The wave of intelligent manufacturing is coming, what is the transformation of traditional enterprises?

With the help of emerging technologies such as the Internet of Things, big data, and artificial intelligence, the global manufacturing industry is moving from automation to intelligence, and accelerating the application of intelligent manufacturing is of great significance for enhancing China's manufacturing competitiveness. The transformation and upgrading of “Made in China” to “China's Smart Manufacturing” is not only the requirement of national strategy, but also the survival of thousands of traditional Chinese manufacturing enterprises.

The so-called intelligent manufacturing is actually to build a human-machine integrated intelligent system, through the cooperation of human and intelligent machines, to expand, extend and partially replace the mental work of human experts in the manufacturing process. That is to say, on the basis of current machine automation, the machine has "intelligence", which enables analysis, reasoning, judgment, conception and decision making in the manufacturing process.

So how to achieve smart manufacturing? As a systematic project, intelligent manufacturing is inseparable from the linkage and blessing of big data, Internet of Things, cloud computing, intelligent machines, artificial intelligence and other technologies.

Big Data

After industrial equipment is connected to the Internet, manufacturers will have access to a wealth of on-site data. Smart manufacturing is the use of big data analytics to improve complex processes and manage the supply chain. The new advantage of big data analytics is that it captures opportunities more quickly and accurately, by pooling data and analysing, potentially discovering useful information that can be used for future decisions, or finding insights for immediate decision making.

Then, the field data is diverse, and what kind of data analysis is beneficial to the manufacturer? The first is regular data analysis, which reveals the actions that should be taken and the results of the analysis provide recommendations for the next steps. The second is predictive data analysis, which will show what might happen. The third is diagnostic data analysis, looking at past performance to reveal what happened and why. The last one is a descriptive analysis that is used to display on the real-time dashboard. With the collection and accumulation of data, manufacturers will be able to accurately assess and predict production, making the plant system more suitable for current business needs and maximizing benefits.

Internet of Things

With the development of the application of industrial Internet of Things, manufacturing elements such as machinery and equipment, people and products are no longer independent individuals. They are closely linked through the Industrial Internet of Things, that is, the Internet of Things connects the processors and sensors at the production site. The robots can communicate with each other and communicate with each other, and the work of machines and people will no longer be strictly divided. The future manufacturing system integrates people and machines.

At present, many companies have provided powerful IoT joints, such as the Watson IoT Platform cloud service platform launched by IBM. The platform incorporates artificial intelligence technology and has powerful cognitive analysis capabilities to provide data and analysis, prediction, and Rich module functions such as semantic recognition and storage.

artificial intelligence

The combination of artificial intelligence and manufacturing systems will be inevitable. Using algorithmic models such as machine learning, pattern recognition, and cognitive analysis, the ability of the plant control management system can be enhanced to achieve intelligent manufacturing. The entire intelligent manufacturing process mainly revolves around smart factories, and artificial intelligence plays an important role in smart factories. Artificial intelligence must research, analyze, and make decisions on the data collected by the Internet of Things, and apply them to specific industries. It is the key to achieving intelligence.

From the existing basic conditions, it is only possible to realize the intelligence of some fields, and it is difficult to carry out intelligent transformation from the entire enterprise level. Among them, the development level of sensors is one of the most important factors limiting the development of intelligent manufacturing. Due to the short board of sensors, it is difficult to ensure the fine management of the production site. The lack of detailed parameters makes it difficult for artificial intelligence to implement the real cause of the problem in the process of analysis and judgment.

Intelligent machine

Intelligent machines are an important part of smart manufacturing. Compared to robots that are still in a simple automation phase, these robots can operate autonomously and communicate directly with the manufacturing system to evaluate physical inputs and differentiate between different product configurations. Intelligent machines can solve problems and make decisions without the help of others. They can not only realize the tasks that were originally prepared, but also have artificial intelligence, which can learn from experience and master new skills.

Intelligent robots have the flexibility to configure themselves and perform new tasks, and they can respond quickly to demand changes and product innovations, which is more advantageous than traditional manufacturing processes. With the improvement of robot cognitive ability, robots can work together beside people, becoming an indispensable tool for humans to work efficiently in the future.

The realization of intelligent manufacturing is a large and complex project. No big technology, such as big data, Internet of Things, cloud computing, intelligent machines, artificial intelligence, etc., can realize the intelligent transformation of manufacturing industry. Therefore, experts in various technical fields are needed. Scholars have developed their own strengths and actively studied their expertise.

He specializes in the industry. Professor Wang Zhenmin, professor of South China University of Technology, director of Guangdong Province, and director of intelligent welding manufacturing equipment and robot engineering technology research center in Guangdong Province, has long been engaged in high-end manufacturing and robot research, and has undertaken various national fund projects and major projects in Guangdong Province. More than 40 topics have been published in more than 50 papers in IEEE ACCESS and other journals; 5 international PCT applications, more than 50 authorized patents, and 21 software copyright registrations, with profound theoretical foundation and practical experience in the field of intelligent robots.

Underwater robot welding and additive manufacturing are key core technologies that need to be developed in the national economic development and national defense security, and need to be supported by science and technology. They have the advantages of marine resources development, nuclear power equipment support, and homeland defense and marine rights protection equipment. Important engineering application value.

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Hammerhead Tower Crane Specification Table:


TYPE

QTZ63C

(TC5211)

QTZ63D

(TC5510)

QTZ63E

(TC5610)

QTZ63

(TC5013)

Rated lifting moment

 (KN.m)

630

630

630

630

Working range

 (m)

3~52

3~55

3~56

3~50

Load

Tip load

(t)

1.1

1

1

1.3

Max load

(t)

6

6

6

6

Lifting height

Free standing

(m)

41

41

40

40

Adhesion type  (m)

120

120

120

120

Lifting speed

Double rates

(m/min)

80/40/7

80/40/7

80/40/7

80/40/7

Fourfold rates

(m/min)

38.5/19.2/3.5

38.5/19.2/3.5

38.5/19.2/3.5

38.5/19.2/3.5

Min stable fall speed

(m/min)

≤7

≤7

≤7

≤7

Slewing speed

(m/min)

0-0.62

0-0.62

0-0.62

0-0.62

Trolley-travelling speed

(m/min)

20/40

20/40

20/40

20/40

Jacking speed

(m/min)

0.5

0.5

0.5

0.5

Balance weight

(t)

9.18

10.7

/

/

Upper chord of jib

(20#)

Φ102*8

Φ102*8

Φ102*8

Φ102*8

Mast section size

(mm)

1611*1611*2500

1611*1611*2500

1835*1835*2500

1835*1835*2500

Mast section material 

(Q235B)

∠160*16&

∠160*14

∠160*16

â–¡135*135*10

∠160*16

Used Tower Crane Liebherr 280ec H

Qtz63 Tc5610 6 Ton Used Tower Crane Manufacturers

Qtk25 Mini Self Erecting Tower Crane


Application Area:

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