6 Types of Automation: A Comprehensive Guide for Engineers

Everything you need to know about various types of automation and their role in modern manufacturing systems.

Muhammad Sufyan

Last updated on 17 Jul, 2024. 17 min read

Automation and Industry 4.0 are revolutionizing the manufacturing and industrial sectors.

Topic

Manufacturing

Automation and Robotics - Is there a Difference?

In industrial terms, automation and robotics are often used together and are closely related to some extent. However, there are some critical differences between the two, which are critical for engineers to ensure optimal applications of both. Here is a quick summary:

Robotics research focuses on adaptability and intelligence while automation research focuses on productivity, efficiency and reliability of autonomous systems.
Robotics mainly uses machines, that sense, and actuate with various degrees of autonomy, while automation also includes software agents.

Simply put, robotics can be considered as one of the sub-categories of automation.

Six Types of Industrial Automation Systems

Within the context of industrial applications for automated processes, there are six types of automation. Let’s take a look at what each kind of automation is.

Fixed Automation: High-Volume Production Systems

Fixed automation, is a production system designed for high-volume manufacturing of standardized products. It is characterized by custom-engineered equipment arranged in a fixed sequence of operations. Fixed automation systems typically feature high production rates, ranging from hundreds to thousands of units per hour, with limited flexibility for product variations.

The design of fixed automation systems involves intricate control systems, often using relay logic or hardwired controls. These systems employ a combination of mechanical, electrical, and pneumatic components to achieve precise and repetitive operations. The control architecture is typically centralized, with a master control unit orchestrating the entire production sequence.

Examples of fixed automation in manufacturing include:

Bottling plants: High-speed filling and capping machines
Automotive assembly: Automated welding lines for car body assembly
Food processing: Continuous flow production lines for packaged goods

Fig 2: Fixed automation in an EV manufacturing plantCaption

Advantages of fixed automation:

High production rates and efficiency for large volumes
Consistent quality due to precise, repeatable operations
Lower unit costs for high-volume production
Limitations of Fixed Automation
High initial investment in custom equipment
Limited flexibility for product changes
Vulnerability to complete system shutdowns

Programmable Automation: Versatility in Manufacturing

Programmable automation is a flexible manufacturing approach that uses reprogrammable equipment to produce a variety of products. Unlike fixed automation, such a system is characterized by its ability to change production parameters through software modifications. For example, while fixed automation might only be capable of moving from A to B, a programmable automation technology could be programmed to move from A to B, and then subsequently be programmed to move from A to C.

Hence, it enables manufacturers to adapt to different product specifications and production volumes.

Key features include batch processing capabilities, allowing for the production of multiple product variants in scheduled batches.

At the heart of programmable automation systems are Programmable Logic Controllers (PLCs). These robust industrial computers consist of several key components:

CPU: Executes the control program, typically ranging from simple 8-bit microprocessors to advanced 64-bit multi-core processors.
Memory: Organized into program memory (ROM or Flash) for storing the control logic, and data memory (RAM) for dynamic data and I/O states.
I/O System: Interfaces with field devices, including digital and analog inputs/outputs, and communication modules for network connectivity.

PLC programming utilizes several IEC 61131-3 standard languages:

Ladder Logic: A graphical language resembling relay logic diagrams, widely used for its intuitive representation of control circuits.
Function Block Diagram (FBD): A graphical language using interconnected function blocks, ideal for process control and data manipulation.
Structured Text (ST): A high-level text-based language similar to Pascal, suitable for complex algorithms and data processing.

Further Reading: What is a PLC (Programmable Logic Controllers): A Comprehensive Guide

Programmable automation finds applications across various engineering fields:

Food and Beverage: Batch processing control in brewing, dairy production, and packaged food manufacturing.
Pharmaceuticals: Precise control of drug manufacturing processes, ensuring compliance with regulatory standards.

Fig 3: A pharmaceutical production line typically uses flexible automation

Batch processing in programmable automation involves the production of finite quantities of products according to a specific recipe or formula. The ISA-88 standard provides a framework for batch control, defining models and terminology for batch manufacturing plants and control systems. Key concepts include:

Recipe Management: Storing and managing product recipes, including ingredients, quantities, and process parameters.
Batch Scheduling: Organizing production batches based on demand, resource availability, and equipment capacity.
Process Segmentation: Breaking down the manufacturing process into modular, reusable units of operation.

