical aspects of work, with the problem of managing the object under study. For example, develop an automation scheme, determine methods of external connections of automation equipment, explore methods of installation, repair, verification of automation equipment, determine economic efficiency

Research methodsinclude the use of specific theoretical and empirical research methods, for example: analysis of scientific and methodological literature, documentary sources, etc.

Structure and scope of work(indicate which structural

elements the work consists of: introduction, number of chapters, paragraphs, conclusion, bibliography, indicating the number of titles, as well as the volume of work in pages, etc.).

The introduction is 2-3 pages long.

2 CHARACTERISTICS OF ELEMENTS OF THE AUTOMATIC CONTROL SYSTEM (ACS)

2.1 Technological characteristics of the regulated object

In this subsection of the course project, it is necessary to briefly outline the technology and main technological characteristics of the regulated object under consideration.

2.2 Mathematical model of the regulated object

It is necessary to draw the transition characteristic of the regulated object according to the option on a given scale.

Based on the type of transient characteristic, it is necessary to determine which typical dynamic links the control object corresponds to in terms of dynamic properties. Write down the transfer function of these links and determine the numerical values ​​of the coefficients from the graph.

For example:

Using the experimentally measured transient response (Figure 2.1), we determine the transfer function of the control object.

The control object corresponds to the serial connection of several aperiodic links and a delay link, therefore its transfer function

Рτ , (2.1)

To determine the numerical values ​​of the coefficientsK 1, T 1, τ 1 Using the graph, we find the steady-state value of the controlled parameterh mouth, h mouth = 14. Let's move to relative units, taking the valueh mouth for 1, divide the resulting segment into ten equal parts, mark the points a = 0.7,i=0.3. Let us determine from the graph the time corresponding to these pointst i=9.8 and t A =11.8. We accept the valuem=3.

Using Table 7.8, we determine the value of the constant coefficients T a *, A ia, IN ia, for a=0.7 and i=0.3 depending on degreemtransfer function

m = 3,

T 7 * = 0.277,

A 37 = 1.125,

B 37 = 1.889.

Determine the delay time of the regulated object

, (2.2)

Determine the time constant of the regulated object

(2.3)

T 1 = 0,277 (11,8 – 9,8) = 1,19

Determining the gain of the regulated object

input
(2.4)

Whereh mouth – steady value of the controlled variable.

Since we are given a transition characteristic, then X input =1, which means

K 1 = h mouth , (2.5)

K 1 =14

As a result, we obtain the OR transfer function in the form

-7.5r

2.3 Determining optimal controller settings

In accordance with the given control law (initial data), it is necessary to determine the transfer function of the automatic controller and calculate the settings.

For example:

According to the initial data, the regulation law is proportional.

The equation of the regulation law has the form:

y = Kε (2.6)

Wherey - output value;

K - gain;

ε – mismatch.

Let us write the regulation law in general form:

X out = K 2 X input (2.7)

Let us determine the transfer function of the automatic controllerW 2 (p)

X out (p) = K 2 X in (p)

W 2 (p) = K 2 (2.8)

We determine the controller settings using the VTI formulas (table 7.13):

Object characteristics:

(2.9)

We determine the limit of proportionality:

δ = 2 K 1 , (2.10)

δ = 2*14 =28

Determining the gain of the automatic regulatorK 2 :

(2.11)

As a result, we obtain the transfer function AR in the form

W 2 (p)=0,035

2.4 Mathematical model of the actuator and measuring transducer

AC electric motors are widely used as actuators in automatic control systems. In systems where speed control of the actuator is required, three-phase asynchronous electric motors with a wound rotor are used. If speed control is not required, then electric motors with a squirrel-cage rotor are used. Two-phase asynchronous motors are widely used as low-power actuators. The dynamic properties of asynchronous electric motors are determined by the differential equation

(2.12)

where T m – electromechanical time constant of the electric motor, s;

TO R – transmission coefficient of the electric motor;

U R – voltage on the rotor, V;

Q – angular velocity of the rotor, rad/s.

Electromechanical time constant T m depending on the inertia, the OR can be within T m =0.006÷2 s. In a course project, for example, we take T m =2s.

According to the initial data, for example, K R =4, thus the transfer function of the IM:

(2.13)

In terms of dynamic properties, the measuring transducer corresponds to the amplifier section. His equation:

X out = KX in (2.14)

Gain coefficient K=1, therefore the transfer function of the IP:

W 5 (p)=1 (2.15)

3 BLOCK DIAGRAM OF THE AUTOMATIC CONTROL SYSTEM

3.1 Process control

It is necessary to select the types of ACS elements, provide a description of their operating principle and technical characteristics. Describe the operation of the automatic control system.

