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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IC693CPU350-BC GE Single slot CPU module
GFD563A101 3BHE046836R0101 ABB Excitation device control unit
2N3A3620-B TOSHIBA Assembly Controller Board
ABDT-PDP 3183203131 ANYBUS Decentralized control system
2N3A8130-A TOSHIBA module
C2RPS-PSM ENTERASYS power-supply module
CC-TCF901 HONEYWELL Hardware Firewall Termination Panel
CC-TDIL01 51308386-175 HONEYWELL Digital Input Module
CC-TDOB11 51308373-175 HONEYWELL Digital Output Module
IBA 940143201 ABB Analogue input board
IDPG 940128102 ABB Positive logic module
MTL MTL838B-MBF Analog Receiver RS485 Modbus
SC-UCMX01 51307198-175 HONEYWELL RTU2020 controller
PROFIBUS DP-V1 ABCC-DPV1 PROSOFT communication module
RMC-131D2 DEIF Current relay
UR6AH GE Universal Relay Module
T8300 ICS TRIPLEX Trusted Expander Chassis
TP857 3BSE030192R1 ABB TP857 Baseplate for BC810
1TGE120021R0110 ABB Communication Gateway
3HNM07686-1 ABB Controller module
140CHS11000 Schneider HOT STANDBY S911
123M4610 Bently Nevada A to B USB Cable 10 Foot
140CRA93100 Schneider QUANTUM RIO DROP ADAPTOR
140CPU11303 Schneider PROCESSOR 512K RAM 16K USER LOGIC 1XMB+
140DDI35300 Schneider DISCRETE INPUT MODULE
086318-002 ABB PARTS ON LINE
AAP3798102-00037 CAMERON UNIVERSDAL CONTROL MODULE
AX722F DC732F PM783F CM772F CP-E 24 1.25 ABB
DSQC355A ABB Robot spare parts
IC660BBA020 GE Combination I/O module
IC695CHS016 GE 16-Slot Universal Backplane
TB711FC1 3BDH000365R0001 CPU Terminal Base
TSX08CD08R6AC Schneider power-supply module
TTH300 ABB Head-mount temperature transmitter
UNDK30P3712S14 Baumer ULTRASONIC SENSOR
UNS3670A-Z,V2 HIEE205011R000 ABB Converter Electronics
140ACO13000 Schneider ANALOG OUTPUT MODULE
850040060P PROMESS Motion control card
531X305NTBAPG1 GE NTB/3TB Terminal Board
1797-IRT8 Allen-Bradley FLEX Ex RTD Thermocouple I/O Module
ILS1B853S1456 SCHNEIDER actuating motor
CC-TCNT01 HONEYWELL C300 Controller Input Output Termination Assembly
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