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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2711-B6C1 PanelView Standard terminal
2410ML-05W-B50 Dc fan
2364-SPM03A Inverter RGU main control board
2301E-8273-1011 Load sharing control
2117-001-105 Self-supporting spinner
“2094-BM01-S AM 400 Volt Class”
2090-SCVP32-0 F-SMA Screw-Type Connector
1794-OE4 I/O Output Analog Module
1794-IE12 Analog Module
1794-AENTR Flex I/O Dual Port Ethernet/IP Adapter Module
1794-ACNR15 Redundant Media ControlNet Adapter
1788-DNBO Fiber Ring Repeater Module
1786-TPYR Fiber Ring Repeater Module
1785-TR10BT Allen-Bradley PLC 5
“1785T-PMPP-1700 Operator Interface Terminal”
1785-L80B PLC5 Processor
1785-L40C15 PLC5 series programmable logic controller
1785-L40B Programmable Logic Controller
1785-L30B Enhanced PLC-5 processor module
1784-SD1 ControlLogix Programmable controller
1784-PKTXD network interface cards
1783-US8T Stratix 2000 Ethernet unmanaged switch
1772-LG processor control module
1771-WA PLC 5 series field wiring arm
1771-P4R PLC 5 Redundant Power Supply Module
1771-OZL PLC-5 digital dry-reed relay contact output module
1771-OZ eight channel Normally Open contact output module
1771-OD16 Isolated AC Output Module
1771-OBN PLC 5 Digital DC Output Module
1771-OBD non-isolated output module
1771-OB DC Output Driver Module
1771-OAD PLC5 discrete output module
1771-NR 8-channel input module
1771-NC6 Communication module
1771-IVN Control system
1771-IQC Control system
1771-IAN Ethernet interface module
1770-XYC battery module
1770-FF Communication module
1769-OB16 Allen-Bradley MicroLogix 1200 Output module
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