Development Case Of Lithium Battery Defect Early
Slitting Burr Of Lithium Battery Electrode
If the burr of lithium battery exceeds the allowable length,
it will lead to the ignition of lithium battery, such as the
ignition of previous Samsung battery and spontaneous
combustion during the use of electric vehicles. These are
malignant accidents that must be solved. By analyzing a series
of influencing factors, this project finds out the model
describing the burr length of lithium battery, and establishes
the burr defect early warning system.
High Precision Model Prediction
In multiple contracts of lithium battery Smart Manufacturing, it is very important to verify the accuracy of
model prediction. There is no precedent in the world for
contracts where customers are unwilling to guarantee the data
quality but require our guarantee model to predict the hit
rate of at least 85%, and all the experts reviewed by the
company think it is difficult to achieve the guarantee
accuracy, so that a considerable number of people within the
company tend to give up! The project leader is responsible for
the continuous development of human resources, and has
achieved 98% hit rate in the actual acceptance, and even 100%
in some acceptance! (team members have reached the model
accuracy surprised by their counterparts in the world twice in
the DFG project in Germany and the new generation secondary
system project in the United States!)
This defect early warning system is one of the key projects
promoted by Shenzhen quality month. Therefore, the main
designer was also invited to give a special lecture on the
opening day of Shenzhen quality month.
System For Knife Notch Of Lithium Battery Pole Cutting Piece
Among the many measures that affect and control the length of
slitting burr of lithium battery, controlling the service life
of the tool is the key. In production on site, when a knife is
used for about a week, it must be replaced to continue
grinding. Since the quality of each knife is difficult to be
exactly the same, the service life of each knife is determined
by the key factors such as the initial knife notch value and
the defect quality of the incoming pole piece in the slitting
process. Due to some problems in the field data structure, for
example, the tool number does not exist in the MES database.
Therefore, in the time period of obtaining the initial time
and end time of a knife, it is usually necessary to obtain
indirectly: the roll numbers of all rolls cut by the knife are
currently obtained through the database in the film detector,
and the measured tool notch values are obtained directly from
the measuring device computer system.
Self-study And Soft Sensing
The change of knife notch parameters at any time is the main
parameter affecting the defect. Therefore, the developed
measuring device is used to measure the knife notch and form
the machine learning of the knife notch, so as to optimize the
The measurement of knife notch is based on a 1000x microscope.
However, in the actual slitting process, the tool is wrapped
in the slitted polar strip, which can not be measured with a
1000 times microscope; Moreover, the calculation of burr
length requires the value of knife notch at any time, which
can not be realized. Therefore, only soft measurement can be
used. Whether the soft sensing is correct or not depends on
the prediction accuracy of the model. With the progress of
self-study, the accuracy of the model is greatly improved,
which can reach three times the accuracy of the offline model.
Therefore, soft sensing has become a very accurate way.
Automatic Measurement System For Knife Notch
A contract in the process of the project is to develop an
automatic measuring device for knife notch, including software
and hardware. This is to enable high-speed, automatic and
accurate measurement of tool notch. The hardware is mainly a
1000x microscope imported from Germany. The tool can be
measured automatically. The software mainly uses machine
vision to obtain the knife gap data from a large number of
pictures taken, and abstractly describe the knife gap.
The disc-shaped cutter rotates automatically in the measuring
device. Through high-power microscopy, hundreds of photos are
taken for each cutter, and then all the photos are combined
into a knife notch. Since each particle of dust becomes a
behemoth under a 1000x microscope and the convenience of the
user of the device is taken into account, the measurement
method and site selection are fully considered.
Defect Early Warning System
In the lithium battery project of a large company, a defect
early warning system has been gradually formed, that is, in
the production process, before the production of the product
is completed, we will know whether the product will be
defective in the future. If so, we will call the police now!
Operators can optimize the parameter combination and even
replace the worn parts, such as cutting tools and molds, so
that the current products will become authentic in the future.
The defect early warning system provides the recommended
optimal parameter combination. By transforming this set of
technology into products, the products of defect early warning
system are formed.
In the production process, when the burr level reaches 80% of
the maximum allowable level, an early warning will be started,
and when it reaches 90%, a strong early warning will be given.
This ratio is adjustable in the software. This is one of the
key projects of a lithium battery manufacturing enterprise.
Smart Manufacturing requires the intelligence of the
system, which is usually predicted by the model, such as the
burr length here.
Defect Warning System Series
Development case of lithium
battery defect early warning system
Function and application of defect early warning system
Customer requirements of defect early warning system
Technical consultation of defect data based on Prediction
Production process optimization based on defect early warning
Introduction to defect early
warning system technology
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