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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DSDI110AV1 3BSE018295R1 ABB
3BSE018295R1 Analog output board ABB
DSDI110AV1 Analog output board ABB
57310001-LM Analog output board ABB
DSCS131 Analog output board ABB
DSCS131 57310001-LM ABB
DSCA114 57510001-AA ABB
57510001-AA Analog output board ABB
DSCA114 Analog output board ABB
3BSE019216R1 Analog output board ABB
DSBC176 Analog output board ABB
DSBC176 3BSE019216R1 ABB
DSBC172 57310001-KD ABB
57310001-KD Analog output board ABB
DSBC172 Analog output board ABB
DSBB175 Analog output board ABB
57330001-Y Analog output board ABB
DSBB110A Analog output board ABB
DSAO110 Analog output board ABB
3BSE018293R1 Analog output board ABB
DSAO120A Analog output board ABB
DSAO120A 3BSE018293R1 ABB
DSAI146 3BSE007949R1 ABB
3BSE007949R1 Analog output board ABB
DSAI146 Analog output board ABB
3BSE018290R1 Analog output board ABB
DSAI133A Analog output board ABB
DSAI133A 3BSE018290R1 ABB
DSAI130D 3BSE003127R1 ABB
3BSE003127R1 Analog output board ABB
DSAI130D Analog output board ABB
DSAI130 Analog output board ABB
DO801 3BSE020510R1 ABB
3BSE020510R1 Analog output board ABB
DO801 Analog output board ABB
DO630 Analog output board ABB
DO620 Analog output board ABB
DO610 Analog output board ABB
DLM02 Analog output board ABB
DLM01 Analog output board ABB
2RCA022748C Analog output board ABB
2RAA005802A0003G Analog output board
DIS0006 Analog output board ABB
DIS0006 2RCA022748C ABB
DIS0006 2RAA005802A0003G ABB
DIS0006 2RAA005802A0003G/2RCA022748C
DI93A HESG440355R3 ABB
HESG440355R3 Analog output board ABB
DI93A Analog output board ABB
DI814 Analog output board ABB
DI810 3BSE008508R1 ABB
3BSE008508R1 Analog output board ABB
DI810 Analog output board ABB
DI651 Analog output board ABB
DI610 Analog output board ABB
DI620 3BHT300002R1 ABB
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