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Function And Application Of Defect Early Warning System Products

Defect Early Warning System

1.  Business model. The promotion of mature technologies to be completed in the project of a large company. This development is based on the rewriting of the existing intelligent system source program, and the annual profit of customers is more than 10 times! Moreover, as an intelligent system, governments at all levels have subsidies and special personnel apply for it.

2.  Operation mode. History is the way to predict the future! For example, there are 10 stages of production. In the first or second stage, we can know whether the products after completing the 10 stages are defective under the existing production conditions; If yes, the system will give an alarm in the first or second stage, adjust the parameters in the second or third to tenth stages, and even replace the worn parts (tools or molds, etc.) to make the product genuine! This can reduce the defective rate and improve the product quality!

3.  Data requirements. The data source can be MES, industrial Internet or SCADA.

4.  Examples. In the one-day schedule of Shenzhen quality month, half a day was specially set aside for our team members to introduce our defect early warning system; Continue to discuss cooperation at the dinner party in the evening.

Defect Early Warning System Products

The standard version of defect early warning system includes intelligent system, client computer, alarm and database. At present, a three-tier system is used in the production line of a large company, including server, client and database. When promoting this technology, in order to simplify the structure and increase sales, our team simplified it into an independent system, loaded it into a computer system, or even loaded it into a large chip. The alarm device of this system adopts two sets of alarms, which are connected to the client computer.

Based on a large number of online data collected on site, a large number of models are used to optimize the coefficients of the model through machine learning, making the model extremely accurate on the existing production line. Before the completion of the existing products, it is convenient to use the way of history to predict the future to predict the defects of this product after the completion of production, whether it is genuine or defective. Using a large number of models on site and the way to optimize defects, optimize the production process that predicts possible defects, and eliminate the defects of the final product.

Functional Application Of Products

The main functions of this system include the following::

1.   Production line customization. The system at industry 4.0 level needs to be customized. This means that different factories and products have different models. Therefore, the team first analyzes customer data (MES, industrial Internet or direct SCADA data) to find out a series of factors affecting defective products. Then the team members customize and install the defect early warning system based on the information.

2.  Data cleaning and optimization. According to the characteristics of the manufacturing process, the wrong and missing collected data are optimized and supplemented.

3.  Defect prediction. For the target defect selected by the customer to be eliminated, it can automatically carry out modeling and machine self-study, determine the relationship between the equipment, process, incoming materials and other factors in the production process and the defect, and the designed defect early warning system can pre report whether the product is defective before the product is produced; If it will be defective, the system will give an alarm and prompt the on-site personnel.

4.  Defective product warning. In actual operation, when 90% or 80% of the maximum allowable value of defects (i.e. the value of defective products) is reached (this value is adjustable), the alarm (whistle, etc.) will be started to ensure that defective products can be fully avoided. This is the origin of the name of defect early warning system. Since the system is to be through machine learning with data, the model in the system can fully reflect the actual situation of the site as long as the data quality is sufficient.

5.  Production guidance. This system guides the operators to pursue the optimal operation on site, such as recommending the best parameter value; If there are wear parts on site, the optimal service time of each wear part can be recommended. Take the slitting tool as an example. It is assumed that the normal use time is one week. Due to the different conditions of each wear part tool, a good tool can reach its normal service life without causing defective products; For poor knives, it is easy to produce defective products before they are lower than the normal service life. Therefore, by determining the notch value of the initial tool, the service life of each tool and other wear parts shall be determined according to the actual service condition of each tool.

6.  Defect elimination. After receiving the alarm, the on-site personnel first determine whether it is possible to eliminate the defective products and make them genuine by optimizing the combination of existing production parameters? The system also provides reference data for operators. If so, use it; If not, warn the on-site operators to quickly replace the worn parts (tools, molds, etc.) to make the predicted final products authentic. This can make the products that would have become defective products become genuine products, so the defective rate is greatly reduced and the product quality is greatly improved.

7.  Soft sensing technology. If some parameters are extremely difficult to measure in the project, soft sensing technology can be used; Based on the associated parameters, the required parameters are predicted through high-precision models (including machine self-study to improve the model accuracy of course). Some online data fall into this category.

8.  Other methods. When dealing with the missing data, other artificial intelligence methods can also be used.

See the installation of defect early warning system in MES for details


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

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