Flexible Automation: Adapting to Product Variations

Flexible automation is a manufacturing approach that combines automation efficiency with the adaptability to produce a variety of products or product variations. It is crucial in modern manufacturing, where product lifecycles are shorter and customization demands are higher. It refers to manufacturing equipment that is controlled by a computer program and is capable of carrying out a diversity of tasks based on the code it has been fed. Unlike programmable automation, which can be programmed for different products but requires significant down-time to do so, flexible automation solutions can be adapted rapidly and often automatically.

Flexible Manufacturing Systems (FMS) are the cornerstone of flexible automation, comprising several key components:

Automated Storage and Retrieval Systems (AS/RS): High-density storage systems that automatically store and retrieve materials, components, and finished products.
Automated Guided Vehicles (AGVs): Self-propelled vehicles that navigate manufacturing floors using various guidance technologies such as magnetic strips, lasers, or vision systems.
Computer Numerical Control (CNC) Machines: Precision machine tools controlled by computers, capable of performing complex machining operations. Modern CNC machines often integrate multiple axes of motion and tool changers, allowing for the production of diverse part geometries with minimal setup changes.

Robotics plays a pivotal role in flexible automation:

Robot Kinematics: The study of robot motion without considering forces, crucial for precise positioning and path planning.
End Effectors: Specialized tools attached to robot arms, designed to interact with workpieces.
Programming Methods: Modern robots use teach pendants for manual programming, offline programming software for complex path planning, and increasingly, AI-driven adaptive programming for real-time adjustment to product variations.

Computer-Integrated Manufacturing (CIM) concepts are integral to flexible automation, encompassing:

Enterprise Resource Planning (ERP) integration for production scheduling and resource allocation
Manufacturing Execution Systems (MES) for real-time production monitoring and control
Product Lifecycle Management (PLM) for managing product data across the entire lifecycle

Examples of flexible automation in industries include:

Automotive: Body shops with reprogrammable welding robots capable of adapting to different car models on the same production line.
Electronics: Surface Mount Technology (SMT) lines that can quickly switch between different PCB designs and component placements.
CNC Machining: Machines can be programmed to design different layouts for metal cutting, PCB design and etching.

Integrated Automation: Holistic System Approach

Integrated automation is a comprehensive approach to manufacturing and process control that interconnects various subsystems and components to create a cohesive, efficient production environment. It refers to an end-to-end automated manufacturing process that does not require any human intervention. In an integrated automation workflow, a computer software system controls and connects the various pieces of manufacturing equipment on the production floor. Once settings and parameters have been set by an engineer or technician, the integrated automation solution carries out the instructions independently and can respond if any inconsistencies occur thanks to artificial intelligence and machine learning technologies.

This holistic strategy is crucial for achieving high Overall Equipment Effectiveness (OEE), a key performance metric that combines availability, performance, and quality factors. By integrating disparate systems, manufacturers can optimize resource utilization, reduce downtime, and enhance product quality.

System integration in automation relies heavily on industrial communication protocols that enable seamless data exchange between different devices and systems. Key protocols include:

Modbus: A simple, robust protocol widely used in industrial applications, particularly in process control.
Profibus: A fieldbus standard for high-speed data communication in manufacturing and process automation.
EtherNet/IP: An industrial network protocol that adapts Ethernet for use in industrial environments, offering real-time control and data collection capabilities.

Integrated automation finds application in various industries:

Process Industries:

Oil and Gas: Integrated control systems manage complex refining processes, linking field devices to centralized control rooms.
Chemical Manufacturing: Batch control systems integrate with supply chain management for optimized production scheduling.

Smart Factories:

Automotive Assembly: Integrated systems coordinate robotic welding, painting, and assembly operations with material handling and quality control.
Electronics Manufacturing: Integrated automation manages component placement, soldering, and testing in PCB production lines.

