Integrating single board computers for machine vision in industrial automation

Introduction

In the realm of technology, single board computers (SBCs) are compact and influential devices. They integrate entire computing systems onto a single board, offering portability, cost-efficiency, and adaptability across diverse applications. SBCs play a crucial role in industrial autom­ation by facili­tating Machine Vision impleme­ntation. This involves proce­ssing visual data from cameras and sensors to automate tasks like quality control and precision measurement. The significance of SBCs extends into Industry 4.0, where they enable Predictive Maintenance by swiftly processing real-time data from connected sensors. This empowers machinery to anticipate maintenance needs, minimizing downtime and maximizing operational efficiency in the era of smart manufacturing. Machine vision is increasingly vital for industrialization mechanization in the economy. While mechanization aided with physical labor, automation significantly reduces human sensory and mental requirements. With technologies related to new inflation, industrial autom­ation has the potential to generate explosive growth.

Machine vision and its advantages over conventional methods

Machine vision is a field used in both industrial and non-industrial systems, involving hardware and software to guide devices based on captured images. Industrial machine vision systems prioritize robustness, reliability, stability, affordability, accuracy, mechanical integrity, temperature stability, and low cost. They rely on digital sensors in specialized cameras and analyze and measure various characteristics for decision-making purposes.

Industry 4.0 is transforming machine vision beyond error detection to include predictive maintenance monitoring and robotic guidance, enabling robots to safely interact with humans and respond to human interactions.

When integrated with AI, machine vision can solve practically every production challenge. AI can enable a machine vision system to self-adjust and learn from each feedback loop cycle, making it wiser and smarter. Machine learning can help vision systems understand massive image datasets better than humans. Adding a self-learning algorithm to machine vision is exciting since vision systems typically follow a fixed set of rules, rendering them rigid when faced with rapid changes. This is crucial since Industry 4.0 relies on flexible manufacturing lines that can quickly adjust to small batches of unique products.

Machine vision and its role in industrial automation

Machine vision adoption in Industry 4.0 is also helped by embedded computing. It analyzes data "on the edge," rather than sending it over a busy network to computers at a secondary location, decreasing bandwidth needs.

Industry 4.0 technology enables robots and co-bots to operate autonomously, improve response time, and reduce errors. Machine vision improves warehouse systems by extracting, processing, and analyzing real-time digitalized images, allowing network, robot, and plant-level managers to visualize production processes. Cameras can gather SKU data, predicting shortages and equipment failures. Vision is a valuable sense in both humans and machines, providing managers with valuable data for operations.

SBCs empowering automation systems in machine vision applications

With the development of industrial automation and artificial intelligence, an increasing number of industrial settings are employing single board computers (abbreviated as SBCs) to realize control, monitoring, data acquisition, and analysis functionalities. These industrial SBCs possess superior performance and versatility and are comparatively more cost-effective than their conventional industrial computer and PLC counterparts, thus experiencing a surge in popularity for employment in industrial applications.

The use of Single Board Computers (SBCs) in industrial settings has indeed become increasingly prevalent due to several key factors, as you've mentioned:

1. Industrial Automation: SBCs are a crucial component in industrial automation systems. They are employed to control and monitor various processes and machinery, making them more efficient and precise. SBCs are particularly well-suited for tasks like process control and robot control due to their reliability and computational power.
2. Industrial Internet of Things (IoT): In the realm of Industrial IoT, SBCs are indispensable. They serve as the bridge between the physical and digital worlds by facilitating communication between sensors, actuators, and other devices. SBCs are responsible for collecting data from various sensors and transmitting it to centralized systems or the cloud for analysis, enabling real-time decision-making and remote monitoring.
3. Industrial Intelligence: SBCs are instrumental in enhancing industrial intelligence. They are employed for data acquisition, processing, and analysis, which can lead to valuable insights for improving production efficiency and product quality. By leveraging artificial intelligence and machine learning algorithms, SBCs can help predict equipment failures, optimize production processes, and minimize downtime.

The benefits of using SBCs in industrial applications include their cost-effectiveness compared to traditional industrial computers and programmable logic controllers (PLCs). SBCs are often more affordable, yet they offer superior performance and versatility. Additionally, their flexibility allows for easier adaptation to changing industrial requirements and the integration of new technologies.

As industrial automation, IoT, and intelligence continue to advance, the demand for SBCs is likely to grow even further. These compact and powerful computing devices are playing a pivotal role in modernizing and optimizing industrial processes across various sectors.