3.2 Block diagram of an open-loop automatic control system for reference and disturbing influences

It is necessary to develop a block diagram of an automatic control system based on master and disturbing influences. Determine the transfer function of an open-loop system.

For example.

Figure 3.1 – Block diagram

We calculate the transfer function of series-connected elements

Transfer function of an open ACS according to the reference influence

(3.1)

Transfer function of an open ACS for disturbance influence

(3.2)

3.3 Block diagram of a closed-loop automatic control system based on reference and disturbing influences

Let us determine the transfer function of a closed-loop automatic control system based on the reference influence (Figure 3.1):

(3.3)

Let us determine the transfer function of a closed ACS based on the disturbing influence (Figure 3.1):

(3.4)

4 STABILITY OF THE AUTOMATIC CONTROL SYSTEM

4.1 Stability according to the Hurwitz criterion. Critical gain

According to the Hurwitz criterion, the system is stable if at a 0 >0 Hurwitz determinants are positive. Let the characteristic equation of the system under consideration

3.36r 4 +10.14r 3 +11.37r 2 +5.57r+2.17=0

Calculating Hurwitz determinants

Δ 1 =10.14

Conclusion: The system is stable.

We determine the boundary gain using the Hurwitz criterion.

We replace the gain factors with letter designations.

W 2 (p)= K 2

W 3 (p)= K 3

W 5 (p)= K 5

We calculate the transfer function of the ACS.

Thus, the characteristic equation of the system has the form:

K 2 K 1-5 =0

We'll make a replacement K 2 K 1-5 = K gr.

3.36 rub 4 +10.14 rub 3 +11.37 rub 2 +5.57 rub +1+ K gr =0

We compose the Hurwitz determinant:

The system is on the stability boundary if one of the Hurwitz determinants is equal to 0.

From the resulting expression we determineK gr.

642,17-102,81-102,81 K gr -104.24=0

102,81 K gr = -435.12

K gr =4.23

Thus the critical gain isK gr =4.23.

4.2 Stability according to the Mikhailov criterion. Critical gain

According to the Mikhailov criterion, the system is stable if the Mikhailov hodograph passes sequentially counterclockwisen-quarters of the complex plane when changing ω=0 ÷ +
. Let the characteristic equation of the system be:

3.36 rub 4 +10.14 rub 3 +11.37 rub 2 +5.57 rub +2.176=0

Mikhailov's polynomial:

Given the values ​​ω=0 ÷ +
We are building Mikhailov’s hodograph.

The calculation must be performed programmatically. For example usingEXEL. Let's create a program for this example.

B2=3.36*B1^4-11.37*B1^2+2.176

B3=-10.14*B1^3+5.57*B1

Table 4.1 – Calculation results

The hodograph must be constructed using a software environment.

Figure 4.1 – Mikhailov’s hodograph

Conclusion: the system is stable.

We determine the boundary coefficient using the Mikhailov criterion.

The characteristic equation for unknown gain factors has the form:

3.36 rub 4 +10.14 rub 3 +11.37 rub 2 +5.57 rub +1+ K gr =0

The Mikhailov polynomial is equal to:

F(jω)

The system is on the stability boundary if Mikhailov’s hodograph passes through the origin of coordinates at frequency ω≠0. Consequently, the system is on the stability boundary if the real and imaginary parts are equal to 0.

4.3 Stability according to the Nyquist criterion. Stability margin in amplitude and phase

In order for the system to be stable in closed form, it is necessary and sufficient that the hodograph of the AFC of the stable open-loop system does not cover a point on the complex plane with coordinates

(-1;0) when changing ω=0 ÷ +0. An open-loop system is considered stable if it consists of stable standard links.

Let the transfer function be an open-loop system.

We determine the AFC:

Setting meanings
we build the AFC of an open-loop system usingExcel:

Table 4.2 – Calculation results

Figure 4.3 – AFM hodograph

Conclusion: the system is stable

The stability margin in amplitude and phase is determined by the hodograph of the AFC of the open-loop system

Amplitude stability margin ΔA=0.74

Phase stability margin Δφ=130 0

5 QUALITY OF SAU

5.1 Transition graph

The transition process graph can be constructed using the trapezoidal method. To do this, it is necessary to determine the AFC of the closed-loop system, highlight the actual frequency response, and construct a DFC graph. Then perform the operations in the following sequence.

Let's consider constructing a graph of the transition process using an example.

We determine the AFC of a closed-loop system:

Building a DFC graph

Table 5.1 – Results of DFC calculation

We divide the DFC into trapezoids, so that two sides of each trapezoid are parallel to the ω axis, and the third coincides with the P axis.

Figure 5.1 – Actual frequency response

We determine for each trapezoid ω 0 , ω d , h 0.