Digital twins are virtual representations of physical assets or processes in integrated automation systems. They serve several critical functions:

Real-time Monitoring: Digital twins provide a live view of system performance, enabling proactive maintenance and optimization.
Predictive Analysis: By simulating various scenarios, digital twins help predict system behavior and optimize operational parameters.
Design Optimization: Engineers can use digital twins to test and refine system designs before physical implementation.
Training: Operators can be trained on digital twins, reducing risks associated with training on live systems.

Fig 4: Robotic arms are key to unlocking integrated automation on the factory floor.

Lights-out manufacturing is an example of integrated automation in action. It refers to when a manufacturing facility can operate without any human involvement and even supervision. Robots and automated equipment are responsible for every step in the process, as well as material and part handling. Today, many manufacturers are aiming to achieve lights-out production to increase productivity, achieve high production rates, and minimize the reliance on skilled human operators—of which there is often a shortage.

CNC machining close-up Fig 5: Lights-out manufacturing is a fully automated production line that requires no human intervention or supervision.

Soft Automation: Software-Driven Process Control

Soft automation refers to the use of software systems to control, monitor, and optimize industrial processes and engineering workflows. It plays a crucial role in modern engineering by enabling flexible, adaptable, and intelligent control solutions.

Virtual commissioning, a key concept in soft automation, allows engineers to design, test, and validate automation systems in a virtual environment before physical implementation, significantly reducing commissioning time and risks.

Software automation tools and platforms form the backbone of soft automation:

Integrated Development Environments (IDEs): Specialized software suites that provide comprehensive facilities for automation software development. Examples include Siemens TIA Portal and Rockwell Automation Studio 5000, which offer integrated programming, simulation, and debugging capabilities.
Version Control Systems: Tools like Git and SVN enable collaborative development and version management of automation software, crucial for maintaining complex control systems over time.
Simulation Platforms: Software like MATLAB Simulink and Emerson DeltaV Simulate allow engineers to model and simulate complex processes and control strategies.

Software engineering principles are increasingly applied to automation, enhancing reliability and maintainability:

Modular Programming: Breaking down complex control systems into smaller, reusable modules, facilitating easier maintenance and updates.
Object-Oriented Concepts: Applying principles like encapsulation and inheritance to create more structured and scalable automation software.
Design Patterns: Utilizing established software design patterns to solve common automation challenges efficiently.

Soft automation finds application across various engineering domains:

Engineering Design: Computer-Aided Design (CAD) software like AutoCAD and SolidWorks automate complex design tasks and enable parametric modeling.
Simulation: Finite Element Analysis (FEA) tools like ANSYS automate structural and thermal analysis of engineering designs.
Project Management: Software like Primavera and Microsoft Project automate scheduling, resource allocation, and progress tracking in large-scale engineering projects.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing soft automation capabilities:

Predictive Maintenance: AI algorithms analyze sensor data to predict equipment failures before they occur, optimizing maintenance schedules and reducing downtime.
Adaptive Control: ML-based control systems can adapt to changing process conditions in real-time, optimizing performance beyond traditional PID control capabilities.
Process Optimization: AI techniques like reinforcement learning can discover optimal control strategies for complex, multi-variable processes.
Anomaly Detection: Machine learning models can identify unusual patterns in process data, alerting operators to potential issues or quality deviations.
Natural Language Processing: Enabling more intuitive human-machine interfaces in automation systems, facilitating easier configuration and troubleshooting.

Hard Automation: Mechanical and Electronic Control Systems

Hard automation refers to the use of dedicated, fixed-function machines and control systems to perform specific tasks in manufacturing and process control.

Unlike soft automation, which relies on flexible, software-driven solutions, hard automation systems are designed for high-speed, repetitive operations with limited variability. These systems excel in high-volume production environments where consistent, precise operations are critical.

