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;
PCI-6251 | NI | Multifunction I/O Module
PC834-001-T | Kollmorgen | Brushless Servo Drive
P0924DB | FOXBORO | terminal base
P0917MG | FOXBORO | Terminal block Module
P0916PW | FOXBORO | Assembly 32 channel contact sensing
PO916NJ-OB | FOXBORO | DIN Mount Base
P0914XA | FOXBORO | terminal
VP325 02X | Concurrent Technologies | Processor Single Board Computer
XV-440-10TVB-1-20 | ETON | Touch panel
XVS-440-10MPI-1-10 | ETON | Touch panel
P0904HA | FOXBORO | Power Supply Module
SQ-300I | B&W | Hybrid automatic voltage control
P0400VP-0N | FOXBORO | Communication Processor
P30B04010PCKST | SANYO DENKI | SERVO MOTOR
SM-100-40-080-P0-45-S1-B0 | ELAU | Servo motor
MDB-8E | Sartorius | Weight bearing sensor
MC-TAMR04 | Honeywell | Low Level Analog Input Multiplexer
KJ3002X1-BF1 | Emerson | RTD Card
K2-400 DI470A | KEBA | Input Card
KEMRO K2-400 CP 450C | KEBA | PLC LCD monitor Liquid Crystal
INFO-4KP-94161B | INDEL AG | Communication module
IC200PWR101E | GE | VersaMax power supply module
HP-5517B | Agilent | Laser interferometer
IC693CPU364 | GE | single-slot Central Processing Unit
H201TI | GE | small on-line early warning transmitter
FBM232 P0926GW | FOXBORO | Ethernet Communication Module
FBM217 P0914TR | FOXBORO | Input Module
H92A0K9V0H00 | FOXBORO | Electrical conductivity transmitter
FCM10E | FOXBORO | I/A SERIES COMMUNICATION MODULE
EMC1600 | EtherWAN | 16-Bay Media Converter and Ethernet Extender Chassis
DMC-4143 | Galil | Motion Controller
DS200SIOBH1ABA | GE | I/O Control Board
D674A905U01 | ABB | Cartridge U-low HART / Std. FET 300
CE4050S2K1C0 | EMERSON | I/O Interface Carrier with Carrier Shield Bar
C400/10/1/1/1/00 | SCHNEIDER | SERVO CONTROLLER
ATCS-15 | SCHUMACHER | Temperature control unit
ACC-8E PMAC-2 602469-103 | Delta Tau | Breakout Terminal Block Board
A90L-0001-0515R | FANUC | Spindle motor cooling fan
350005-02-04-00-00-00 | Bently Nevada | DC IN Card Input Module
330930-065-01-05 | Bently Nevada | NSv Extension Cable
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