What Is a Machine Vision Controller?
Tasks of a Machine Vision Controller
Powerful processors enable various functions and tasks in industrial image processing. Examples of this are:
By monitoring and controlling machines, systems and processes in production using a machine vision system, production efficiency can be significantly increased. The controller enables precise control and real-time monitoring.
The controllers collect the data recorded by cameras, profile sensors and other devices for further processing. The data is then processed, analyzed and visualized using manufacturer-specific software.
User-friendly operation is important for monitoring and controlling the production line, as the controller is the link between the human operator and the automated system. Traditional operating systems such as Linux are often used.
The industrial controllers have multiple interfaces to ensure seamless connectivity and communication between the detection devices used and action components in an industrial automation system. Real-time connections can also be used. This is especially important for fieldbus systems such as PROFIBUS or EtherCAT etc.
Depending on the area of application, machine vision controllers can be specifically adapted to different requirements via different hardware components such as processors and storage media.
The Difference Between Machine Vision Controllers and Smart Devices
Structure and Components of a Machine Vision Controller
CPU (Processor)
Mainboard
Memory (RAM and Hard Disk)
Interfaces
- RTE
- PROFINET
- Ethernet IP
- EtherCAT
- Ethernet TCP/IP
- UDP
- Digital I/O
- SFTP
Cooling System
Graphics Card
The Differences Between the Interfaces
Ethernet TCP/IP
UDP
SFTP
DIO (Digital Input/Output)
RTE (Real-Time Ethernet)
Requirements for Controllers with AI Tools
Computing Power and Hardware
| Processor (CPU) | Since AI tools often require extensive computing power, the industrial PC should be equipped with a powerful CPU. For deep learning applications, multi-core processors are particularly advantageous because they enable parallel calculations efficiently. |
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| Graphics card (GPU) | A powerful GPU is essential for image processing and especially for AI and deep learning applications. GPUs provide the computing power needed to efficiently accelerate machine learning and parallel image processing processes. |
| Neural processing unit (NPU) |
A NPU is a specialized hardware module specifically designed to accelerate calculations in neural networks and machine learning models. It is specially optimized for artificial intelligence, particularly deep learning and inference processes. NPUs increase the efficiency and speed of AI applications by taking over typical computing tasks from CPUs and GPUs – but with significantly higher performance, combined with better energy efficiency. Thanks to this improved energy efficiency, less power is lost than with GPUs, which is particularly important in industrial environments where passive cooling is often used. |
| RAM and memory |
Sufficient RAM – at least 16 GB, ideally 32 GB or more – is crucial for efficient handling of large amounts of data. Image data from cameras in particular requires a large amount of memory, as it is analyzed in RAM by algorithms and AI applications. Numerous image sources and evaluations often run in parallel, which further increases the need for storage. AI models are also constantly growing and require increasingly more capacity. For fast data access, SSDs (solid state drives) are preferable. |
In addition to computing power and hardware components, the use of AI software accelerators is also key. Compared to conventional software solutions, acceleration factors of two to three can be achieved, which significantly increases system performance. For optimal real-time processing, close coordination between software and hardware is essential to ensure the shortest possible process times.
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Processor (CPU)
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Since AI tools often require extensive computing power, the industrial PC should be equipped with a powerful CPU. For deep learning applications, multi-core processors are particularly advantageous because they enable parallel calculations efficiently.
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Graphics card (GPU)
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A powerful GPU is essential for image processing and especially for AI and deep learning applications. GPUs provide the computing power needed to efficiently accelerate machine learning and parallel image processing processes.
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Neural processing unit (NPU)
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A NPU is a specialized hardware module specifically designed to accelerate calculations in neural networks and machine learning models. It is specially optimized for artificial intelligence, particularly deep learning and inference processes. NPUs increase the efficiency and speed of AI applications by taking over typical computing tasks from CPUs and GPUs – but with significantly higher performance, combined with better energy efficiency. Thanks to this improved energy efficiency, less power is lost than with GPUs, which is particularly important in industrial environments where passive cooling is often used. |
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RAM and memory
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Sufficient RAM – at least 16 GB, ideally 32 GB or more – is crucial for efficient handling of large amounts of data. Image data from cameras in particular requires a large amount of memory, as it is analyzed in RAM by algorithms and AI applications. Numerous image sources and evaluations often run in parallel, which further increases the need for storage. AI models are also constantly growing and require increasingly more capacity. For fast data access, SSDs (solid state drives) are preferable. |
In addition to computing power and hardware components, the use of AI software accelerators is also key. Compared to conventional software solutions, acceleration factors of two to three can be achieved, which significantly increases system performance. For optimal real-time processing, close coordination between software and hardware is essential to ensure the shortest possible process times.
Robust Hardware for Powerful Controllers
For a stable and reliable system, the controller must withstand the demands of the industrial environment.
Heat generation and cooling: High computing power requires efficient heat removal to avoid overheating and system failures. Industrial controllers are often equipped with special cooling technologies or even fanless designs, which are particularly effective in dusty and dirty environments and also reduce maintenance.
To get the most out of an industrial PC for industrial image processing with AI tools, both hardware requirements – such as powerful CPU, GPU, NPU, sufficient RAM and fast memory – as well as software and interface compatibility must be carefully considered. The key here is choosing a robust and reliable system that fits seamlessly into the industrial environment and can be flexibly expanded and integrated.
Flexible Use of Machine Vision Controllers
Applications of Machine Vision Controllers
Machine Vision Controller with Machine Vision Cameras
In the automotive industry, the quality of individual components of passenger car interior doors, such as window controls or armrests, must be visually checked for different types of door. For this purpose, a vision system consisting of several cameras, lenses, illuminations and a control unit is installed.
