Description
hardware flow control. It is an ideal choice in the field of industrial automation.
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;
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UFC911B106 3BHE037864R0106 System board card
UFC911B108 ABB Control module card key
UFC911B110 ABB Input control panel
UFC921A101 3BHE024855R0101 Interface module
UFD203A101 3BHE019361R0101 ABB Industrial Ethernet
UNITROL 1010 PLC control system
UNITROL 1020 ABB Input output module
UNITROL1000 Z.V3 3BHE014557R0003 ABB Numerical control module
UNS0007A-P V1 ABB DCS controller module
UNS0119A-P,V101 ABB Digital input submodule
UNS0874A ABB Control I/O module
UNS0887A-P 3BHE008128R0001 Analog input module
UNS2880b-P,V2 3BHE014967R0002 Analog output circuit board
UNS2881B-P V1 ABB Robot network communication card
UNS2882A 3BHE003855R0001 Connecting terminal unit
UNS2980c-ZV4 ABB Robot axis calculation board
UNS4881b,V4 3BHE009949R0004 ABB Analog output circuit board
UNS3670A-Z V2 HIEE205011R0002 Servo servo module
C3K122-24P ENTERASYS Controller module
C3K122-24 ENTERASYS Control of signal
C3G124-48 ENTERASYS Processor module
C3G124-24 ENTERASYS Processor module
ENTERASYS C3G124-24P Network interface module
08G20G4-48P 800-Series Layer 2 switch
08G20G4-48 800-Series Layer 2 switch
08G20G4-24P 800-Series Layer 2 switch
08G20G2-08P 800-Series Layer 2 switch
ENTERASYS 08H20G4-48 800-Series Layer 2 switch
08G20G2-08 800-Series Layer 2 switch ENTERASYS
08H20G4-48P 800-Series 10/100 Switches ENTERASYS
08H20G4-24P ENTERASYS switch
08H20G4-24 ENTERASYS 24 port 10/100 800-Series Layer 2 switch
B5K125-48 and B5K125-48P2 ENTERASYS gateway
B5K125-24 and B5K125-24P2 ENTERASYS Switch
B5G124-24 and B5G124-24P2 Ethernet Switch
ENTERASYS B5G124-24 Ethernet Switch
I3H252-8TX-2FX ENTERASYS High-end Secure Networks
I3H252-8FXM ENTERASYS
I3H252-6TX-MEM ENTERASYS Ethernet
ENTERASYS I3H252-4FX-MEM Ethernet L2 Switch
switch I3H252-4FXM ENTERASYS
I3H252-12TX ENTERASYS Ethernet switch
Switch A2H254-16 ENTERASYS
A2H124-24FX Switch ENTERASYS
ENTERASYS A2H124-48P Switch Ports
A2H124-48 Switch Ports ENTERASYS
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