Description
SM140/30/210/P1/44/R1/B1 Schneider Motor
SM140/30/210/P1/44/R1/B1 Schneider Motor
Module Clips Drive controller servo moto
SM140/30/210/P1/44/R1/B1 Motor is a device that converts electrical energy into mechanical energy.
It uses energized coils (i.e. stator windings) to generate a rotating magnetic field and applies it to the rotor (such as a squirrel cage closed aluminum frame) to form a magnetic electric rotational torque.
SM140/30/210/P1/44/R1/B1 Electric motors are divided into DC motors and AC motors according to their power sources. Most electric motors in the power system are AC motors,
which can be synchronous motors or asynchronous motors (the stator magnetic field speed and rotor rotation speed of the motor do not maintain synchronous speed).
The SM140/30/210/P1/44/R1/B1 electric motor is mainly composed of a stator and a rotor. The direction of force movement of the energized wires in the magnetic field is related to the direction
of the current and the direction of the magnetic field lines (magnetic field direction). The working principle of an electric motor is that the magnetic field exerts force on the current, causing the motor to rotate.
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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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