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Opportunities and challenges of AI manufacturing

Source:Updated:2018-06-21 08:22:26

Machine after substitution wave, information revolution intensified, machinery and equipment, and products manufacturing elements are no longer individuals, they are closely linked with the Internet of things through the industry and achieve a more coordinated and efficient manufacturing system. Cloud computing can store a large amount of field data to realize functions such as machine status monitoring, production analysis and product prediction and evaluation, which is the industrial 4.0 model proposed by Germany.

The transformation of the current manufacturing can be thought of as automatic upgrade and the integration of information technology, it is not just a substitution, automation and machinery we hope factory can achieve greater democracy decision-making, flexible production of diversified products, and can quickly respond to market changes more.


Will be a combination of artificial intelligence and manufacturing system is inevitable, use of machine learning, pattern recognition model and cognitive analysis algorithm, can improve the competency of factory control management system, implementation of the so-called intelligent manufacturing, can make the enterprise get better advantage in today's competitive environment.

German science and artificial intelligence research center director professor Hans wu thought Kurt in the peak BBS "in 2018 the new artificial intelligence", points out that industrial 4.0 era of smart manufacturing can be divided into three levels, the first is a core part is the most intelligent factory, the second is the intelligent operation service, the third is intelligent manufacturing support services.

The core of intelligent manufacturing is intelligent factory

The whole intelligent manufacturing process mainly revolves around the intelligent factory, and artificial intelligence plays an important role in the intelligent factory. Internet of things will be connected together all of the machines, such as the networked controller, sensors, actuators, then, AI sensors can be analyzed and then upload the data, which is the core of intelligent manufacturing.

As industrial applications and development of the Internet of things, the network and the real system will closely relates in together, will produce the scene of the processor is the Internet of things, sensors, and makes robots can communicate, can communicate with each other, and the machine and people will no longer be strict division of labor, the future manufacturing systems and the machine together.

Digital twins are important role, and the whole process of intelligent manufacturing has a twin model, digital system includes anything in the real world, can be applied or operation manual, etc. The system can flexibly configure the production of products, such as giving instructions to the machine according to the product requirements and asking the machine to do something.


In addition, there is human-computer interaction in the intelligent manufacturing system, that is, the interaction between human and robot. There is also artificial intelligence to drive, optimize products and processes. Factories need to do some predictive maintenance or predict the energy consumption of machines and so on. More and more of these functions can be realized in smart factories.

Intelligent operation improves the efficiency of the factory

In addition to pure production, smart systems also provide running services. Examples include internal mobile travel management, intelligent logistics, smart buildings, smart products and smart power grids. By digitizing multiple births, all elements of the entire process can be coded at first, including product features, manuals, etc.


With this information system, managers can easily understand to the actual situation of the entity object, according to the result of data analysis can be reasonable arrangements and scheduling, making factory can run at the lowest energy consumption, and obtain good production efficiency, or is it better to meet customer delivery requirements.

The word twins themselves are very complex content, and they are not enough in many places at present, such as training, supplier, partner service, etc. These functions need to be redesigned and improved.

Big data analytics support smart manufacturing

In the future, the factory will provide optimization Suggestions for products through data analysis, but in fact most of the data will come from outside the enterprise, such as feedback data from customers. Sufficient data can provide accurate insight, so the data also includes many external related fields, such as partners and suppliers, because they provide parts. In addition, companies can learn about certification, regulation and legal requirements from regulators, as well as media, investors and shareholders, as well as competitors. If companies don't pay attention to their competitors, they are likely to be overtaken one day.


When designing a product, you need to plan when the production will be launched. Then, you need external data as well as internal data. Manufacturers must know their service providers, partners, etc., and how they are doing. Therefore, it is a great challenge to combine the internal data of smart factories with external data.

External data will encounter the problem of data standardization, because partners or suppliers to the data may be structural, some is to use language to describe the product, not form or text to reflect. So you want to combine unstructured data with internal structured data. Most of the data inside the factory is structured, such as taking product photos and voice calls with the camera.

If you want to solve these problems, you need to manage your customer relationships and make your supply chain smarter. Such intelligence means that the participants in the entire manufacturing process should be interconnected and use the Internet of things, big data analysis and other technologies to obtain more business intelligence. Of course, you can also use AI to help the enterprise optimize the entire process.


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