For example, 1 trapezoid: ω 0 =0,54.

ω d =0 ,31

h 0 =45,5

We calculate the X value for each trapezoid:

Using the X value, we find the values ​​in the tableh x functions, given by the values ​​of τ, for each trapezoid.

Automation and modeling of the technological process

1 PROCESS AUTOMATION

Automation is a direction in the development of production, characterized by the liberation of a person not only from muscular efforts to perform certain movements, but also from the operational control of the mechanisms that perform these movements. Automation can be partial or complex.

Complex automation is characterized by the automatic execution of all functions to carry out the production process without direct human intervention in the operation of the equipment. A person's responsibilities include setting up a machine or group of machines, turning it on and monitoring it. Automation is the highest form of mechanization, but at the same time it is a new form of production, and not a simple replacement of manual labor with mechanical labor.

With the development of automation, industrial robots (IR) are increasingly used, replacing a person (or helping him) in areas with dangerous, unhealthy, difficult or monotonous working conditions.

An industrial robot is a reprogrammable automatic manipulator for industrial use. The characteristic features of PR are automatic control; the ability to quickly and relatively easily reprogram, the ability to perform labor actions.

It is especially important that PR can be used to perform work that cannot be mechanized or automated by traditional means. However, PR is just one of many possible means of automating and simplifying production processes. They create the prerequisites for the transition to a qualitatively new level of automation - the creation of automatic production systems that operate with minimal human intervention.

One of the main advantages of PR is the ability to quickly changeover to perform tasks that differ in the sequence and nature of manipulation actions. Therefore, the use of PR is most effective in conditions of frequent changes of production facilities, as well as for the automation of manual low-skilled labor. Equally important is to ensure rapid readjustment of automatic lines, as well as their assembly and commissioning in a short time.

Industrial robots make it possible to automate not only basic but also auxiliary operations, which explains the constantly growing interest in them.

The main prerequisites for expanding the use of PR are as follows:

increasing the quality of products and the volume of their output with a constant number of workers due to reducing the time required to complete operations and ensuring a constant “fatigue-free” mode, increasing the shift ratio of equipment, intensifying existing and stimulating the creation of new high-speed processes and equipment;

changing the working conditions of workers by freeing them from unskilled, monotonous, hard and hazardous work, improving safety conditions, reducing the loss of working time from industrial injuries and occupational diseases;

saving labor and freeing up workers to solve national economic problems.

1.1 Construction and calculation of the “rigid lead – printed circuit board hole” model circuit

An essential factor in the implementation of the assembly process is to ensure the assembleability of the electronic module. Assemblability depends in most cases on the accuracy of positioning and the effort required to assemble the structural elements of the module, and the design and technological parameters of the mating surfaces.

In the case where a rigid lead is inserted into the board hole, the following characteristic types of contact of the mating elements can be distinguished:

contactless output passage through the hole;

zero type contact when the end of the lead touches the chamfer of the hole;

contact of the first type, when the end of the lead touches the side surface of the hole;

contact of the second type, when the side surface of the lead touches the edge of the hole chamfer;

contact of the third type, when the end of the lead touches the side surface of the hole, and the lead surface touches the chamfer edge of the hole.

The following are accepted as classification criteria for identifying types of contact: change in the normal reaction at the point of contact; friction force; the shape of the elastic line of the rod.

The reliable operation of the setting head is significantly influenced by the tolerances of individual elements. In the processes of positioning and movement, a chain of tolerances arises, which in unfavorable cases can lead to an error when installing the ERE, leading to poor-quality assembly.

The assembleability of the product thus depends on three factors:

dimensional and accuracy parameters of the mating surfaces of product components;

dimensional and accuracy parameters of the mating surfaces of the base element of the product;

dimensional and precision positioning parameters of the executive body with the component located in it.

Let's consider the case of a zero-type contact, the diagram of which is shown in Figure 1.1.

M G

R G

R F l

Q

j

Figure 1.1 – Design diagram of a zero-type contact.

Initial data:

F – assembly force directed along the head;

f – friction coefficient;

Rg – reaction of the assembly head, perpendicular to its movement;

N – reaction normal to the chamfer generatrix;

Mg – bending moment relative to the assembly head;

1.2 Design of the gripping device

Gripping devices (GD) of industrial robots are used to grab and hold objects to be manipulated in a certain position. When designing gripping devices, the shape and properties of the object being grabbed, the conditions of the technological process and the features of the technological equipment used are taken into account, which determines the variety of existing gripping devices of the PR. The most important criteria when evaluating the choice of grippers are adaptability to the shape of the object being grasped, grip accuracy and grip strength.

