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Defect Warn

Equip Intelli

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Example Of Lithium Battery Manufacturing Optimization Three Contracts Summary

Project Summary

Development of our team in the field of lithium battery began in the Spring Festival of 2012 and has been 10 years now! During this period, a series of large enterprise projects were completed, there are more than 100 lithium battery manufacturing related models developed only!

Related items are:

(1)The development of optical film thickness density consistency optimizes the production process through a large number of process parameters

(2)The basic model test of lithium battery manufacturing was personally signed by the founder of a large company; The project determines the extremely high accuracy of the model: the contract requires 85% hit rate, and the actual hit rate is 98%

(3)The Level 2 of lithium battery production optimization, such as the Level 2 of lithium battery pole slice cutting, has completed a series of software functions based on the prediction of defect model and the prediction of knife notch as the main influencing factor; In order to realize the high accuracy of the model, the model for predicting this defect and the model for predicting knife notch are respectively machine learned

(4)The hardware of knife notch measuring device is photo shooting and processing based on 1000 times microscope, and the software is the processing logic of knife notch

After the relevant lithium battery project, the company was accepted as the supplier of the cloud rail project of the same company (since 2020)

For related lithium battery project technologies, there are only a dozen ppts (each can be explained for 2 hours) (more than 100 ppts for all Smart Manufacturing)

More Than 100 Sets Of Emerging Industry Models

Emerging Industry Model

In the lithium battery project, more than 100 sets of prediction models for various defects and display parameters in each manufacturing section have been developed. See the related models. In subsequent projects, such as lithium battery winding, volume division and rolling, a large number of models have also been developed. The high accuracy of the model is incredible! See High precision model prediction.

Field Problems

After several years of project development of large lithium battery enterprises, it is obvious that it is difficult to provide data required for high-quality Smart Manufacturing on site. Enterprises usually have many on-site problems. For example, the MES system of the enterprise does not have the key tool data of the project. Therefore, the data about the knife hole and tool usage can only be found in a database of the cutting machine, which is often not connected to the production line. Although every enterprise has a large amount of data on the table, there are few data that really meet the needs of high-quality Smart Manufacturing, and there are often important data omissions. The enterprise has no clear punishment system for the problem of missing data on site or deliberately not collecting data.

Development And Application Of Defect Early Warning System

The optimization of lithium battery production process mainly contributes to the development and application of defect early warning system; The development and use of defect early warning system mainly depends on the high accuracy of the model! The benefits of lithium battery project are summarized as follows:

  • An accurate lithium battery model has been developed to make accurate defect prediction possible

  • The machine learning of knife gap prediction model and lithium battery defect model are realized to ensure the high accuracy of the model

  • The successful development of the software and hardware of the tool notch measuring device ensures the high-speed automatic measurement, makes the tool notch machine learning normal, and also enables the optimal use of the tool on site. (See the description in the following section

  • The successful application of soft sensing technology contributes to high-precision model prediction

  • The successful development and application of defect early warning system has greatly reduced the defective lithium battery; Usually, the defective rate of lithium battery is close to 10% in all factories in China through various sections of lithium battery manufacturing!

Optimal Use Of Cutting Tools

The loss caused by the time occupied by tool replacement on site is greater than that caused by tool regrinding. Therefore, in the use of tools, the tools are grouped according to the initial tool notch, for example, divided into five groups. In the installation of tools, all tools are taken from one group, This can greatly reduce the loss of early replacement of some tools and simultaneous replacement of other tools due to tool differences.

The initial tool notch value is automatically measured by a specially designed and developed tool. For the hardware and software development of the automatic measuring device, see the development case of lithium battery defect early warning system.

Also See: Level 2 control of coating thickness and density of Metal Pass lithium battery

Lithium Battery Manufacturing Optimization Series

Defect Warning System Series
Development case of lithium battery defect early warning system
Function and application of defect early warning system products
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


Project Cases

   Summary, Key Projs, Model Projs, Rolling Mills
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Intelli Equip., New Level 2, Li-Batt

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