Гибкое решение для передовой переработки

Из-за широкого спектра применения, пластмасса представляется одним из главных компонентов твердых отходов. Пластмассу получают из нефтяных источников, и она не подвержена биохимическому разложению. Сталкиваясь с нехваткой мест для закапывания отходов и с обществом с экологическим сознанием, переработчики имеют возможность принять новые технологии для сокращения отходов и увеличения усилий по переработке. Преимущества, обеспечиваемые улучшенной деятельностью по переработке, таковы: сокращение отходов, уменьшение стоимости конечного продукта, сокращение потребления энергии, охрана окружающей среды и экономия нефти.

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Vocabulary / Словарь:

1. due to – благодаря, вследствие, в результате, из-за

2. a diverse array – широкий спектр

3. solid waste – твердые отходы

4. petroleum – нефть

5. non-biodegradable – не подвергаемый биохимическому разложению

6. landfill space shortage – нехватка места для закапывания отходов, мусора

7. environmentally – по отношению к окружающей среде

8. to adopt – принимать

9. recycling – переработка отходов, утилизация

10. reduction – снижение, понижение, сокращение, уменьшение, спад

11. cost decrease – снижение затрат

12. energy consumption – потребление затрат

13. waste/scraps – отходы

14. features – особенность, характерная черта; деталь, признак, свойство

15. commonly – обычно, обыкновенно, как правило, в большинстве случаев

16. through – через, сквозь, по, внутри

17. extrusion – экструзия, выдавливание, прессование

18. pellet – гранула, зерно

19. foil – фольга

20. venting – дренаж

21. heating – нагрев

22. screws – винт, шуруп

23. complicate – усложнять, затруднять

24. durable – длительный, долговременный, долговечный

25. relevant – подходящий, уместный, имеющий отношение к делу

Образец № 3

What happens when automation systems fail?

Kevin Ackerman,

11/16/2012

Что происходит, когда системы автоматизации отказывают?

Кэвин Акерман,

11/16/2012

Abstract

The article deals with the problem of automation systems failure. The example vision-guided robotic (VGR) bin is being considered. It is pointed out that there are significant challenges in implementing this system successfully. Much attention is given to the process VGR operating. In conclusion, it is said that the process stops and requires significant intervention and downtime.

What happens when automation systems fail?

What happens when it fails? This simple question is often overlooked when automation systems are designed and implemented. Asking this question can provide another dimension to a solution, often creating extra work for a system integrator in the short term, but definitely has long-term benefits. What really matters is how often it fails, and what happens when it does. A few examples follow of actual vision systems where considering this question was critically important.

Vision-guided robotic (VGR) bin picking is a unique challenge. The intention is that product in bins is removed by robots and loaded into machines, onto conveyors, etc. As easy as this might sound, there are significant challenges in implementing it successfully, mainly based on the structure of the bin and parts inside. VGR itself is a whole other topic (vision – 2D vs. 3D, bins – structured, layered, jumbled, and such), but no matter the technology, part presentation, and other factures, the success/failure rate «per pick» is a serious consideration.

This is best explained by example. Consider a bin that contains 100 parts. This is the «standard» bin, and whenever a bin is presented to a robot, it starts with 100 parts. Then consider the success rate on an individual part – this is the product of the vision success rate (How likely can parts be identified and located?) and the robot grip success rate (Once a part is located by vision, how likely is it that the part can be physically gripped?). In the example, if the vision success rate is 99.5% and grip success rate is 99.5%, then the per part success rate approximates 99% (99.5% x 99.5%).

That means for each part in the bin, the robot is 99% likely to pick it successfully. Sounds good, but consider that 99% over the 100 parts (an entire bin) – the «bin success rate». Basic statistics tells us that the bin success rate is (0.99)100 = 0.366 or 36%. Suddenly 99% isn’t so good. This means that for a typical bin, there is only a 36% chance it will be emptied without issue or, in other words, a 64% chance there will be a failure at some point in that bin.

So what happens when it fails? This is an important question in this example, because it appears that in 64% of bins there will be an issue. Is it a big deal? This is application specific – perhaps the process is fine, and it will result in a couple «leftover» parts in the bin. The other extreme is that the process stops and requires significant intervention and downtime.