In the classification of gripping devices of the charger, the characteristics that characterize the object of capture, the process of capturing and holding the object, the technological process being served, as well as the signs reflecting the structural and functional characteristics and design basis of the charger are selected as classification ones.

Factors associated with the gripping object include the shape of the object, its mass, mechanical properties, aspect ratio, physical and mechanical properties of the object's materials, and surface condition. The mass of the object determines the required gripping force, i.e. load capacity of the PR, and allows you to select the type of drive and design base of the charger; the state of the surface of the object determines the material of the jaws with which the memory must be equipped; the shape of the object and the ratio of its dimensions also influence the choice of charger design.

The properties of the object's material influence the choice of method for capturing the object, the required degree of sensing of the memory, the possibility of reorienting objects in the process of capturing and transporting them to the technological position. In particular, for an object with a high degree of surface roughness, but non-rigid mechanical properties, it is possible to use only a “soft” clamping element equipped with sensors for determining the clamping force.

The variety of memory devices suitable for solving similar problems, and the large number of features characterizing their various design and technological features, do not allow constructing a classification on a purely hierarchical principle. Gears are distinguished according to the principle of operation: grasping, supporting, holding, capable of relocating an object, centering, basing, fixing.

Based on the type of control, memory devices are divided into: uncontrolled, command, hard-coded, adaptive.

Based on the nature of attachment to the PR hand, all memories are divided into: non-replaceable, replaceable, quick-change, suitable for automatic change.

All gripping devices are driven by a special device - a drive.

A drive is a system (electrical, electromechanical, electropneumatic, etc.) designed to drive the actuators of automated technological and production machines.

Main drive functions: force (power, torque), speed (set of speeds, speed range); the ability to maintain a given speed (force, torque) under conditions of load changes; speed, design complexity; efficiency, cost, dimensions, weight.

Basic requirements for drives. The drive must:

1) comply with all the main characteristics of the given technical specifications;

2) allow electrical remote automatic control;

3) be economical;

4) have a small mass;

5) provide simple coordination with the load.

According to the type of power energy used, drives are distinguished: electric, pneumatic, hydraulic, mechanical, electromechanical, combined.

Pneumatic drives use the energy of compressed air with a pressure of about 0.4 MPa, obtained from the workshop pneumatic network through an air preparation device.

1.2.1 Technical specifications for device design

At the technical specification stage, the optimal structural and layout solution is determined and technical requirements for equipment are drawn up:

1) name and scope of application – device for installing electrical electronics on a printed circuit board;

2) the basis for development - the assignment for the CCP;

3) the purpose and purpose of the equipment is to increase the level of mechanization and automation of the technological operation;

4) sources of development - using the experience of introducing technological equipment in the industry;

5) technical requirements:

a) the number of mobility steps is at least 5;

b) maximum load capacity, N 2.2;

c) static force at the operating point of the equipment, N not more than 50;

d) time between failures, hours, not less than 100;

e) absolute positioning error, mm +0.1;

f) speed of movement with maximum load, m/s: - along a free trajectory no more than 1; - along a straight path no more than 0.5;

g) the working space without equipment is spherical with a radius of 0.92;

h) pneumatic drive of the gripping device;

6) safety requirements GOST 12.1.017-88;

7) payback period 1 year.

1.2.2 Description of the design and operating principle of the industrial robot RM-01

The industrial robot (IR) RM-01 is used to perform various operations of folding, installation, sorting, packaging, loading and unloading, arc welding, etc. The general view of the robot is shown in Figure 1.2.

Figure 1.2 – Industrial robot RM-01

The robot manipulator has six stages of mobility. The manipulator links are connected one to another using joints that imitate the human elbow or shoulder joint. Each link of the manipulator is driven by an individual DC electric motor through a gearbox.

The electric motors are equipped with electromagnetic brakes, which allows you to reliably brake the manipulator links when the power is turned off. This ensures the safety of servicing the robot, as well as the ability to move its parts manually. PR RM-01 has a position-contour control system, which is implemented by the SPHERE-36 microprocessor control system, built on a hierarchical principle.

"SPHERE-36" has two levels of control: upper and lower. At the top level the following tasks are solved:

Calculation of algorithms for planning the trajectory of movement of the manipulator gripper and preparation of motion programs for each of its links;

Logical processing of information about the state of the device that makes up the robotic complex, and agreement to work as part of the robotic complex;

Exchange of information with a higher-level computer;

Interactive mode of operation of the operator using a video terminal and keyboard;

Read-write, long-term storage of programs using float drive;

Manual mode of manipulator control using a hand control panel;

Diagnostics of the control system operation;

Calibrating the position of the manipulator links.