Advantages of SBCs in industrial machine vision in terms of size, flexibility, and performance

AI in the manufacturing industry has improved the safety and efficiency of all its operations. One of the popular tools used in manufacturing is Machine Vision. It is used to perform an automated visual inspection on the objects that are manufactured in thousands every day. With the advancements made in Artificial Intelligence, the processes of algorithm development have become superior. Deep learning-based inspection models are often combined with machine vision systems. Such systems are easier to train and implement. Industrial machine vision systems are also more reliable, robust, and stable. They have high mechanical and temperature stability, are of low cost but high accuracy. There are many different applications of Machine Vision in manufacturing. Here are a few of the machine vision applications –

1. Object detection

   It is a machine vision use case where the Machine Vision aided system looks for individual objects rather than the entire image. The goal of this exercise is to identify different objects inside an image so that it can eliminate objects which are not relevant to the inspection and only focus on the relevant objects. A variety of techniques are used to make object detection as efficient as possible. Object detection is used in many points in the manufacturing industry like an assembly line, sorting, quality management, inventory management, etc. For instance, in the gearbox assembly chain, machine vision analyzes the image for specific parts and confirms the presence or absence of such parts in that image.

   
2. Parts counting

   It is a task that is slow and tedious but doesn’t require a lot of intelligence to do. However, manual operators trying to reach their daily goals might make mistakes while counting and that can cause massive delays in assembling parts. Machine vision can use its object identification algorithm to detect the parts and then count them accurately and quickly. For instance, in the manufacturing chain, machine vision can count the piston rings in a stack with great efficiency.

   
3. Surface defect identification

   Surface defect identification is another machine vision application that is an essential step in quality control. Manual identification of surface defects is a tedious task and defects can be missed by human operators as they try to match the supply with the demand. Machine vision can provide the accuracy and efficiency of surface inspection in an easy-to-train model. In manufacturing industries, surface defect inspection can detect defects in casting components, bearings, and different metal surfaces. For instance, packaging defect identification can help identify bad packaging which will cause damage during transport.

   
4. Print defect identification

   Print defect identification is the process of detecting anomalies in the prints like inconsistencies in color, text, or pattern. Manual inspection by human operators might lead to oversight that will cause deterioration in the quality of the final product. Machine vision can perform print defect identification using AI and deep learning. This takes care of prints, labels, and packaging prints.

5. Print character reading

   Print character identification is one of the machine vision use cases that is performed using OCR (optical character recognition). Machine vision can help in tracking various objects in the manufacturing supply chain with the help of print character reading. It can verify the name tag and other details of any object and update the status of the object while it goes through various stages of its life cycle. It is useful in logistics as items are at a high risk of being misplaced.

   
6. Barcode scanning

   Machine vision can be used to read barcodes and data matrix codes. This will help categorize products that the AI identifies thereby error-proofing production and packaging processes. Machine vision barcode scanners are more efficient than the manual categorization process which has the potential to be error-prone. In manufacturing, barcode scanners can separate products based on their attributes or features.

   
7. Locating

   Machine vision can locate an object and state its coordinates or position relative to the operator looking for it. The ability to locate an object at any point in time helps with logistics and supply chain management. In case an item is misplaced, object detection through machine vision can find the location of an object easily and quickly. In manufacturing, locating objects is essential as there’s an inflow and outflow of various parts from one section into another. Machine vision can identify and keep track of all such objects through various means.

   
8. Measurement

   Measuring various objects, their surface area, volume, length, and width is necessary to estimate the space they’ll occupy while transporting. Physical inspection can only give an estimate of such a measurement. Using machine vision, the AI can identify the object, and calculate its geometrical dimensions from an image. For instance, the inner diameter of an engine cylinder bore can be measured by an image taken by a 2D or 3D camera using machine vision.

   
9. Robotic guidance

   This is a machine vision application that involves locating a specific part and ensuring its proper placement and positioning so that no errors or downtime occur in production. Robotic guidance can perform visually assisted robotic operations through a machine controller or robot. The robots can be used to manage repeatable activities with high precision and accuracy, working without a break to ensure maximum efficiency, and can easily be used in environments not safe for manual operators. For instance, automated pick and place will assemble components of any object very quickly.

   Thus, machine vision is a tool that can revolutionize the efficiency and accuracy of the manufacturing industry through AI and deep learning algorithms. Coupled with controllers and robots, such models can monitor everything that takes place in the manufacturing supply chain, from assembling to logistics with the least human intervention. It removes the errors that come with manual operations and allows the people involved in such operations to engage in more cognitive tasks. Machine vision uses are vast and varied. Hence, machine vision can change the way a manufacturing company performs its tasks.

Conclusion

Machine vision plays a role, in automation bringing about improvements in precision, efficiency, quality control, cost reduction and adaptability within manufacturing processes. It offers accuracy when it comes to tasks such as quality control, measurement and inspection. This leads to product quality and reduced errors. However implementing machine vision does require an investment and involves complex setup and maintenance. Integrating it into existing production lines can also be quite challenging. Additionally there are factors like variability in production environments, data management concerns and security risks that pose their set of challenges. Despite these obstacles advancements in technology have made machine vision solutions more accessible than before. This has resulted in adoption, across industries with the promise of improving operational efficiency, product quality assurance while boosting global market competitiveness.