The technical components of hard automation systems include:

Sensors: Devices that detect physical properties and convert them into electrical signals.
- Photoelectric sensors: Use light beams to detect object presence or absence.
- Inductive sensors: Detect metallic objects using electromagnetic fields.
- Capacitive sensors: Detect both metallic and non-metallic objects through changes in capacitance.
Actuators: Devices that convert control signals into physical action.
- Pneumatic actuators: Use compressed air for linear or rotary motion.
- Hydraulic actuators: Employ pressurized fluid for high-force applications.
- Electric actuators: Utilize electric motors for precise positioning and speed control.
Control Units: Devices that process sensor inputs and control actuator outputs.
- Programmable Logic Controllers (PLCs): Industrial computers that execute control logic.
- Microcontrollers: Compact, integrated circuits for embedded control applications.
- Dedicated control circuits: Custom-designed electronic circuits for specific control functions.

Control system architectures in hard automation typically involve:

Feedback Control Loops: Monitor system output and adjust inputs to maintain desired performance.
- PID (Proportional-Integral-Derivative) control: A widely used algorithm for precise control.
- Cascade control: Uses multiple nested feedback loops for complex processes.
Feedforward Control: Anticipates and compensates for known disturbances before they affect the system.
- Model-based feedforward: Uses process models to predict and counteract disturbances.
- Adaptive feedforward: Adjusts compensation based on measured disturbances.

Examples of hard automation in industry include:

Manufacturing: High-speed bottling lines with dedicated filling, capping, and labeling machines.
Automotive: Robotic welding cells in car body assembly, programmed for specific weld patterns.
Electronics: Pick-and-place machines for rapid, precise component placement on PCBs.
Process Control: Continuous flow chemical reactors with dedicated temperature and flow control systems.

Integration of hard automation with other automation types often involves:

Edge Computing: Deploying local processing units near hard automation systems to enable real-time data analysis and decision-making.
IoT Connectivity: Equipping hard automation systems with network-enabled sensors and controllers for remote monitoring and control.
SCADA Integration: Connecting hard automation systems to Supervisory Control and Data Acquisition systems for plant-wide monitoring and control.
MES Interface: Linking hard automation systems with Manufacturing Execution Systems for production scheduling and quality control.

Implementing Automation: Strategies for Success

Selecting the Right Type of Automation

Selecting the appropriate type of automation for a specific engineering task requires a systematic approach that considers both technical and economic factors. A comprehensive framework for assessment should include the following steps:

Process Analysis: Thoroughly analyze the current process, identifying bottlenecks, variabilities, and potential areas for improvement.
Requirements Definition: Clearly define the objectives of automation, including desired throughput, quality standards, and flexibility requirements.
Technology Assessment: Evaluate available automation technologies against the defined requirements, considering factors such as technical feasibility, scalability, and integration capabilities.
Cost-Benefit Analysis: Conduct a detailed financial analysis, including initial investment, operational costs, and expected benefits.
Risk Assessment: Identify potential risks associated with each automation option and develop mitigation strategies.

Factors to Consider While Implementing an Automation System

When choosing between different automation types, several key factors must be considered:

Production Volume: High-volume, standardized production often favors fixed automation, while lower volumes with higher variability may benefit from flexible or programmable automation.
Product Variety: Greater product variety typically requires more flexible automation solutions, such as robotics or reconfigurable manufacturing systems.
Process Complexity: Complex processes with multiple variables may require integrated automation systems with advanced control capabilities.
Skill Level of Workforce: The availability of skilled personnel can influence the choice between more automated systems and those requiring greater human intervention.
Future Scalability: Consider potential future changes in production requirements and select automation solutions that can adapt or scale accordingly.

Integration and Interoperability Challenges

Integrating different types of automation systems presents numerous challenges in modern industrial environments. Protocol incompatibilities often arise when connecting legacy systems with newer technologies, as older systems may use proprietary protocols that are not easily translatable to modern standards. Data format issues compound this problem, with disparate systems using different data structures, semantics, and encoding methods.

To address these challenges, several strategies for ensuring interoperability have emerged:

OPC UA (Open Platform Communications Unified Architecture): This platform-independent standard provides a robust framework for industrial interoperability. OPC UA offers a unified address space model, allowing seamless integration of diverse systems and devices.
Middleware Solutions: Custom-developed or off-the-shelf middleware acts as a translation layer between incompatible systems. Examples include Apache Kafka for real-time data streaming and RabbitMQ for message queuing.
API-First Approach: Designing systems with well-documented APIs from the outset facilitates easier integration and future scalability.
Containerization: Using technologies like Docker allows for consistent deployment of applications across different environments, reducing integration complexities.

