Technical Training Question Set

To understand our business, please answer the following questions; Or contact our team for answers.

1. What is the business of Metal Data?

2. What is Metal Data 's defect early warning system?

3. Is there any other defect warning system? （Limit value method and analysis method, etc）

4. Our main technical areas?

5. What are the objectives of each stage of Smart Manufacturing?

6. What is a Level 2?

7. What is the new generation of Level2 system?

8. What are the technical advantages of defect early warning system?

9. What is multilevel computer control architecture?

10. What are the main ideas for solving quality problems or defective products at the production site? What are the requirements?

11. What is a primary system? What are the product examples?

12. What is a three-level system?

13. What is three-level computer control at the workshop level?

14. What is device software? What is the relationship between device software and intelligent system? What are the advantages of device software development?

15. What are the problems in the mixing process of battery electrode slurry? How to solve?

16. What are the problems in the coating process of lithium battery electrode paste? How to solve?

17. What are the problems in the rolling process of lithium battery electrode after coating? How to solve?

18. What are the problems in the baking process of lithium battery slurry after cold coating? How to solve?

19. What are the problems and how to solve them in the process of lithium battery pole slice cutting?

20. How many pieces of lithium battery are wound into miles? What are the problems and how to solve them?

21. What are the three main stages of lithium battery production, chip production, cell production and battery module production?

22. What is the role of lithium battery manufacturing execution system data in diagnosing the performance of each section, three sections and the whole lithium battery?

23. Please explain Metal Data industry 4.0 brain business data modeling

24. Please explain the defect early warning system of Metal Data industry 4.0 brain and artificial intelligence

25. Please explain Metal Data industry 4.0 brain - Basic Automation brain

26. Please explain Metal Data industry 4.0 brain - intelligent supply chain brain

27. Please explain the multi-disciplinary requirements and multi-disciplinary integration of Smart Manufacturing

28. Please explain the four business areas that Smart Manufacturing must deal with

29. Please describe the main functions of the manufacturing execution system in the manufacturing production line

30. Please explain the main functions of ERP system in manufacturing enterprises

31. Please explain some important problems in intelligent system development

32. How to design the model of intelligent system? How to design the scene use case of intelligent system?

33. How to integrate basic automation, MES and ERP of multi-level computer system?

34. How to comprehensively optimize the incoming products of process equipment in the production process through the defect early warning system?

35. What are the common problems in field data collection at present? How to optimize?

36.  Introduction to the method of machine learning based on online data, which can be solved by model-based self-study in the production process

37. Pre calculation of machine self-study during production, calculation of closed-loop control after re calculation, long-term self-study, etc

38. Please explain the offline model, online model and software library of Metal Data on the software technology development platform of Smart Manufacturing equipment

39. Please explain the common problems caused by the lack of intelligent system in the construction of domestic intelligent production line, and explain the solutions

40. Please explain the possible development stages and main functions of intelligent systems in high-end material manufacturing

41. Use the project cases of Metal Data team to illustrate the development process of offline model, online model and online system in the past 30 years

42. Explain the problems and application requirements of the finite element artificial intelligence model of the German DFG project of our team

43. Explain the three groups of models and relevant data sources established by our team for Morgan

44. Explain the three sets of Level2 systems and main features developed by our team in the project of Cascade company in the United States

45. Explain the problems and solutions of the team to solve the project of Oregon company in the United States

46. Explain the problems and solutions of the team to solve the POSCO project in Korea

47. Explain the on-site problems, solutions and actual effects of the team in the project of Nangang

48. Explain the soft sensing technology in BYD lithium battery pole slice cutting project

49. Explain the defect early warning system technology based on artificial intelligence in BYD lithium battery pole slice cutting project

50. Clarify the high accuracy of the model in BYD project

51. Comparing the accuracy of the models of Oregon EOS and NISCO before and after improvement, this paper expounds the high accuracy of Metal Data model

52. Briefly describe the business areas, customer needs and solutions, for the major projects of the company

53. Briefly explain the methodology of learning in the lecture (direction, interest, method)

54. This paper briefly explains the basic methods of foreign language, self-study computer and optimizing scientific research in middle school

55. Explain the industry recognition of the team's technology (China selection, German scholarship, American certification, China hundred talents program, other talent programs, Shenzhen quality month promotion project, etc.)