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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PFTL101BER acid resistant type pillow block tension meter horizontal load cell
PFTL101B horizontal load cell for pillow block tensiometer
PFTL101BE horizontal load cell for pillow block tension meter
PFTL101AER Acid resistant type pillow block tension meter horizontal load cell
IMHSS03 ABB Inverter Spare Parts
PFTL101A pillow block tension meter vertical load cell
PFTL101AE pillow block tension meter vertical load cell
PFCL201CD/CE pillow block tension meter vertical load cell
IMHSS03 ABB I/O module backplane
PFCL 201C pillow block tension meter vertical load cell
T9110 ICS TRIPLEX Processor Module
T9110 controller module
PM510V16 ABB PLC module
PM510V16 3BSE016240R1 DCS spare parts
5SHY4045L0006 3BHB030310R0001 igct module
CMA136 3DDE300416 ABB Servo Control Module
CMA136 3DDE300416 host controller board
PFTL101A 2.0KN 3BSE004172R1 ABB pillow sensor
3BSE004172R1 ABB load cell
DSMB-02C ABB sensor power supply
DSMB-02C 3AFE64666606 ABB ethernet adapter
SCYC55830 Ethernet I/O communication template
SCYC55830 58063282A DC Drive Power Board
3BSE003127R1 ABB driver module
DSAI130D 3BSE003127R1 TDI S100 I/O Module
PM861K01 Servo Control Module
PM861K01 3BSE018105R1 PID Controller Module
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3BSE050091R20 ABB Oscilloscope adapter
3BSE050091R20 PFEA112-20 ABB Communication control panel
TRICONEX 3481 Triple Modular Redundant Controller
3481 TRICONEX Analog interface board
S20360-SRS Electronic control unit
S20360-SRS KOLLMORGEN Control module spare parts
MSPC-6886/6800 MSPC Servo control panel
3BSE042238R1 ABB LCM liquid crystal module
PP846 ABB Governor motherboard
PP846 3BSE042238R1 ABB CNC system key panel
05701-A-0512 SIEGER Electronic components and components
60M100-00 BENTLY Main control panel
60M100-00 BENTLY Servo drive driver
PR6423/010-140 Servo drive driver
PR6423/010-140+CON021 Pulse encoder interface module
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