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Production Process Optimization based on
Defect Early Warning

Online early warning / closed-loop control of production process. Firstly, select a defect and establish the correlation between the defect and parameters for process, product and equipment. Once the defect is predicted based on the online model: (1) the Online Early Warning system prompts the on-site operators to take measures to avoid the defect; (2) If the defect is found in the on-line monitoring of the product, or the defect is predicted based on the on-line model, when the data is sufficient, the system can judge which of the parameters causes the defect, so the system will make sure the same defect will not appear twice by adjusting the production parameters of the front end!

Establishment of defect prediction model. Even if there is online defect measurement, the possible parameter combination is limited. When the defect is measured, the loss has already been caused! We want to predict the possible defect before the defect actually exists! Therefore, the defect prediction model for production line is always needed! When there is online measurement, the defect prediction model can be crated more accurately.

The model needs on-line data for real-time self-learning optimization. On the one hand, its accuracy is continuously improved, on the other hand, it also keeps up with the continuous update of production line technology, products and equipment.

Production process optimization. Common optimization includes: (1) online tracking of defect value change; (2) Defect early warning (table display and curve display); (3) Automatic formation of optimal combination value of process parameters and equipment parameters.

The following is the overall functional architecture of manufacturing optimization, guidance and execution system development based on SCADA:

  1. Intelligent diagnosis and control system for product quality (data model diagnosis optimization)

  2. Near / remote diagnosis and pre maintenance system of equipment quality (data model diagnosis optimization)

  3. Diagnostic optimization system for production efficiency (data model diagnosis optimization)

  4. Standardized development of data information collection template (model data standardization)

  5. Robot configuration quick development platform (primary system → Level 2)

  6. Establishment of communication standard protocol based on OPC-UA (automatic OPC)

  7. Whole process simulation and feasibility identification system of products (data model diagnosis optimization)

  8. Etc.

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

Planning & Consulting

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Defect Early WarningModelsManufacturingLi-BatterySteel

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