The Industrial Internet of Things (IIoT) platforms offer enhanced integration capabilities by providing a unified framework for connecting, managing, and analyzing data from diverse industrial devices and systems. Key aspects of IIoT implementation include:

Edge Computing: Deploying edge devices to process data near its source, reducing latency and bandwidth requirements while enabling real-time decision-making.
Cloud Integration: Utilizing cloud platforms (e.g., AWS IoT, Azure IoT) for scalable data storage, advanced analytics, and remote monitoring capabilities.
Digital Twin Technology: Creating virtual representations of physical assets and processes to enable simulation, optimization, and predictive maintenance.

Skills and Training for Automation Implementation

The field of automation engineering demands a diverse and evolving skill set. Modern automation engineers must be proficient in multiple programming languages, including ladder logic for PLCs, high-level languages like Python and C++ for advanced control algorithms, and scripting languages for data analysis and visualization.

Expertise in network protocols such as Modbus, Profinet, and OPC UA is crucial for system integration. Additionally, a strong foundation in system architecture design is essential for creating scalable and efficient automation solutions.

Cross-disciplinary knowledge is paramount in automation engineering. Mechanical engineering principles are necessary for understanding the physical systems being automated, including kinematics, dynamics, and material properties.

Electrical engineering knowledge is critical for working with sensors, actuators, and power systems. Software engineering concepts such as object-oriented programming, version control, and agile methodologies are increasingly important as automation systems become more software-driven.

Suggested Reading: How to Launch Agile Automation

Advantages and Disadvantages of Automation

Automation is a key part of what is heralded as “Industry 4.0”, or the Fourth Industrial Revolution. Through a combination of smart machines, control systems, and automation tools, manufacturing operations are able to function more independently, with various steps in the process communicating with each other and process data informing computer system decisions in real-time.[3]

Whenever we talk about a new manufacturing or industrial framework, there are two sides to the story. Automation, particularly integrated automation, represents a massive paradigm shift in manufacturing that has many people feeling very optimistic and many feeling quite anxious. It’s important to look at both sides to understand where the benefits and disadvantages lie, and how to address them.

Advantages:

Productivity: One of the main reasons that manufacturers are investing increasingly in automation is because it can unlock higher productivity rates. With automation, tasks throughout the production chain—or the chain as a whole—can operate independently and with greater efficiency.
Consistency: With automated technologies in place, manufacturers can benefit from greater process consistency thanks to in-depth process monitoring data. A lower reliance on human labor also minimizes the risk of human error in the production process.
Labor: This is admittedly a tricky one (you’ll also find it in the disadvantages section). There are benefits for both employers and employees when it comes to industrial automation. For one, automation enables manufacturers to mitigate the effects of labor shortages, particularly for skilled technicians. They can keep up production rates and even scale without having to seek out and hire additional workers. For another, automation eliminates the need for humans to undertake many tedious, repetitive tasks. This creates more space for more complex and valuable tasks that require problem solving, such as product development, process supervision, and big-picture strategizing.

Disadvantages:

Cost: While there are long-term cost benefits for integrating automation in production workflows, the transition to automation and Industry 4.0 comes with a significant initial cost due to the high price of automated equipment and software tools. This cost can be prohibitive to smaller producers, and may only be viable for large manufacturing companies.
Labor: On this side of things, automation threatens existing labor forces and can lead to worker displacement. If a whole segment of jobs is suddenly automated, those workers are at risk of losing their employment and their skills may be devalued. Upskilling and trainings are an important strategy for mitigating this effect. They equip workers for new roles and give them new skills that are in demand.
Cybersecurity: As processes across manufacturing become more connected, they become more efficient but they also increase their vulnerability to cyber attacks. Malware, ransomware, and data breaches can compromise business processes significantly and come with a high cost. Establishing a robust cybersecurity system can help to minimize these risks and keep IP and production chains safe as automation is implemented.

