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
NEC G8NXAA5G
NEC 136-551733-B-02
NEC RSA-983/D
NEC FC-9821KE
NEC FC-E18M/CY1Z9ZA FC98-NX FC-E18M IPC
NEC FC-E18M/SX103Z
NEC FC-9821X MODEL1
NEC FC-9801F
NEC FC-9821X MODEL2
BESS100kW-21 5kWh
SAM ELECTRONICS LYNGSO MARINE 2200-PN271-148-556-G
SAM ELECTRONICS DAS-40M
SAM ELECTRONICS C6115-WAP401
SAM ELECTRONICS C6115-BAP401
ROCKSON GUARDIAN-V11
PHONTECH MPA-1603-94-03118-009-REV-2
PHONTECH MPA-1603-208-03118-009-REV-2
NESELCO BETONVEJ-10 DK-4000
NESELCO 601-D-2200
MSSA CMR-PWR-ALARM-PT8
MRT1624 16CHANNEL GENERAL PURPOSE ALARM PANEL
MARSEN LM-63434
LYNGSO MARINE UMS-2100-ACCOMMODATION
LYNGSO MARINE UMS-2100
KAMEWA ANA-DIG-PSR-I-OS
JRCS EXT-200-1-5755
INLTEH ITPWS
HANLA LEVEL INSTRUMENT TAU-LIDEC-12
EMERSON SF-CONTROL-LEVELDATIC-MDU-100S
DEIF AL8-2
DECKMA GMBH GAS-3000
CONSILIUM SALWICO GS4
BRAINCHILD ELECTRONIC CR06
AQUAMASTER RAUMA ATC3-A7033172
AQUAMASTER RAUMA ATC2-A7030004
AQUAMASTER RAUMA ATC-2A-A7033149.1
ABB Capacitance 1uf
ABB board type instrument STU PFSA103C
ABB board type instrument STU STU3BSE002488R1
ABB PFSA103C STU3BSE002488R1
ABB PFBL141B-75KN tension sensor
ABB tension amplifier 3BSE050090R65
ABB tension amplifier PFEA111-IP65
ABB PFEA111-IP65 3BSE050090R65
ABB tension amplifier PFEA112-IP20
ABB tension amplifier 3BSE050091R20
ABB PFEA112-IP20 3BSE050091R20
ABB tension amplifier PFEA113-IP65
ABB tension amplifier 3BSE028144R265
ABB PFEA113-IP65 3BSE028144R265
ABB matching unit PFVO143
ABB matching unit 3BSE023150R1
ABB PFVO143 3BSE023150R1
ABB matching unit PFVO142
ABB matching unit 3BSE023732R1
ABB PFVO142 3BSE023732R1
ABB pressure head control unit PFXA401F
ABB pressure head control unit 3BSE024388R3
Reviews
There are no reviews yet.