Description
hardware flow control. It is an ideal choice in the field of industrial automation.
3 Case Studies on Reducing Scrap Rates
Any product assembled or produced in a factory goes through a series of quality tests to determine whether it needs to be scrapped.
High scrap rates are caused by the opportunity cost of not delivering products to customers in a timely manner, wasted personnel time, wasted
non-reusable parts, and equipment overhead expenses. Reducing scrap rates is one of the main issues manufacturers need to address. Ways to
reduce scrap include identifying the root causes of low product quality.
3.1 Data processing
Root cause analysis begins by integrating all available data on the production line. Assembly lines, workstations, and machines make up the industrial
production unit and can be considered equivalent to IoT sensor networks. During the manufacturing process, information about process status,
machine status, tools and components is constantly transferred and stored. The volume, scale, and frequency of factory production considered in
this case study necessitated the use of a big data tool stack similar to the one shown in Figure 2 for streaming, storing, preprocessing, and
connecting data. This data pipeline helps build machine learning models on batch historical data and streaming real-time data. While batch
data analytics helps identify issues in the manufacturing process, streaming data analytics gives factory engineers regular access to the latest
issues and their root causes. Use Kafka (https://kafka.apache.org) and Spark streaming (http://spark.apache.org/streaming) to transmit real-time
data from different data sources; use Hadoo (http://hadoop.apache.org ) and HBase (https://hbase.apache.org) to store data efficiently; use
Spark (http://spark.apache.org) and MapReduce framework to analyze data. The two main reasons to use these tools are their availability as open
source products, and their large and active developer network through which these tools are constantly updated.
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80VD100PD.C022-01
140DDO35300 output module
140DDI84100 output module
140DD035300 output module
140CRP93100 output module
140CRA93200 output module
140CPU67260 output module
140CPU67260 output module
140CPU43412 output module
140CPU11303 output module
140CPU11302 output module
140CPS11410 output module
140CPS11400 output module
140CHS11100 output module
140CHS11000 output module
140AVI03000 output module
140ACO13000 Output module
140ACO02000 output module
140ACI04000 output module
080-332-000-R Multifunction device server
64SD1-08KRF1-13 Data multi-function board
62K-NHC0-DH Automatic production line control
61C350 I/O module
61C22A I/O system
60M100-00 sensor
33VM52-000-29
31C450-503-4-00 Frequency changer
31C075-503-4-00
31C015-503-4-00 Frequency changer
31C005-503-4-00 Frequency changer
30-W2960B01A Serial port
30V4060 Ac driver
26D023003 microprocessor
22-COMM-D Communication module
22B-CCC Communication module
20-VB00601 Power module
20DC460N0ENNBNBNE Frequency changer
12HGA11J52 Universal auxiliary relay
12HFA51A42H relay
8V1090.00-2 Servo driver
8LSA55.EB030D200-1
8LSA25R0060D000-0 Servo motor
8B0C0320HW00 Power module
8AC123.60-1 B&R
8AC110.60-2
07KT92-CS31 Programmable controller
07KR31-FPR3600227R120 Servo driver
07AC91-GJR5252300R0101 Distributed Automation I/O
6SM77K-3.000 Servo motor
6SM56-S-3000 Robotics and automation
6SM37L-4.000 KOLLMORGEN Stepper motor
6GK5204-0BA00-2AF2 redundancy
6DD1642-0BC0 module
5X00481G04 Controller module
5X00419G01 Relay Output Module
5X00121G01 Analog output module
5X00062G01 Analog input module
5SHY6545L0001-AC10272001R0101-5SXE10-0181
5SHY4045L0006-3BHB030310R0001 IGCT series module
5SHY4045L0001 3BHB018162R0001 Fluidic tube
5SHY3545L0020 3BHE014105R0001
5SHY3545L0010 Thyristor IGCT
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