Semiconductors are a key enabler for Smart Industry
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Learn moreIndustrial sensors, connectivity and PLCs
Smart industry is centred around industrial sensors added to equipment with both wired and wireless connectivity technologies, as well as more efficient power conversion technologies. Adding new power control systems can dramatically reduce the energy consumption of manufacturing systems, reducing costs and carbon emissions.
Updating programmable logic controllers (PLCs) to become part of the real-time systems is providing more visibility of the performance of the manufacturing systems. While PLCs have been a constant part of an industrial process control system, they have reduced in size and added connectivity to improve the real-time performance of industrial automation systems.
This is leading to the development of the smart factory that can be monitored in real-time and quickly reconfigured to meet the changing production requirements.
Industry 4.0: the next step for Factory Automation
Industry 4.0 is the next phase in factory automation, connecting up the equipment on the factory floor as part of the Industrial Internet of Things (IIoT). Being able to monitor and control the equipment from the cloud adds intelligence to the process without having to update the equipment. New equipment such as industrial robots can be added to the production facilities to enhance the efficiency of the process control. STMicroelectronics can help you make Industry 4.0 and Smart Industry a reality, today. Data from all the equipment, including robots, is collected and delivered to a database in the cloud for processing. This can provide a higher level of supervisory control via a data dashboard with a real-time display of the performance of the factory automation system, with alarms triggered if thresholds are exceeded.
Industry 4.0, enabler of predictive maintenance
But it goes further than that. Collecting data from the wireless sensor network and building a data model of the process not only provides opportunities for optimization and continuous improvement, but also allows for predictive maintenance.
Applying machine learning algorithms to the data models allows trends to be identified, highlighting the changes to the machine health. This data can be used to identify systems that may fail earlier than expected, allowing preventive maintenance to be planned in. This avoids failures that stop the production line, which can be expensive.
Industrial robotics: increased productivity, reliability and efficiency
Industrial robots increase the flexibility of factory automation systems. Two and three axis systems can be used to increase productivity.
Accuracy and repeatability are key measures for industrial robots. Repeatability is often the most important factor, ensuring that the same operations are delivered each time as part of the production system.
Industrial robotics uses different levels of motion control. Simple pick-and-place assembly requires repeatably in a limited number of pre-taught positions, while applications such as welding and finishing require continuous control with accurate orientation and speed.
Industrial robots can work closely with machine vision systems and machine learning to identify the correct positioning for the robot arms without the need for training. The machine learning algorithms can help improve the accuracy and repeatability of the process, boosting efficiency.
Direct-drive robots and actuators
Some industrial robots use direct drive systems, connecting directly to an electric motor rather than using a system of gears. Direct drive motors with the latest power control systems can increase the efficiency of the system by reducing the energy consumption and providing more precise control in 2 or 3 dimensions.
Robot arms with precise motors can deliver a wide range of flexible operations, replacing multiple pieces of equipment. This increases the reliability and efficiency of the production systems and can be linked to the industrial wireless sensor network. Robots and manipulators are key to help increase productivity and relieve humans from heavy, repetitive or dangerous tasks.
ST has a long-standing cooperation with leaders in the field of industrial robotics. That's why we can offer products and solutions across the entire Bill of Materials (BOM) for robotics applications, including:
- STM32 microcontrollers iNEMO modules,
- Inertial Measurement Units (IMU),
- STSPIN motor drivers,
- Transceivers and network processors ICs,
- Teseo GNSS positioning chips.
And many other devices and ICs, such as precision amplifiers, power transistors and battery management systems.
Wireless sensor networks: key components for IIoT
Adding sensors to industrial equipment is a key part of data acquisition in Industrial IoT, and is often achieved via a wireless sensor network.
A number of different wireless communication technologies can be used to deliver data from the sensor on the equipment back to a sensor gateway. This can range from Bluetooth to Wi-Fi and other industrial protocols such as the wireless version of the HART protocol.
Cloud connectivity for industrial automation
This is then linked back to the cloud to update the central database and provide a dashboard to a user anywhere in the world, with sophisticated data analysis and management tools. All of this provides the increased productivity and efficiency of the manufacturing systems and allows for preventative maintenance and increased availability of the factory automation system as part of Industry 4.0.
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