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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PMF1216D63 SCHNEIDER
RXZE1M114M SCHNEIDER
STBAVO0200 SCHNEIDER
STBDAI7220 SCHNEIDER
STBDAO8210 SCHNEIDER
TCSESM043F2CS0 SCHNEIDER
TFTX11702 SCHNEIDER
TSX07301012 SCHNEIDER
TSX07301028 SCHNEIDER
TSX07311612 SCHNEIDER
TSX07311622 SCHNEIDER
TSX07311628 SCHNEIDER
TSX07311648 SCHNEIDER
TSX07312422 SCHNEIDER
TSX073L2028 SCHNEIDER
TSX08CD08R6AC SCHNEIDER
TSX08EAV8A2 SCHNEIDER
TSX08ED12R8 SCHNEIDER
TSX3721101 SCHNEIDER
TSXAEG4111 SCHNEIDER
TSXCUSBMBP SCHNEIDER
TSXMRPC007M SCHNEIDER
TSXMRPC007 SCHNEIDER
TSXMRPC007MC SCHNEIDER
TSXMRPF008M SCHNEIDER
TSXP57204M SCHNEIDER
TSXP572634 SCHNEIDER
TSXP57304M SCHNEIDER
TSXP573623 SCHNEIDER
TSXP575634M SCHNEIDER
TSXPBY100 SCHNEIDER
TSXSCP114 SCHNEIDER
VX5G48C32Q SCHNEIDER
XB2-EA142 SCHNEIDER
XBTF023110 SCHNEIDER
XBTF034610N SCHNEIDER
XBTFC044310 SCHNEIDER
XBTGK2120 SCHNEIDER
XBTMEM08 SCHNEIDER
XPS-AV11113 SCHNEIDER
810-099175-013 LAM
810-077433-001 LAM
810-064624-521 LAM
810-107813-107 LAM
810-002895-001 LAM
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810-800082-306 LAM
810-1314-003 LAM
810-099175-013 LAM
810-17003-002 LAM
810-057038-002 LAM
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