Recommended reading: Is Your Manufacturing Plant Ready for Automation?

Key Takeaways

At its core, automation is about improving productivity and reducing the need for human labor. And it has been evolving for millenia. Thousands of years ago, water and wind mills freed people up from the labor intensive and time-consuming task of hand pounding wheat and grain. Today, the most advanced automated solutions are enabling entire production chains to go ahead without supervision. We covered a lot about automation in this article, if you’re looking for a quick summary, here it is:

There are six main types of automation.
The main advantages of automation are higher productivity and output consistency.
The main disadvantages of automation are high initial cost and worker displacement.
Labor is a two-sided issue when it comes to automation: on the one hand, employers can mitigate labor shortages and workers are relieved from tedious, repetitive work. On the other hand, automation can replace jobs and lead to layoffs and short term unemployment issues.

Frequently Asked Questions (FAQs)

How does the implementation of integrated automation systems affect cybersecurity in industrial environments?

Integrated automation systems increase the attack surface due to interconnected devices and networks. Implementation requires robust cybersecurity measures, including network segmentation, encryption protocols (e.g., TLS 1.3), multi-factor authentication, and continuous monitoring through Intrusion Detection Systems (IDS). The ISA/IEC 62443 standard provides a framework for securing industrial automation and control systems.

What are the key considerations when transitioning from traditional PLC-based control to a more software-centric automation approach?

Key considerations include:

(1) Scalability of the software architecture, often addressed through microservices design;

(2) Real-time performance capabilities of software platforms compared to PLCs;

(3) Integration with existing hardware and communication protocols;

(4) Skill set transition for the engineering team, including software development practices;

(5) Cybersecurity implications of increased connectivity;

(6) Validation and verification processes for software-based control systems.

How does edge computing enhance flexible automation systems, and what are its limitations?

Edge computing enhances flexible automation by enabling real-time data processing and decision-making closer to the source, reducing latency and bandwidth requirements. It facilitates adaptive control algorithms and local optimization. Limitations include constrained computational resources compared to cloud solutions, potential for increased system complexity, and challenges in managing distributed edge devices at scale.

What role does digital twin technology play in the design and optimization of integrated automation systems?

Digital twin technology creates virtual replicas of physical assets and processes, enabling simulation, optimization, and predictive maintenance in integrated automation systems. It allows for:

(1) Virtual commissioning to test control strategies before physical implementation;

(2) Real-time performance monitoring and optimization;

(3) Predictive maintenance through analysis of simulated vs. actual performance;

(4) Training operators in a risk-free virtual environment.

How can engineers effectively implement machine learning algorithms in hard automation systems traditionally controlled by deterministic logic?

Implementing machine learning in hard automation involves:

(1) Identifying appropriate use cases where ML can enhance performance, such as quality inspection or predictive maintenance;

(2) Developing hybrid control architectures that combine deterministic logic with ML models;

(3) Ensuring real-time performance of ML inference engines;

(4) Implementing robust data collection and preprocessing pipelines;

(5) Validating ML model outputs against traditional control methods;

(6) Designing fallback mechanisms to ensure system safety in case of ML model failures.

What are the technical challenges in integrating legacy automation systems with modern IIoT platforms, and how can they be addressed?

Technical challenges include:

(1) Protocol incompatibilities between legacy systems and modern IIoT platforms;

(2) Limited computational resources in legacy devices;

(3) Data format inconsistencies;

(4) Cybersecurity concerns.

References

Table of Contents

Automation and Robotics - Is there a Difference?Six Types of Industrial Automation Systems Fixed Automation: High-Volume Production Systems Programmable Automation: Versatility in Manufacturing Flexible Automation: Adapting to Product Variations Integrated Automation: Holistic System Approach Soft Automation: Software-Driven Process Control Hard Automation: Mechanical and Electronic Control Systems Implementing Automation: Strategies for Success Selecting the Right Type of Automation Factors to Consider While Implementing an Automation System Integration and Interoperability Challenges Skills and Training for Automation Implementation Advantages and Disadvantages of Automation Advantages:Disadvantages:Key Takeaways Frequently Asked Questions (FAQs)References