Smart Manufacturing
Consulting and product development based on existing software
 

I. Smart Manufacturing technology consulting

Smart Manufacturing technology consulting customers are as follows. Here, we focus on benchmark customers, such as PRIMetal, SMS and Danieli, etc. in engineering technology industry; EOS (Evraz Oregon Steel) , NISCO, and South Korea POSCO etc. in the iron and steel industry; BYD in the lithium battery industry, Skyworth & TCL, etc.

The benchmark projects are shown in the figure below. It mainly includes the DFG finite element artificial intelligence project in Germany, Morgan (now PRIMETAL) large-scale model project in the United States, the three sets of Level 2 project in Cascade Steel, the New-Generation Level 2 project in Oregon and Nanjing, and the lithium battery Smart Manufacturing project of BYD and Tesla, etc.

During the period of completing a series of BYD projects in Shenzhen, it also completed a number of emerging industry projects, as shown in the figure below.

II. Consulting and product development based on existing software

1. Defect early warning system

The main contribution of the team in Smart Manufacturing is to collect data on the factors related to on-site defective products or analyze data based on other data sources, establish engineering models, carry out machine learning and optimize production operation. The ultimate goal is to eliminate or reduce defective products in the production process, and improve the yield.

This is to refer to the customer's MES or industrial Internet or other data obtained based on SCADA, and to carry out defect early warning based on the existing software. Before the products is produced, the model is established through the idea of historical prediction of the future, and machine learning is carried out. The generated model is used to predict whether the relevant products will become defective after the production is completed; If it is a defective product in the future, the alarm will be given before the production is completed. The operator can make the product genuine by changing the parameter combination or even changing the wear parts (knife or mould). The existing software suggests the recommended value of the best parameter combination.

2. Equipment intelligent system

It is simplified by the omni-directional Smart Manufacturing system, which enabled the production equipment system to achieve the optimal operation and to provides equipment software for the existing equipment hardware. Equipped with intelligent system, the price of equipment can therefore increase by 2 to 10 times. For example, the price of 5G material production line of a fortune 500 company is 180 million RMB when equipped with intelligent system. When only equipment hardware is available, its value is only 30 million RMB! Based on the online data of existing equipment, engineering modeling is carried out. The established model is based on the existing online data for machine learning, so that the model is fully bound with the existing production line (that is, the model is extremely accurate on the existing production line). Based on the model, various operation parameters of the existing production line can be well coordinated. The intelligent system is equipped at the production site. When the production line has superior basic automation and manufacturing execution system, the optimal full-automatic operation can be realized through system integration.

3. Tool Optimization and management system

In the absence of this system, the tool can be used for a specific length of time and then removed for the next round of grinding. When using this system, the possible cutting process in the production process is simulated based on the initial knife gap until the end knife gap is reached. Therefore, it is necessary to know the defect of the object cut by the tool in the production process. The system can use the existing high-power microscope on the production line to measure the initial knife notch and the end knife notch, combined with the field application simulation to form machine learning. If more effective automatic measurement is needed, the automatic tool detection system that has been successfully developed by our team and used in a large lithium battery production line can be used; The system can also classify the initial quality of the tool after grinding based on the initial tool notch, and give priority to the use of tools of the same grade in the same environment to avoid too frequent tool change. The system can also track and simulate the use of existing tools in the tool library. When the number of tools in the tool library reaches a certain number, it will give an early warning and urge the site to send the tools to the grinding consumer for re-grinding. This ensures that a sufficient number of tools are available in the tool magazine.

4. Optimal use and management system of mould

You can refer to the above Tool Optimization and Management System. Five sets of rapid design systems have been completed. Based on a large number of models, the interface only displays production operation terms, which can enable non designers to carry out high-quality design! There are also two sets of high-level design systems developed for professional designers, considering the internal correlation of all operating parameters. The system has been strictly verified by POSCO, TISCO and other large international enterprises, and is completely consistent with the site, which makes customers very surprised!

III. Future business priorities

1. Overview of defect early warning system

Business model. Put the mature technology completed in BYD and other projects into the computer system or chip to form products that can be sold on a large scale. The price is 1 / 10 of the price developed by our team. This development is based on the rewriting of the existing intelligent system source program. If we sell only one copy at 100% price, our profit will be more than 80% and the customer's profit will be more than 10 times a year! Moreover, as an intelligent system, governments at all levels have subsidies and special personnel apply for it.

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!

2. Later business development

Upgrade to equipped with intelligent system. After 2 to 3 years of operation of the defect early warning system (it can also be now with the support of large companies), it will be upgraded for another round. This is another huge field! This system can increase the price of equipment by 2 to 10 times! For example, the example of AMER group (world top 500) 5G material production line is 6 times! The main reason for the relatively low export price of certain equipment is the lack of software (intelligence and data of equipment), only hardware!

Then upgrade to an all-round Smart Manufacturing System. This has been restored to the system developed by us for more than 30 years. During this period, more than 200 Smart Manufacturing projects have been completed in Europe, America and other countries! This is based on our technical advantages as German Ph.D. of engineering and US Ph.D. of software, and at least 3 years of working experience in 7-8 professional fields. Below is our development in Level 1 basic automation (Industry 2.0), Level 2 model and logics (Industry 4.0), Level 3 MES and Level 4 ERP (all in Industry 3.0, below Industry 3.5). This Mylti-Level Automation is our primary work area. A list of publications and presentation PPTs are showed in Meta4-0.com/pub/index.htm.

Planning & Consulting

  Biz discuss, Planning, Maturity, Consulting Area, Predict Maint.
  Defect Early Warning, Models, Manufacturing, Li-Battery, Steel

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