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;
https://www.xmamazon.com
https://www.xmamazon.com
https://www.plcdcs.com/
www.module-plc.com/
https://www.ymgk.com
YOKOGAWA CP134E-64
YOKOGAWA CP132E-32
YOKOGAWA PS63A
YOKOGAWA CP99AA
YOKOGAWA FC11A
YOKOGAWA PW504
YOKOGAWA CP313D
YOKOGAWA PS501
YOKOGAWA CP334D
YOKOGAWA PS35A
YOKOGAWA ADM12
YOKOGAWA PM1C
YOKOGAWA DV91A
YOKOGAWA RB401
YOKOGAWA ES1B
YOKOGAWA FC2A
YOKOGAWA PS40B
YOKOGAWA ES1C
YOKOGAWA PS33A
YOKOGAWA EP1-A
YOKOGAWA CPL-6
YOKOGAWA AAM21
YOKOGAWA PW402 S2
YOKOGAWA 2302-32-VLE-2
YOKOGAWA 230311
YOKOGAWA 8596020000
YOKOGAWA 8662570000
YOKOGAWA 8662560000
YOKOGAWA AIP121-S00
YOKOGAWA AIP171
YOKOGAWA AIP578
YOKOGAWA AIP591
YOKOGAWA ALR121-S00
YOKOGAWA AMM42
YOKOGAWA ANR10D
YOKOGAWA ATK4A-00
YOKOGAWA AVR10D-Q22020
YOKOGAWA CP345
YOKOGAWA CP401-10 S1
YOKOGAWA CP451-10
YOKOGAWA CP451-50
YOKOGAWA CP451-51
YOKOGAWA CP461-50
YOKOGAWA DR1030B60
YOKOGAWA EB501
YOKOGAWA F3BU06-0N
YOKOGAWA F3LC21-1N
YOKOGAWA F3NC01-0N
YOKOGAWA F3NC02-0N
YOKOGAWA F3PU06-0N
ABB REF620E_F
ABB REF620E_F NBFNAAAANDA1BNN1XF
YOKOGAWA F3PU10-0N
YOKOGAWA F3SP21-0N
YOKOGAWA F3WD64-3N
YOKOGAWA F3XD64-3N
YOKOGAWA F3YD64-1A
YOKOGAWA LR 4220E
YOKOGAWA NFAI143-H00
YOKOGAWA PSCAMAAN A5E00239363/04
YOKOGAWA PSCAMAAN16404-500/3
Reviews
There are no reviews yet.