At the lower control level, the tasks of processing specified movements by the manipulator links, which are formed at the upper level, are solved. Program positions are worked out at specified parameters (speed, acceleration) using digital electromechanical modules that drive the manipulator links. The control system consists of the following devices: central processing unit (CPM); RAM; ROM; an analog input module (MAV), where signals from potentiometric coarse computational position sensors are supplied; serial interface module (SIM); input/output module (IOM); communication module (MC).

Information exchange between top-level modules is carried out using the system bus.

The lower level of management has:

Drive processor modules (MPM);

Drive control modules (MCM).

The number of MPP and MUP modules corresponds to the number of manipulator links and is equal to 6. The MPP is connected to the communication module using system highways. The electric motors of the manipulator links are controlled using transistor pulse-width converters (PWC), which are part of the power supply unit (PSU). The MCP is based on the K1801 microprocessor and has:

Single-chip processor;

Initial start register;

System RAM, capacity 3216 – bit words; system ROM, with a capacity of 2x16 bit words;

Resident ROM with a capacity of 4x16 bit words;

Programmable timer.

The performance of the MCP is characterized by the following data:

Summation with register addressing means – 2.0 µs;

Summation with mediocre register addressing means – 5.0 µs;

Fixed point multiplication – 65 µs.

The operator panel is designed to perform operations on and off the PR, to select its operating modes.

The main elements of the panel are:

Mains power switch (NETWORK);

Emergency shutdown button (EMERGENCY). The mains power turns off when the button is pressed. The button is returned to its initial position by turning it clockwise;

Control system power button (CK1);

Control system power off button (CK0);

Drive power button (DRIVE 1). At the push of a button
the drive power is turned on, and at the same time the electromagnetic brakes of the motors are unlocked;

Drives power off button (DRIVE 0);

Mode selection switch. It has three positions ROBOT, STOP, RESTART. In ROBOT mode the system works normally. In STOP mode, program execution will stop at the end of the line step.

Moving the switch to ROBOT mode will continue the program execution to the beginning of the next step. RESTART mode is used to restart the execution of a user program from its first step;

Automatic start button (AUTOSTART). Pressing the button starts the system so that the robot begins executing the program without issuing commands from the keyboard. The button is pressed after the SC power is turned on. The mode is activated after turning on DRIVE 1.

The hand control panel is used to position the manipulator during teaching and programming. The remote control provides 5 operating modes:

Computer control of the manipulator (COMP);

Manual control in the main coordinate system (WORLD);

Manual control of degrees of mobility (JOINT);

Manual control in the tool coordinate system (TOOL);

Disabling mobility measure drives (FREE).

The selected mode is identified by a signal light.

The speed of movement of the manipulator is adjusted using the “SPEED”, “+”, “-” buttons. To compress and decompress the manipulator’s gripping device, use the “CLOSE” and “OPEN” buttons.

The "STER" button is used to record the coordinates of points when specifying a movement path. The "STOP" button, located at the end of the manual control panel, is intended to interrupt the execution of the program by turning off the power to the drives. Used to stop movement in normal situations. The "OFF" button has the same purpose as the "STOP" button. The difference is that the power to the manipulator drives is not turned off.

Moving the joints of the manipulator using the hand control panel is carried out in three modes: JOINT, WORLD and TOOL.

In the JOINT mode (selected by the corresponding button on the control panel), the user can directly control the movement of individual links of the manipulator. This movement corresponds to pairs of buttons “-” and “+”, respectively, for each link of the manipulator (i.e. column, shoulder, elbow, and three grip movements).

In WORLD mode, the system is actually fixed relative to the main coordinate system and moved in certain directions of this system (X, Y, Z, respectively).

It should be noted that work in WORLD mode can be carried out at low speeds to prevent the robot from entering the robot's space within the hand boundary. We also point out that movement is provided automatically using all parts of the manipulator simultaneously.

TOOL mode provides movement in the active coordinate system.

The 12-bit line indicator is designed to display information about operating modes and errors:

NOKIA AOX - appears briefly upon startup;

ARMPWROFF - power to the manipulator drives is turned off;

MANUALMODE - allowed to control the robot from the control panel;

COMP MODE - the manipulator is computer-controlled;

LIMIT STOR - the joint is moved to the extreme position;

TOO CLOSE - the given point is very close to the manipulator;

FAR LLP - the specified point is outside the robot’s working area;

TEACH MOOE - TEACH mode is activated, the manipulator moves along arbitrary trajectories;

STEACH MODE - the TEACH-S mode is activated, the manipulator moves along straight trajectories;

ERROR - buttons on the hand control panel are pressed simultaneously, which form an unacceptable operation, etc.

