Industrial automation refers to the technological integration that utilizes control systems
(such as programmable logic controllers and distributed control systems) and technical equipment
(including robots, sensors, actuators, and transmission systems) to accomplish industrial tasks—such
as manufacturing processes, material handling, inspection, and packaging—with minimal human
intervention. Its core objectives are to enhance efficiency, consistency, quality, and safety while
reducing operational costs.
Core Components
Sensing and Detection:
Function: Acquires information from the physical world (position, temperature, pressure, flow, images, weight, etc.).
Key Components: Various sensors (proximity switches, photoelectric sensors, temperature sensors,
pressure transmitters, vision systems, encoders, etc.).
Importance: Provides foundational data for decision-making within control systems.
Control and Decision-Making:
Programmable Logic Controllers (PLCs): Enable sequential and logical control for discrete manufacturing
(e.g., assembly lines).
Distributed Control Systems (DCS)/Process Control Systems: Monitor and regulate process variables in
continuous flow industries (e.g., chemical, pharmaceutical).
Motion Controllers: Precisely govern the trajectory, speed, and position of motors and mechanical devices.
Industrial Computers/Embedded Systems: Execute more complex control algorithms and human-machine
interface software.
Function: Receives sensor signals, performs calculations and judgments based on preset logic or algorithms,
and issues commands to actuators.
Key Components:
Actuation and Drive:
Industrial Robots: Perform complex, repetitive, or hazardous tasks like welding, painting, material handling,
and assembly. Collaborative robots are designed to safely share workspaces with humans.
Drive Systems: Motors (servo motors, stepper motors, variable frequency motors) and their controllers provide power.
Actuators: Pneumatic/hydraulic cylinders, valves, relays, frequency converters, etc., convert control signals
into mechanical motion or process regulation.
Conveying and Handling Systems: Conveyors, automated guided vehicles (AGVs), hoists, etc., facilitate
material movement between workstations.
Function: Receives control commands to generate physical actions or alter process states.
Key Components:
Communication and Connectivity:
Function: Enables data exchange between equipment, controllers, and information systems.
Key Technologies and Protocols: Fieldbus, Industrial Ethernet, OPC UA, Time-Sensitive Networking (TSN),
etc., ensuring real-time, reliable information transmission.
Human-Machine Interaction and Monitoring:
Function: Provides interfaces for operators to monitor production processes, input commands, and view
alarms and historical data.
Key Components: Human-machine interfaces (HMIs), Supervisory Control and Data Acquisition (SCADA) systems,
Manufacturing Execution Systems (MES) for managing the production execution layer.
Primary Application Areas (Examples)
Discrete Manufacturing: Automotive assembly, electronics assembly, machining, metal fabrication.
Process Industries: Petrochemicals, pharmaceutical production, food and beverage processing, water treatment.
Material Handling and Warehousing: Automated storage and retrieval systems, sorting systems, packaging lines.
Quality Inspection: Automated visual inspection, dimensional measurement, defect detection.
Equipment Control: CNC systems for machine tools, printing machinery control, textile machinery control.
Current Developments and Trend Directions
Integration and Interconnectivity:
Increasing system integration across equipment, production lines, and factory levels to eliminate “information silos.”
Emphasis on open standards and unified communication architectures (e.g., OPC UA over TSN) to enable
seamless collaboration between equipment from different manufacturers.
Data-Driven Intelligence Enhancement:
Leveraging vast amounts of equipment operational data and process parameters for deeper analysis.
Objectives include: Predictive maintenance (based on equipment condition rather than fixed cycles), process
optimization (real-time parameter adjustments to boost yield), energy efficiency management, quality traceability
and analysis.
(Note: This refers to rule- or model-driven optimization using data itself, avoiding specific AI technology terminology)
Flexibility and Modularity:
To meet demands for small-batch, high-variety production, automation systems require greater adaptability
and reconfigurability.
Modular design and plug-and-play concepts facilitate rapid adjustments to production line layouts and functionalities.
Enhanced Collaboration and Safety:
With the increasing prevalence of human-robot collaboration, advanced safety sensing technologies (e.g., safety
laser scanners, area-scan sensors) and inherent safety designs in collaborative robots are essential.
Safety standards (e.g., functional safety) are gaining increasing importance in automation system design.
Enhanced Software Capabilities:
Industrial software (e.g., advanced process control, manufacturing execution systems, digital simulation tools)
plays a prominent role in boosting overall automation system performance and planning efficiency.
Increased adoption of virtual commissioning technology
reduces on-site debugging time.
In summary, industrial automation serves as the core technological pillar for building modern, efficient, reliable,
and secure manufacturing systems. Through the seamless integration of hardware (sensors, actuators, controllers,
robots) and software (control logic, communication protocols, human-machine interfaces, management systems),
it enables precise, continuous, and efficient control and management of production processes. Its development
continues to focus on deeper system integration, enhanced data utilization capabilities, greater flexibility and
adaptability, and safer human-robot collaboration.