V. Writing.

Ex.1. Render the text in writing.

Текст під назвою „ Автоматизація. Автоматизоване виробництво ” описує перспективи розвитку автоматизації, надає характеристику головним видам роботи у виробництві. Текст висвітлює провідну ціль автоматизації. Особлива увага приділяється визначенню переваг та недоліків інтегрованого автоматизованого виробництва. У тексті розглянута послідовність етапів виробничого процесу, що призводить до створення повної інтеграції автоматизованих систем на виробничих підприємствах.

Ex.2. Write an abstract to the text, using the key-patterns for writing an abstract in English and Russian:

1. The article (paper, book, etc.) deals with... 1. Эта статья касается …

2. As the title implies the article describes... 2. Согласно названию, в статье описывается…

3. It is specially noted... 3. Особенно отмечается…

4. A mention should be made... 4. Упоминается…

5. It is spoken in detail... 5. Подробно описывается…

6. ...are noted. 6. Упоминаются…

7. It is reported... 7. сообщается

8. The text gives a valu­able information on... 8. текст дает ценную информацию...

9. Much attention is given to... 9. Большое внимание уделяется..

10. The article is of great help to... 10. Эта статья окажет большую помощь…

11. The article is of interest to… 11. Эта статья представляет интерес для...

12. It (the article) gives a detailed analysis of... 12. Она(статья) дает детальный анализ…

13. It draws our attention to... 13. Она привлекает наше внимание к

14. The difference between the terms... and.. 14. Следует подчеркнуть различие между

should be stressed терминами… и …

15. It should be stressed (emphasized) that… 15. Следует подчеркнуть, что…

16. ...is proposed. 16. Предлагается

17. ...are examined. 17. Проверяются(рассматриваются)…

18. ...are discussed… 18. Обсуждаются…

19. An option permits... 19. Выбор позволяет…

20. The method proposed... 20. Предлагаемый метод…

MACHINE VISION: THE EYES OF INTEGRATED MANUFACTURING

Machine vision encompasses a variety of techniques to gather data from the surrounding environment. Although most often thought of as cameras and image processors, ma­chine vision also includes technologies such as lasers as well as sonar and infrared sensors.

Most vision systems, however, consist of a camera, image processor and an operator interface. Images are gathered by the camera in a field made of small dots called pixels. (Each pixel represents the light at that point and is given a value as such. Early vision systems operated in a binary fashion, in which each pixel was reduced to a light/no light value.

Practical applications of vision systems demanded so- called gray scale images, in which each pixel is given a va­lue representing an increasing light intensity. Most commer­cial vision systems can discern 64 levels of light or gray scales, although some operate at 264 gray-scale levels to han­dle more demanding tasks.

Another important characteristic of vision systems is re­solution, the number of pixels that can be processed. Today's vision systems typically operate at 512 (horizontal) x 512 (vertical) pixels. Newer versions handle 1,024 x 1,024 reso­lution, which increase the field of vision by a factor of four over 512 x 512 systems. Such systems eliminate or reduce the need for multiple cameras to make vision more reliable as well as easier to install and maintain.

Many algorithms and techniques are needed to evaluate an image. This function generally determines the number of features the system can check and the time required to make such checks. Some vision systems evaluate images with sophisticated software, while others use unique¹ hardware to speed up the process.

Although vision systems can perform much more ”intelligent” part inspections than other type of equipment, a price must be paid and that is in programming. Telling the sys­tem exactly what to look for can be time-consuming. But sophisticated programming methods using computer graphics and menus ease this task greatly. Eventually, vision systems will be able to take part geometry, directly from CAD/CAM to further reduce programming time.

Machine vision systems are emerging as a vital element in factory automation, to help ensure quality and providing feedback to control production processes. Quality control applications are especially pushing the progress of machine vision. Although vision systems are fairly expensive, they j can provide an increase in quality that can quickly pay J for itself. These savings come from reduced scrap, less rework, and lower warranty costs.

Most of this quality control is being implemented as ins­pection systems, in which the vision device checks parts for qualities that are impossible with means other than human inspection. But unlike humans, vision systems do not get tired or sick and are more accurate, faster, or both. Moreover, vision systems provide a consistent method for performing (inspections, eliminating variations that may lead to rejection: of good parts or acceptance of bad parts.

Integrated manufacturing practically requires the benefits j of machine vision. Since vision systems are computer-based, they can be programmed and can communicate with other I intelligent devices and even higher-level computers such as CAD/CAM systems.

Another application for vision in the factory is robot gui­dance. The idea is to allow robots to «see» what they are doing, increasing productivity and flexibility of the device. Industry analysts debate the importance of vision for robotics, ho­wever. Some experts say the complexities and costs limit vision to a small percentage of installed robots.

Advocates of robot vision, however, seem to be winning the argument.

Vision systems must become easier to program if they are to see widespread use in the factory. Users cannot afford experts to custom program each system since a large factory may have dozens or even hundreds of vision systems.

Industry observers also point out that vision vendors must become more «factory oriented» if they are to meet the needs of integrated manufacturing. Many suppliers sprung from academia and therefore may not understand the harsh realities of the factory.

Although work is being done on three-dimensional robot guidance, practical applications in vision systems are simpler.