In addition, the indicator of the selected speed with this encoding:

1 illuminated element - tool speed ≈ 1.9 mm/s;

2 illuminated element - tool speed ≈ 3.8 mm/s;

3 illuminated element - tool speed ≈ 7.5 mm/s;

4 illuminated element - tool speed ≈ 15.0 mm/s;

5 illuminated element - tool speed ≈ 30 mm/s;

6 illuminated element - tool speed ≈ 60 mm/s;

7 illuminated element - tool speed ≈ 120 mm/s;

8 illuminated element - tool speed ≈ 240 mm/s.

Below is an example of the PR RM-01 control program for drilling holes for surface mounting of ERE:

G04 File: SVETOR~1.BOT, Thu Dec 01 21:35:19 2006*

G04 Source: P-CAD 2000 PCB, Version 10.15.17, (C:\DOCUME~1\Shepherd\WORKERS~1\SVETOR~1.PCB)*

G04 Format: Gerber Format (RS-274-D), ASCII*

G04 Format Options: Absolute Positioning*

G04 Leading-Zero Suppression*

G04 Scale Factor 1:1*

G04 NO Circular Interpolation*

G04 Millimeter Units*

G04 Numeric Format: 4.4 (XXXX.XXXX)*

G04 G54 NOT Used for Aperture Change*

G04 File Options: Offset = (0.000mm,0.000mm)*

G04 Drill Symbol Size = 2.032mm*

G04 Pad/Via Holes*

G04 File Contents: Pads*

G04 No Designators*

G04 No Drill Symbols*

G04 Aperture Descriptions*

G04 D010 EL X0.254mm Y0.254mm H0.000mm 0.0deg (0.000mm,0.000mm) DR*

G04 "Ellipse X10.0mil Y10.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Draw"*

G04 D011 EL X0.050mm Y0.050mm H0.000mm 0.0deg (0.000mm,0.000mm) DR*

G04 "Ellipse X2.0mil Y2.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Draw"*

G04 D012 EL X0.100mm Y0.100mm H0.000mm 0.0deg (0.000mm,0.000mm) DR*

G04 "Ellipse X3.9mil Y3.9mil H0.0mil 0.0deg (0.0mil,0.0mil) Draw"*

G04 D013 EL X1.524mm Y1.524mm H0.000mm 0.0deg (0.000mm,0.000mm) FL*

G04 "Ellipse X60.0mil Y60.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Flash"*

G04 D014 EL X1.905mm Y1.905mm H0.000mm 0.0deg (0.000mm,0.000mm) FL*

G04 "Ellipse X75.0mil Y75.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Flash"*

G04 D015 SQ X1.524mm Y1.524mm H0.000mm 0.0deg (0.000mm,0.000mm) FL*

G04 "Rectangle X60.0mil Y60.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Flash"*

G04 D016 SQ X1.905mm Y1.905mm H0.000mm 0.0deg (0.000mm,0.000mm) FL*

G04 "Rectangle X75.0mil Y75.0mil H0.0mil 0.0deg (0.0mil,0.0mil) Flash"*

After drilling holes in the PCB, the robot installs the ERE. After installing the ERE, the board is sent for wave soldering.

2 MODELING OF THE TECHNOLOGICAL PROCESS

Modeling is a method for studying complex systems, based on the fact that the system under consideration is replaced by a model and the model is studied in order to obtain information about the system being studied. A model of the system under study is understood as some other system that behaves from the point of view of the research objectives in a manner similar to the behavior of the system. Typically, a model is simpler and more accessible to study than a system, which makes it easier to study. Among the various types of modeling used to study complex systems, simulation modeling plays a large role.

Simulation modeling is a powerful engineering method for studying complex systems, used in cases where other methods are ineffective. A simulation model is a system that displays the structure and functioning of the original object in the form of an algorithm that connects input and output variables accepted as characteristics of the object under study. Simulation models are implemented in software using various languages. One of the most common languages ​​specifically designed for building simulation models is GPSS.

The GPSS (GeneralPurposeSystemSimulator) system is designed for writing simulation models of systems with discrete events. The GPSS system most conveniently describes models of queuing systems, which are characterized by relatively simple rules for the functioning of their constituent elements.

In GPSS, the system being modeled is represented by a set of abstract elements called objects. Each object belongs to one of the object types.

Each object type is characterized by a specific behavior and set of attributes defined by the object type. For example, if we consider the work of a port, loading and unloading arriving ships, and the work of a cashier in a movie theater, issuing tickets to patrons, we will notice great similarities in their functioning. In both cases, there are objects that are constantly present in the system (the port and the cashier) that process objects entering the system (ships and movie theater patrons). In queuing theory, these objects are called devices and requests. When processing of an incoming object ends, it leaves the system. If at the time of receipt of the request the service device is busy, then the request is placed in a queue, where it waits until the service device becomes free. A queue can also be thought of as an object whose function is to store other objects.

