Description
1KHW002601R0001 Контроллер ABB
CC – Link и другие. Каждый слот IO может быть выбран автономно в соответствии с потребностями клиента, а один модуль поддерживает до 16 каналов.
Технологии основаны на инновациях1KHW002601R0001 Предоставление клиентам высококачественных и надежных продуктов всегда было постоянным стремлением к нулю.
Давайте посмотрим на его инновации и различия с предшественниками: с жидкокристаллическим дисплеем, вы можете увидеть параметры связи, состояние канала IO,
информацию о версии модуля и так далее; 1KHW002601R0001 Отладка и обслуживание более интуитивно понятны; ABS огнестойкая пластиковая оболочка, небольшой размер,
легкий вес, с использованием совершенно новой пряжки монтажной карты, установка более прочная и надежная.
0 Preface
Germany”s “Industry 4.0″ and the United States” “Industrial Internet” will
restructure the world”s industrial layout and economic structure, bringing different challenges and
opportunities to countries around the world. The State Council of China issued “Made in China 2025” as an action plan
for the first ten years of implementing the strategy of manufacturing a strong country, which will accelerate the integrated
development of IoT technology and manufacturing technology [1]. IoT collects data on machine operations, material usage
, facility logistics, etc., bringing transparency to operators. This transparency is brought about by the application of data analytics,
which refers to the use of statistical and machine learning methods to discover different data characteristics and patterns. Machine
learning technology is increasingly used in various manufacturing applications, such as predictive maintenance, test time reduction,
supply chain optimization, and process optimization, etc. [2-4]. The manufacturing process of enterprises has gradually developed from
the traditional “black box” model to the “multi-dimensional, transparent and ubiquitous perception” model [5].
1 Challenges facing manufacturing analysis
The goal of manufacturing analytics is to increase productivity by reducing costs without compromising quality:
(1) Reduce test time and calibration, including predicting test results and calibration parameters;
(2) Improve quality and reduce the cost of producing scrap (bad parts) by identifying the root causes of scrap and optimizing
the production line on its own;
(3) Reduce warranty costs, use quality testing and process data to predict field failures, and cross-value stream analysis;
(4) Increase throughput, benchmark across production lines and plants, improve first-pass rates, improve first-pass throughput,
and identify the cause of performance bottlenecks such as overall equipment effectiveness (OEE) or cycle time;
NI FP-TB-1 183893D-01
NI GPIB-140A 186135F-31
NI GPIB-140A 186135G-01
NI GPIB-140A 186135H-01L
NI GPIB-140A/2
NI NI-9505
NI PCI-5421
NI PCI-6224
NI PXI-1031
NI PXI-4071
NI PXI-4351 185450D-01
NI PXI-4461 186900T-11L
NI PXI-4462 188261H-11L
NI PXI-6527 185633D-01
NI PXI-6608 185745H-02
NI PXI-7340
NI PXI-8186
NI PXI-8423
NI PXI-8461
NI PXIE-8840QC
NI SBRIO-9607 783816-01
NI SCXI-1000
NI SCXI-1001
NI SCXI-1100
NI SCXI-1102B
NI SCXI-1104C
NI SCXI-1121
NI SCXI-1124
NI SCXI-1125
NI SCXI-1126
NI SCXI-1127
NI SCXI-1140
NI SCXI-1141
NI SCXI-1160
NI SCXI-1300
NI SCXI-1304
NI SCXI-1324
NI SCXI-1325
NI SCXI-1327
NI SCXI-1346
NI SCXI-1349
NI SCXI-1520
NI SCXI-1600
NI SH68-68-EP
140CPU53414BC SCHNEIDER
140CPU65150 SCHNEIDER
140CPU42402 SCHNEIDER
140CPU43412TSX SCHNEIDER
140CPU53414U SCHNEIDER
140CPU67861 SCHNEIDER
140CPU43412U SCHNEIDER
140CPU31110SV SCHNEIDER
140CPU31110 SCHNEIDER
140CPU53414A SCHNEIDER
140CPU53414B SCHNEIDER
140CPU67160S SCHNEIDER
140CPU53414 SCHNEIDER
140CPU21304 SCHNEIDER
140ACI04000 SCHNEIDER
140ACO02000 SCHNEIDER
140ACO13000 SCHNEIDER
140AVI03000 SCHNEIDER
140CHS11100 SCHNEIDER
140CPS11100 SCHNEIDER
SCHNEIDER 140CPS11410
140CPS11420 SCHNEIDER
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