Demand on the consumer side is changing more rapidly, and the challenges facing the manufacturing industry are becoming more complex. Today, smart manufacturing uses sensing technology and combined with AI algorithms to improve information visibility and system controllability. Under the application of Cyber-Physical System, collaborative robots, digital twins, and predictive maintenance are smart manufacturing development focus of the industry.
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Collaborative Robot
In the past, traditional large industrial robots had high barriers to entry, high investment costs, and long payback periods. In response to this problem, collaborative robots were gradually developed to replace large industrial robots.
Collaborative robots are relatively low-cost, highly flexible, and easier for enterprises to deploy without requiring too much additional protection or software suites to meet the changing needs of the manufacturing process. Collaborative robots are suitable for introduction into highly repetitive tasks but do not need to be too difficult for on-site work. Collaborative robots can also improve safety.
Collaborative robots have a better price advantage and are also intelligent production that more SMEs are willing to adopt, which also drives the market for collaborative robots. Collaborative robots are mainly used in industries with short design cycles and high product variability, such as the automotive industry and electronics manufacturing that demand automation flexibility.
- Universal Robots
Universal robots have the highest market share in the Cobot industry. Through automated manufacturing solutions, it solves the labor shortage problem in the manufacturing industry, and actively develops and adopts robotics for dirty, dull and dangerous 3D work. Market, targeting China, India and Southeast Asian markets with large labor demand.
- Techman Robot (TM ROBOT)-Quanta Computer Inc.
TM ROBOT was first sold in 2016 and became the second-largest market share in the global collaborative robot market in 2018. TM ROBOT focuses on machine vision technology, uses visual inspection to automatically correct object position offsets, and works with smart interface Allows personnel to quickly and easily operate to follow the work progress and monitor the operation of the robot arm in time. TM ROBOT deepens the application in various industries by accurately identifying objects, and also expands the production system of smart vision.
- Traditional Industrial Robots Enter the Market For Collaborative Robots (Cobot)
Many major industrial automation and robotics manufacturers are also investing in the development of collaborative robots. For example, ABB launched the Yumi single-arm and dual-arm Cobot. Compared with previous industrial robots, it is more flexible and lowers cost. Its Cobot can connect networks and devices. It can monitor and control work more accurately and use data-driven analysis to improve operational performance, reliability and service life. ABB also introduced a monitorable infinite sensor, which can be evaluated by monitoring the degree of vibration and vibration status. On the other hand, it also allows maintenance personnel to monitor remotely without having to approach the dangerous bearing shaft.
KUKA, another global robotics brand, is developing.
Human-Robot Augmentation (HRA), which is an extension of human workers, improves accuracy and sensitivity. Through machine learning, it can interact with humans in real-time, respond to changing tasks, and install components faster and more accurately and safely.
- Intense Industry Competition
With the breakthrough of sensors and machine vision technology, combined with AI, Cobot continues to improve technology and develop in the direction of safety and ease of use. Cobot is not a replacement of humans. It can cooperate with humans and machines; complement each other and work together to complete job requirements.
To help increase productivity.
In contrast, a large number of resources continue to invest in R&D automation skills and Cobot, which also makes the industry more intense.
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Digital Twins, Real-Time Integration
Digital twins are virtual copies of devices, processes, and systems collected through sensors, and are one of the new strategic technologies. The digital twins establish a connection between the physical model and the virtual model, monitor and return data in real-time through the sensor, further analyze and judge, and feedback to enable the virtual model to be continuously optimized to achieve real-time virtual-real integration.
At present, this technology is also applied in various industries, such as monitoring patient flow in the emergency room in the medical field, remote monitoring in the building field, real-time monitoring and transmission and distribution regulation in the energy field, etc., thereby more efficient management and reduction of costs.
In terms of manufacturing, digital twins are used for production, design, and product development, so that the product development cycle can be reduced to respond to the trend of customization and miniaturization, and create more suitable manufacturing plans and precise production control. Optimize the overall manufacturing process. Besides, in the transformation of industrial automation, the establishment of the Internet of Things also allows the technology of digital twins to be more accurate and achieve smart manufacturing. Digital twins used in product design allow manufacturers to adjust product designs in a virtual environment and test and verify product functionality, safety, and quality before the product is officially launched.
The digital twins on the production side focus on virtual adjustment to implement the factory's digital and automation. Also, the digital twins on the operation side use the data of the entire production line to predict the failure through simulation to reduce the risk of energy consumption or downtime.
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Predictive Maintenance To Reduce Costs And Increase Production
Predictive maintenance is an important technology in smart manufacturing. By analyzing production data and real-time monitoring of equipment operation to optimize maintenance plans, it can effectively prevent accidents such as downtime, reduce maintenance costs, and increase plant operating time to increase production.
Today, most factories operate around the clock, and the relative downtime costs continue to increase. In the event of unexpected downtime, high costs will be lost. Optimizing downtime planning through predictive maintenance minimizes the risk of unplanned downtime and extends equipment life. Therefore, this technology has become one of the important topics in smart manufacturing.
Predictive maintenance is built on the foundation of the Internet of Things. The basic conditions include data collection sensors installed in each device to monitor and transmit data to the central control center to store and analyze calculation data to achieve the function of prediction and maintenance.
The necessary condition for smart manufacturing is the establishment of the Industrial Internet. The data on the Internet includes the historical signals stored and the data collected at all times. With the improvement of technology and accuracy, digital twins and predictive maintenance are becoming more and more important, which can be used as an important basis for decision-making. This technology also needs to be considered together when planning the equipment architecture to achieve wisdom manufacturing capabilities.
