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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RELIANCE 57C554 Remote I/O Shark Interface Module
RELIANCE 57C494 Power Supply Module
RELIANCE 57C493 Automax Power Supply
RELIANCE 57C491 Supply module
RELIANCE 57C463 Frequency Event Counter Module
RELIANCE 57C446 PLC Module
RELIANCE 57C445 PC link interface
RELIANCE 57C443 Remote I/O scanning module
RELIANCE 57C442 Interface Module
RELIANCE 57C441 created Modbus Plus Module
RELIANCE 57C440 Ethernet Communication Module
RELIANCE 57C439 7010 Automax processor
RELIANCE 57C435A AutoMax Processor Module
RELIANCE 57C435 7010 Processor Module
RELIANCE Processor Module 57C431
RELIANCE 57C430 Processor Module
RELIANCE 45C322 power supply module
RELIANCE 45C360 Analog output card
RELIANCE 45C313 Bracket assembly
RELIANCE 45C201 Remote I/O Processor.
RELIANCE0-57435 7010 processor module
RELIANCE 0-57419 input module
RELIANCE 0-57408 power module
RELIANCE 0-57407 DCS processor module
RELIANCE 0-57404 Network communication module
RELIANCE 0-57402 Output module
RELIANCE 0-57401 digital I/O drive
RELIANCE 0-57400 digital control system created
RELIANCE 0-57334- I/O rack
RELIANCE 0-846405-4R Driver board assembly
RELIANCE 0-57160-L Stabilized circuit board
RELIANCE 0-48652-4 printed circuit board
RELIANCE 0-48652-30 Calibration instrument panel
RELIANCE 0-419437-B AutoMax Interface Board
RELIANCE DSA-MTR-40D2 DRIVE
RELIANCE 0-57400-A AC/DC input module
RELIANCE 0-57402-C utput module
RELIANCE 0-57405-C analog I/O drive module
RELIANCE 30V4060 GV3000/SE AC Drive
RELIANCE 57045 Spare parts module
RELIANCE 57412 Field voltage regulator module
RELIANCE 57421 Pulse Tach input module
RELIANCE 57C332 Rack assembly
RELIANCE 57C443 I/O Scanner Module
RELIANCE 57C652 module
RELIANCE 803.65.00 PC BOARD
RELIANCE 770.90.10 Autobus Interface Module
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