Ten technologies for intelligent manufacturing
The intelligent manufacturing reform involves the entire manufacturing industry, and there is no doubt that this is a big market of trillions. The sub-markets are all large blue oceans: China's robot market will reach 600 billion yuan in the next 10 years; China's civil drone market is expected to reach 11.09 billion yuan in 2018; it is expected that by 2020, China's automated logistics The market size of the system will exceed 100 billion yuan...
Intelligent manufacturing is a very large and very wide concept. In addition to the manufacturing enterprise itself, it is also closely related to the upstream and downstream enterprises in the supply chain. It includes automation, information technology, intelligent logistics, intelligent computing, and intelligent decision-making. The realization of intelligent manufacturing is a process from manual to semi-automatic to fully automated, ultimately achieving intelligent and flexible production. Intelligent manufacturing combines manufacturing with information technology and Internet technology to realize the interconnection of the entire industry chain in production processes, production management, supply chain systems, and marketing systems.
So how do companies implement their own smart manufacturing reforms? The following ten technologies are all knowledge points:
1. Multi-source multi-channel data real-time acquisition and sensing technology
Multi-source sensor data acquisition is the premise of intelligent sensing in intelligent manufacturing process. It is composed of various sensors (pressure sensor, displacement sensor, visual sensor, etc.) to realize real-time acquisition, analysis and conversion of multi-source and multi-channel distributed data. .
The multi-source sensor data acquisition system includes the following technologies:
• Signal conversion technology
• Real-time network communication technology
• Multi-thread management technology
• Data Cache Pool Technology
• Black Box Technology
• Information Security Technology
2. Heterogeneous data content fusion and transmission sharing technology
Through content analysis and fusion processing of various heterogeneous computing data, mining hidden information and effective data from massive data, and improving the accuracy of various equipment state monitoring in the intelligent manufacturing process.
Heterogeneous data includes: massive multimedia sensor data, text/hypertext, sound data, image data, video sequences, and so on.
3. Multi-task adaptive coordination technology for complex working conditions
The realization of intelligent manufacturing often needs to be able to analyze the current working environment and task requirements independently, realize multi-task adaptive collaborative planning, and adaptively adjust the operating strategy according to different task difficulty.
Multiple operating conditions include the following (taking excavation as an example):
• Commonly used, mining shape rules, and often using this feature
• Special, mining shape rules, but not often used
• Self-marking, mining shapes are irregular, but often used
• Highly customizable, highly dependent on driving experience
4. Multi-machine coordinated clustering interaction and control technology
The intelligently manufactured multi-machine cluster mimics the behavior of biological clusters, and the single machine interacts with each other through information interaction and autonomous control, so that complex tasks of diversity can be completed at low cost in various sinister environments.
Specifically include:
• Remote console, human-computer interaction device remote control, task assignment and monitoring
• Mobile client, web page, APP for task assignment and monitoring
• Intelligent mechanical end, environment sensing, body condition sensing, autonomous operation control
• Mobile Internet, wireless data communication bearer
• Satellite positioning, navigation and measurement assistance
• Cloud data center, environmental modeling analysis, task and trajectory planning, big data analysis and diagnostics
5, big data drive fault diagnosis deep learning technology
The massive characterization data generated during the operation of the manufacturing equipment contains a large amount of fault information. On the basis of collecting the operational characteristic data of the intelligent equipment, the deep learning algorithm is applied to the knowledge mining of the big data, and the diagnostic rules related to the fault are obtained. Intelligent fault prediction and analysis of equipment failures.
6, digital twinning and digital prototype modeling analysis technology
Digital Hybrid makes full use of physical models, sensor updates, operational history and other data, integrates multi-disciplinary, multi-physical, multi-scale, multi-probability simulation processes to complete mapping in virtual space, reflecting the full life of each equipment in the manufacturing process. Cycle process.
7. Multi-technology route work plan optimization decision technology
For the decision-making problem of uncertain, semi-structured or unstructured intelligent manufacturing work schemes, intelligent manufacturing and product design are realized in the environment of uncertainty, incompleteness and fuzzy information through signal reasoning and quantitative reasoning. Self-determination of the optimization of the multi-objective and multi-technology route work plan in service.
8, process tooling collaborative push and automatic clamping technology
Personalized push technology and semantic retrieval technology are integrated into the process tool pushing process. Based on the personalized semantic retrieval of the intelligent equipment and product tooling features, a personalized technology tooling collaborative push mechanism is formed to improve the process of acquiring products in the process of intelligent manufacturing process design. Tooling efficiency.
9. Product knowledge map and knowledge network construction technology
Through the structural level integration of distributed multidisciplinary knowledge data, the grammar and semantic differences of multidisciplinary and multi-domain knowledge data are eliminated, the data structure is consistent, and the knowledge of design and design library data is represented, and the knowledge base is established. .
Structured data, semi-structured data, and unstructured data are structured, transformed, and filtered to form convergent or consistent and non-redundant structured data, that is, subjectively abstract the objective world into a design database, and then form through knowledge representation. knowledge base.
10. Electromechanical and liquid integration cloud platform knowledge service technology
Knowledge service technology starts with the automatic push of knowledge, organizes the interdisciplinary knowledge of machine, electricity and liquid integration in an orderly manner, and pushes the designer to appropriate design knowledge in the appropriate design process to realize the individualization of interdisciplinary knowledge service. Efficient and intelligent.