Each object can be characterized by a number of attributes that reflect its properties. For example, a service device has a certain productivity, expressed by the number of requests it processes per unit time. The application itself can have attributes that take into account the time it spent in the system, the time it waited in the queue, etc. A characteristic attribute of a queue is its current length, by observing which during operation of the system (or its simulation model), one can determine its average length during operation (or simulation). The GPSS language defines object classes with which you can define service devices, customer flows, queues, etc., as well as set specific attribute values ​​for them.

Dynamic objects, called transactions in GPSS, are used to specify service requests. Transactions can be generated during the simulation and destroyed (leave the system). The creation and destruction of transactions is performed by special objects (blocks) GENERATE and TERMINATE.

Messages (transactions) are dynamic GPSS/PC objects. They are created at specific points in the model, advanced through blocks by the interpreter, and then destroyed. Messages are analogous to thread units in a real system. Messages can represent different elements even within the same system.

Messages move from block to block in the same way as the elements they represent (programs in the computer example) move.

Each promotion is considered an event that must occur at a specific point in time. The GPSS/PC interpreter automatically determines when events occur. In cases where an event cannot occur, although the time for its occurrence has approached (for example, when trying to occupy a device when it is already occupied), the message stops moving until the blocking condition is removed.

Once the system has been described in terms of the operations it performs, it must be described in GPSS/PC language using blocks that perform the corresponding operations in the model.

The user can define special points in the model at which statistics about queues need to be collected. Then the GPSS/PC interpreter will automatically collect statistics about queues (queue length, average time spent in queue, etc.). The number of delayed messages and the duration of these delays are determined only at these given points. The interpreter also automatically counts the total number of messages arriving at the queue at these points. This is done in much the same way as for devices and memories. Certain counters count the number of messages delayed in each queue, since the number of messages that pass any point in the model without delay may be of interest. The interpreter calculates the average time a message spends in the queue (for each queue), as well as the maximum number of messages in the queue.

2.1 Development of a block diagram and modeling algorithm

To model queuing systems, a general-purpose modeling system – GPSS – is used. This is necessary due to the fact that in the practice of research and design of complex systems, there are often systems that need to process a large flow of requests passing through servicing devices.

Models based on GPSS consist of a small number of operators, due to which they become compact and, accordingly, widespread. This is because GPSS has built-in the maximum possible number of logic programs required for modeling systems. It also includes special tools for describing the dynamic behavior of time-varying systems, with changes in state occurring at discrete moments in time. GPSS is very easy to program because the GPSS interpreter performs many functions automatically. Many other useful elements are included in the language. For example, GPSS maintains a simulation time timer, schedules events to occur later in the simulation time, causes them to occur on time, and manages the order of arrival.

To develop a block diagram, we will analyze the technological process of assembling the module being developed.

This technological process is characterized by sequential execution of technological operations. Therefore, the block diagram will look like a chain of sequentially connected blocks, each of which corresponds to its own technological operation and each of which lasts a certain time. The connecting links of these blocks are the queues formed as a result of each technological operation, and are explained by the different execution times of each of them. This block diagram is based on the design diagram for the assembly process of the designed module (Fig. 1.2) and is presented in Fig. 2.1.

Figure 2.1 – Block diagram of the technological process

In accordance with this scheme, we will create an algorithm for the model.

This algorithm contains the following blocks:

	– creates transactions at certain time intervals;
	– occupying the queue with a transaction;
	– clearing the queue;
	– device occupation;
	– release of the device;
	– delay in processing transactions.

All blocks are written from the first position of the line, first comes the block name, and then, separated by commas, the parameters. There should be no spaces in the parameter entry. If some parameter is missing in the block (set by default), then the comma corresponding to it remains (if it is not the last parameter). If there is a * symbol in the first position of a line, then this line is a comment.

Let's describe the parameters of some blocks:

A). GENERATE A,B,C,D,E,F

Creates transactions at specified time intervals.

A – average time interval between transactions occurrences.

B – 1) if a number, then this is half the field in which the value of the interval between the occurrences of transactions is evenly distributed;

2) if it is a function, then to determine the interval the value of A is multiplied by the value of the function.

C is the moment in time when the first transaction appears.

D – maximum number of transactions.

E – transaction priority value.

F – the number of parameters for the transaction and their type (PB-byte integer, PH-half-word integer, PF-full-word integer, PL-floating point).

b). TERMINATE A

Destroys transactions from the model and decreases the completion counter by A units. The model will terminate if the completion counter becomes less than or equal to zero. If parameter A is missing, then the block simply destroys transactions.

If the device named A is free, then the transaction occupies it (puts it into the “busy” state); if not, then it is queued to it. The device name can be a numeric number or a sequence of 3 to 5 characters.

