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.
https://www.xmamazon.com
https://www.xmamazon.com
https://www.plcdcs.com/
www.module-plc.com/
https://www.ymgk.com
PP825 3BSE042240R1 ABB
3BSE042240R1 control panel ABB
PP825 control panel ABB
3BSE042240R3 control panel ABB
PP825A control panel ABB
PP825A 3BSE042240R3 ABB
PP836 3BSE042237R1 ABB
3BSE042237R1 control panel ABB
PP836 control panel ABB
3BSE042237R2 control panel ABB
PP836A control panel ABB
PP836A 3BSE042237R2 ABB
PP845 3BSE042235R1 ABB
3BSE042235R1 control panelABB
PP845 control panelABB
3BSE042238R1 control panelABB
PP846 control panelABB
PP846 3BSE042238R1 ABB
PP865A 3BSE042236R2 ABB
3BSE042236R2 control panel ABB
PP865A control panel ABB
3BSE092977R1 control panel ABB
PP875 control panel ABB
PP875 3BSE092977R1 ABB
PP877 3BSE069272R2 ABB
3BSE069272R2 control panelABB
PP877 control panelABB
3BSE069276R1 control panelABB
PP881 control panelABB
PP881 3BSE092978R1 ABB
PP885 3BSE069276R1 ABB
3BSE069276R1 control panel ABB
PP885 control panel ABB
3BSE069297R1 control panel ABB
PP886H control panel ABB
PP886H 3BSE069297R1 ABB
PM891 Controller unit ABB
AC800M 3BSE053240R1Controller unit
PM891K01 Controller unit ABB
PM891K02 Controller unit ABB
3BSE053242R1 Controller unit ABB
36BSE053241R1 Controller unit ABB
PM891AK02 Controller unit ABB
PM891AK01 Controller unit ABB
PM891K02 3BSE053242R1 ABB
PM891K02 3BSE053242R1 ABB
PM891K01 36BSE053241R1 ABB
AC800M 3BSE053240R1 PM891
PM802F 3BDH000002R1 ABB
Reviews
There are no reviews yet.