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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DSDP150 ABB Power supply controller
PM803F Robot power supply panel ABB
TK802F Contactor contact ABB
FI820F ABB Servo control unit
BCU-02 ABB Embedded controller
BCU-12 R8i INU control unit ABB
AIM0016 ABB Data acquisition unit
BIO0003 ABB Servo control unit
CPU0002 ABB Switch quantity input module
IEPAS02 ABB CPU processor
07KR51 220VDC ABB Fuse out device
PCD231B ABB Digital input module
IEPAS01 ABB Processor module
5SHX08F4502 ABB Expansion module
IMDS003 ABB Code reading module
5SGX1060H0003 ABB Thyristor (thyristor)
RMIO-12C ABB Control module
216EA62 ABB Network interface module
XO08R1-B4.0 ABB System card piece
72395-4-0399123 ABB Processor module
VA-3180-10 ABB Circuit board
VA-MC15-05 ABB PLC function module
UFC719AE01 ABB Analog input card
EL3040 ABB Gas analyzer
FS450R12KE3/AGDR-71C ABB Controller module
SC560 ABB Simulation module
83SR04C-E ABB Excitation system control panel
81EU01E-E ABB Card piece module
DSMB-02C Interface board of the ABB rectifier bridge
83SR06B-E ABB Robot motherboard
CP450-T-ETH ABB Control board card
086339-001 ABB DCS spare parts
216EA61b ABB Controller master unit
216AB61 ABB Decentralized control
216DB61 ABB Processor module
PPC380AE01 ABB PID controller module
YPP110A ABB Digital output module
YPK112A ABB Programming configuration
PE1315A ABB Communication template
CI547 ABB Distributed module
MB510 ABB Inverter drive board
1TGE120011R1001 ABB TR control panel
1SAP250100R0001 ABB Controller main unit
0-57406-E RELIANCE
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