Intelligent manufacturing represents the deep integration of digital technology with traditional manufacturing processes. At its core lies physical artificial intelligence (AI), which introduces AI algorithms into physical systems such as robotic arms, automated guided vehicles (AGVs), and computer numerical control (CNC) machine tools. For these physical systems to operate effectively, they rely on real-time data from the physical environment, which is precisely where sensors come into play.
Industrial sensors, acting as the “eyes and ears,” have become indispensable cornerstones in modern manufacturing and automation. They have evolved from basic measuring devices into sophisticated systems capable of comprehensive monitoring of discrete and process automation. When combined with AI (visual AI, acoustic AI, or physical AI), industrial sensors enable physical systems to possess self-learning capabilities, thereby enhancing manufacturing efficiency, safety, and facilitating the application of digital twin technology and data analysis. This article will delve into various types of sensors and their applications.
Understanding Industrial Sensors and Their Working Principles
Industrial sensors are devices that sense and process various physical parameters such as distance, pressure, temperature, flow, liquid level, motion, speed, acceleration, etc. The data they collect is crucial for monitoring and controlling manufacturing processes. The collected data is then sent to programmable logic controllers (PLCs) or CNC machines via digital/analog input/output (I/O) and communication modules.
In a typical industrial sensor, the main components (see Figure 1) include sensing elements, voltage references, operational amplifiers (OpAmps), analog-to-digital converters (ADCs), processors, interfaces, and power management modules. The sensing elements are responsible for measuring physical parameters and converting them into electrical signals such as voltage, current, or resistance.
Components of a Typical Industrial Sensor
Industrial sensors serve as the interface between AI and the physical world, much like the nervous system in the human body. When the digital world needs to interact with the physical environment, analog and mixed-signal sensors are indispensable. Next-generation industrial automation systems will not only incorporate AI capabilities in the information technology (IT) cloud but also embed AI into operational technology (OT) devices deployed on-site. In other words, AI algorithms will be applied to sensors or PLCs/robots at the edge to enable rapid decision-making. Here, we will focus on sensors used in industrial automation.
Types of Sensors
Image (Visual) Sensors:
Image sensors capture images and videos of products in production using cameras to determine their presence, orientation, and accuracy. They are indispensable devices in quality control and inspection processes. With a single device, image sensors can inspect multiple points on a product, providing strong support for machine vision.
Block Diagram of a Machine Vision System
Position and Torque Sensors:
Although Hall effect sensors, force sensors, and optical sensors have been applied in motor position control, the overall cost of these solutions remains high due to the complex manufacturing processes of sensing elements such as magnets and precision resistors, as well as optical encoders.
Nowadays, a new trend is emerging: inductive position sensors are being adopted in motion control and robotics applications. These sensors use printed circuit boards (PCBs) with windings as sensing elements, paired with analog front-ends (AFEs) and controllers.
Ultrasonic Sensors:
Ultrasonic sensors measure distance using ultrasonic waves and are well-suited for detecting transparent objects (unlike optical sensors) and are unaffected by dust and dirt.
Photoelectric Sensors:
Photoelectric sensors detect objects using light and are classified into three types: through-beam, retro-reflective, and diffuse-reflective, each with distinct characteristics and application scenarios. Photoelectric sensors offer significant advantages such as non-contact detection, applicability to almost all materials, and support for long-distance line-of-sight detection. They primarily employ infrared and laser technologies.
Proximity Sensors:
Proximity sensors, based on electromagnetic induction principles, can detect metal objects without physical contact and exhibit strong tolerance to environmental factors such as dust and oil. For detecting non-metal objects, ultrasonic and photoelectric technologies are preferred solutions.
Pressure Sensors:
Pressure sensors are used in pneumatic, hydraulic, or cleanroom environments to maintain ideal operating conditions and alert to deviations. They typically use strain gauges or piezoresistors as their core components and employ Wheatstone bridge structures to cancel out errors, measuring pressure through minute voltage changes.
Temperature Sensors:
Temperature sensors are used to monitor and regulate temperature across various industries, including food processing and machinery operation. Common types include thermocouples, resistance temperature detectors (RTDs), and semiconductor temperature sensors.
Environmental Sensors:
Environmental sensors such as gas sensors and chemical sensors are used to monitor specific toxic or flammable gases in environments requiring vigilance and are often integrated into safety systems. For example, rain and light sensors, initially designed for automotive applications, can identify particulate matter in the environment by emitting and measuring reflected light using photodiodes. Another example is electrochemical sensors for continuous glucose monitoring (CGM). When paired with Bluetooth microcontrollers, they can measure minute changes in chemical currents with extremely low system power consumption.
Key Considerations for Sensor Selection
When selecting sensors for physical AI systems in intelligent manufacturing, five key factors should be prioritized:
- Accuracy and Speed Required by the Application: Sensors must meet the accuracy and speed requirements of specific AI tasks, such as real-time quality inspection, predictive maintenance, and robot control.
- Data Quality and Reliability: Sensors generate vast amounts of data over time, and AI can analyze this data to uncover patterns. Sensors that consistently provide reliable data are crucial for training and running AI models, laying a solid foundation for agile decision-making throughout the manufacturing ecosystem.
- Interoperability and Integration: Sensors should seamlessly integrate with existing manufacturing systems and support standard fieldbuses and communication protocols. In other words, new AI sensors must be miniaturized and interoperable.
- Cybersecurity and Data Privacy: As the number of connected sensors increases, the risk of cyber threats also rises, imposing stricter requirements on OT and IT security. Ensuring the security of data transmission at the edge is vital, especially when AI systems rely on sensitive operational data provided by sensors for decision-making. Sensors with self-calibration and redundancy functions can be used to detect and isolate threats.
- Sustainability and Energy Efficiency: When expanding the number of sensors in physical AI systems, operators still need to keep power consumption within budget. Sensors with low operating currents offer significant advantages in terms of system scalability.
In conclusion, industrial sensors are fundamental components of physical AI systems, enabling them to perceive, understand, and interact with the real world at the edge and in the cloud. As AI continues to evolve, advancements in sensor technology will be key to fully unlocking AI’s potential and developing smarter and more adaptable systems in the field of intelligent manufacturing.