The transaction releases the device named A, i.e. switches it to the "free" state.

d). ADVANCE A,B

Delays the processing of a transaction by this process and schedules the start time for the next stage of processing.

A is the average delay time.

B - has the same meaning as for GENERATE.

Collects statistics about the entry of a transaction into a queue named A.

Collects statistics about the exit of a transaction from the queue named A.

2.2 Development of a program for modeling a technological process using the GPSS language.

Now the task of modeling is to create a machine model on a computer, which will allow us to study the behavior of the system during the simulation time. In other words, you need to implement the constructed block diagram on a computer using blocks and operators of the GPSS language.

Since the operation of the model is associated with the sequential occurrence of events, it is quite natural to use the concept of “Model Time Timer” as one of the elements of the system model. To do this, introduce a special variable and use it to record the current operating time of the model.

When a simulation begins, the simulation timer is usually set to zero. The developer himself decides what value of real time to take as the reference point. For example, the starting point may correspond to 8 a.m. of the first simulated day. The developer must also decide on the choice of the size of the time unit. The time unit can be 1 s, 5 s, 1 min, 20 min, or 1 h. Once a time unit is selected, all time values ​​produced by the simulation or included in the model must be expressed in terms of that unit. In practice, the values ​​of the model time should be quite small compared to the real time intervals occurring in the simulated system. In this system, the time unit usually chosen is 1 minute.

If, when modeling a certain system at the current value of the model time, its state has changed, then you need to increase the timer value. To determine by what amount the timer value should be increased, use one of two methods:

1. The concept of a fixed increment of timer values.

With this approach, the timer value is increased by exactly one unit of time.

Then you need to check the system states and determine those scheduled events that should occur at the new timer value. If there are any, then it is necessary to perform operations that implement the corresponding events, change the timer value again by one unit of time, etc. If the check shows that no events are scheduled for the new timer value, then the timer will move directly to the next value.

2.The concept of variable increment of timer values.

In this case, the condition that causes the timer to increment is the arrival of a "nearby event" time. A near event is an event that is scheduled to occur at a time equal to the next closest value of the model time timer. The fluctuation of the timer increment from case to case explains the expression "variable time increment".

Usually, after a certain point in time, it becomes necessary to stop modeling. For example, it is necessary to prevent new requests from entering the system, but maintenance must continue until the system is released. One way is to introduce a major pseudo-event into the model, called "simulation termination". Then one of the functions of the model will be planning for this event. The moment in time, the occurrence of which should cause the simulation to stop, is usually specified as a number. That is, during the modeling process, you need to check whether the “simulation completion” event is the next event. If “yes,” then the timer is set to the end of the simulation, and control is transferred to the procedure that handles the completion of the simulation.

The initial data for developing the program are the time intervals at which the electronic electrical energy is received on the first block, the processing time on each block and the simulation time during which it is necessary to study the behavior of the system. The developed program is presented below.

generate 693.34.65

advance 99.6,4.98

advance 450,22.5

advance 248.4,12.42

advance 225,11.25

advance 248.4,12.42

advance 49.8,2.49

The result of the program is presented in Appendix A.

From the results obtained we see that 6 products will be manufactured in one work shift. At the same time, a queue is not created at any of the sites, but at the same time, at five sites the technological process of manufacturing the device has not been completed. The obtained values ​​of the equipment load factor and processing time at each site during modeling with minor deviations correspond to those calculated in the technological part of this diploma project.

Summing up, we conclude that the technological process was developed correctly.

CONCLUSIONS

During the thesis project, the design of a low-frequency amplifier was developed. At the same time, all the requirements of the technical specifications and relevant regulatory documents were taken into account.

In the first section of the diploma project, the initial data were analyzed, the type of production, the stage of development of technological documentation, and the type of technological process for organizing production were selected.

We chose a standard technological process, on the basis of which we formed a TP for the PCB assembly.

In the second section of the CP, a diagram of the “rigid terminal - printed circuit board hole” model was calculated and constructed. A gripping device has been developed.

In the third section, a block diagram and modeling algorithm were developed, on the basis of which the technological process of manufacturing the device was modeled using the GPSS language.

LIST OF LINKS

1 GOST 3.1102-81 “Stages of development and types of documents.”

2 GOST 3.1109-82 “Terms and definitions of basic concepts.”

3 Technology and automation of electronic equipment production: Textbook for universities / Ed. A.P. Dostanko.-M.: Radio and Communications, 2009.

4 Computer production technology – Dostanko A.P. and others: Educational-Mn.: Higher School, 2004.

5 Technological equipment for the development of electronic accounting services: Head. Pos_bnik/M.S.Makurin.-Kharkiv: KhTURE, 1996.