Top 10 profitable technologies to watch in manufacturing

We live in an era where new technologies or applications emerge almost every day. The question is whether manufacturers can see through the latest technological advances and find key application scenarios. Sometimes, even emerging technologies can be brought to market at the wrong time, such as VR/AR that "fever" after a burst of popularity. But as long as technology can gain a foothold in the industry, it has the potential to revolutionize the way manufacturers operate. So we've picked out the top 10 technologies for you to watch in 2020.

5G network

The real-time communication between the cloud platform and factory production facilities in the intelligent manufacturing process, as well as the information interaction between massive sensors and artificial intelligence platforms, and human The efficient interaction of the computer interface has extremely harsh and diverse requirements on the communication network, and it is necessary to introduce highly reliable wireless communication technology. In turn, 5G will give manufacturing equipment stronger flexible manufacturing capabilities, reducing the construction and maintenance costs of production line construction and transformation. For manufacturers who want to connect a large number of their devices to the Industrial Internet of Things and realize digital and even intelligent production, the application of 5G technology is crucial.

Industrial Internet of Things

Utilizing the vast amounts of data collected by sensors, the Industrial Internet of Things enables businesses to seamlessly connect devices from connected devices at near real-time speeds Data collection. Combined with other technologies mentioned in this article, such as edge computing, 5G, artificial intelligence, and machine learning, the Industrial Internet of Things can help companies use production data to realize product design, production, sales, inspection, diagnosis and maintenance, information statistics to scrapping. Full cycle information management. In addition, the mixed-flow manufacturing mode realized on the basis of the Industrial Internet of Things can also better meet the needs of flexible production.

Robots and Automation

In recent years, my country's industrial robot industry has entered a period of rapid growth and has been used in more and more fields in the manufacturing industry. However, the vast majority of domestic robots are used in low-end fields such as handling and palletizing, but the results are not obvious in the fields of multi-degree-of-freedom robots, interactive robots, and fully autonomous mobile robots. In the future, with the enrichment of application scenarios of collaborative robots, the application of robot technology will be further refined, coupled with the continuous maturity of artificial intelligence and machine learning technologies, industrial robots will also gain greater use.

On the other hand, with the development of 5G technology, the software application part will be separated from the robot side and migrated to the cloud computing platform through the wireless network, which greatly improves the processing power of the robot and is sufficient to cope with more complex applications. need.

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning technologies allow manufacturers to gain data-based insights about their operations. In recent years, my country's artificial intelligence technology research and industrial applications have developed rapidly, and it has been widely used in speech recognition, computer vision, robotics and other fields. The opportunities brought by this technology to manufacturers include: in the product development process, by incorporating artificial intelligence and machine learning modules, the system can independently design a large number of optional solutions according to the parameters input by the designer; in the production and manufacturing process, it can be Through machine vision technology, autonomous sorting and handling can be realized, and human collaboration can be realized in complex processes, and even predictive maintenance can be realized by building models; in the marketing process, users’ purchasing habits and product preferences can be analyzed based on machine learning models. Attributes carry out deep learning, make personalized recommendations to users, and provide relevant suggestions to the sales side.

UAV

The design concept of UAV was first used in the military field, but in recent years, the application of UAV technology in the civilian field has also achieved considerable development. In addition to consumer drones, the industrial application scenarios of industrial drones are also increasing, mainly focusing on security, inspection, and logistics delivery. In the future, the combination of manufacturing and production links may develop functions such as timely delivery of parts and operation monitoring, which will bring new opportunities for the optimization of the manufacturing production environment.

Wearable Devices

The growing variety of wearable devices brings more and more opportunities to manufacturers. At present, the application scenarios of wearable devices that have been developed include: providing visual assistance in training, manufacturing, maintenance, inspection, design and other processes to improve efficiency; monitoring employee status, and reminding unsafe operations and fatigue status; in restricted environments Improve productivity in the middle of the process (such as freeing hands in high-altitude work, and realizing keyboard-free input through gesture recognition); contact platforms or experts, expand large amounts of data, and obtain remote assistance. In the future, with the further integration of wearable devices as carriers with technologies such as artificial intelligence, machine learning, virtual reality and augmented reality, more uses will be developed.

3D printing and additive manufacturing

After the crazy expansion from 2014 to 2016, the growth rate of the domestic additive manufacturing market has increased significantly. slowed down, but still maintained a growth rate of 26.2% in 2018. In the future, with the rise of the experience economy, the demand for mass customization will also be driven, and the maturity of 3D printing and additive manufacturing technology means that manufacturers can use more new materials to meet this demand.

Since additive manufacturing technology does not have cost advantages in desktop-level applications and large-scale replication of simple processes, the current domestic additive manufacturing applications are mainly concentrated in the fields of high added value and high customization, including Industrial machinery, automobile manufacturing, aerospace and other fields, but due to the highly customized characteristics of medical materials such as prosthetics and implants, the future prospects of additive manufacturing in the biomedical market are also worthy of attention.

Edge Computing

With the continuous upgrading of production equipment, it is no longer realistic to always wait for data to be transmitted to the control center and then return to the command . Edge computing can provide data processing capabilities based on the principle of being close to the location of demand, and only transmit the remaining key information in the network, thereby meeting the needs of the manufacturing industry in terms of agile connection, real-time business, data optimization, and application intelligence. Adopting edge computing means businesses can even produce without an internet connection and decision-making delays, giving manufacturers greater flexibility.

Blockchain

Blockchain technology is mainly used to solve the problem of trusted transactions in non-secure environments. At present, the application of blockchain technology mainly focuses on four main points: distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. For the manufacturing industry, blockchain is especially suitable for use in supply chain scenarios to solve traceability problems. For example, aircraft manufacturer Airbus Group has used blockchain technology to analyze suppliers and the source of components, thereby effectively reducing the time and cost of repairing aircraft parts.

The current main application scenarios of blockchain in the manufacturing industry include anti-counterfeiting traceability, product life cycle management, supply chain management, collaborative manufacturing, etc. But overall, the current application of blockchain in the manufacturing sector is still in its infancy. In the future, with the maturity of cloud manufacturing scenarios, blockchain technology can also be used in global distributed manufacturing scenarios to solve problems such as standardized certification and order distribution, laying a solid foundation for the manufacturing industry to reach a new level.

Quantum computing

Although IBM, which launched the world's first commercial quantum computer, admits it will take time for commercial quantum computers to beat today's conventional computers. But IBM's recent report, Exploring quantum computing use cases for manufacturing, also optimistically predicts that combining quantum computing with manufacturing in the future will allow companies to solve problems that traditional computers can't handle. For example, in the field of new materials development, quantum computing can help manufacturers discover, design and develop materials with more favorable strength-to-weight ratios, higher energy density batteries, and more efficient synthesis and carbon capture for energy generation and carbon capture. Catalytic processes; in the control field, it can be used in semiconductor manufacturing to calculate more accurate results; in the design field, it will greatly speed up the analysis of building structures, fluid/aerodynamics and shorten the time required; it can also be used in the supply chain link to strengthen the machine Learned analytical skills to improve production line optimization and risk modeling capabilities.