Computer Vision Manufacturing Use Cases: A Guide

Abirami Vina -

Artificial intelligence (AI) is expanding its reach across industries, with manufacturers now tapping into computer vision applications to enhance efficiency, precision, and productivity at every stage of production.

Computer vision applications make it possible for computers and machines to see and understand real-world information through images and videos.

For example, in manufacturing, computer vision can be used to check product quality, detect anomalies, take safety precautions, and automate processes. In fact, automotive companies like Audi are using Vision AI systems for quality control and spot welding processes.

As manufacturing shifts into Industry 4.0, where automation, data insights, and connected devices are taking center stage, AI is helping drive these changes. Let’s take a closer look at how computer vision is being used in manufacturing today.

Computer Vision in Manufacturing: Use Cases

Manufacturers can use computer vision to handle inspection and quality assurance tasks at a high volume. Let's explore a few key applications of computer vision in manufacturing.

Quality Control and Inspection

Quality is the backbone of every business. This sentiment is especially true in manufacturing, where the quality of every product directly impacts reputation, customer satisfaction, and overall success. With demand constantly rising, it’s no longer practical for manufacturers to rely solely on people to check the quality of every finished product. By adopting computer vision systems, manufacturing plants can detect defects with greater accuracy and efficiency.

Computer vision being used to detect a dented metal tin can. (Source) 

Computer vision techniques like object detection, OCR (Optical Character Recognition), OCV (Optical Character Verification), and instance segmentation can detect defects or anomalies in assembly and production lines. Catching flaws on the assembly line can help industries reduce raw material waste and boost their first-pass yield.

Predictive Maintenance

Regular maintenance is vital to improving any product's lifetime. By regularly monitoring costly equipment, manufacturing industries can prevent unexpected breakdowns and avoid disruptions in operations.

Predictive maintenance is a strategy that uses data analytics and machine learning algorithms to predict when equipment is likely to fail. Computer vision can be used for predictive maintenance by continuously analyzing images and video feeds to detect signs of wear, overheating, or alignment issues before they lead to failure. By spotting potential issues early, industries can schedule maintenance or other solutions in advance, keeping the workflow running smoothly.

Assembly Line Automation

With automation, manufacturers can keep up with rising demand without sacrificing quality. Take, for example, robots integrated with computer vision that can automate assembly lines. These robots receive video input from cameras at different angles, and it helps them understand their surroundings and make precise movements. They’re especially common in chip manufacturing, where every tiny detail matters, and absolute precision is essential.

Inventory and Supply Chain Management

Another aspect of the manufacturing industry is managing inventory and optimizing logistics, both of which demand significant attention and manpower. However, with computer vision systems, inventory monitoring can be streamlined easily. Vision-enabled cameras can constantly monitor the inventory shelves, and industries can get real-time updates on different products' availability and usage patterns. Based on these inputs, management can make informed decisions.

Worker Safety

Cmputer vision technology can play a big role in identifying safety hazards in warehouses and other working facilities.

Applications like fall detection can be implemented to track worker movements and alert management of any mishaps. Manufacturers can install cameras in injury-prone areas for continuous monitoring. These cameras, equipped with computer vision models, can monitor the worker's movements and trigger an alarm when they detect a fall or any unusual behavior.

Using computer vision to detect if workers are wearing personal protective equipment (PPE). (Source)

Technologies Behind Computer Vision in Manufacturing

Now, that we’ve taken a look at computer vision applications in manufacturing, let’s understand some of the underlying technologies that support these applications.

Machine Learning and Deep Learning

Under the hood of Vision AI applications are computer vision models designed to analyze visual data, like images and videos, to recognize objects, patterns, and irregularities.

For example, these models can be trained for object detection (to spot and locate specific objects in a scene) of people, vehicles, or parts on a production line. Machine learning (ML) and deep learning (DL) are key techniques used to train these models, helping them detect specific patterns or subtle differences.

Let's say you want to train a computer vision model to detect defects. First, you’d gather images or videos of both defective and quality products. Next, you would annotate the data to highlight defects, teaching the model to distinguish between good and faulty items. Then, the model can be trained on these labeled datasets. The model will learn the characteristics of each product type during training.

After training, the model can be tested with a separate set of images to check its performance, and you can make adjustments as needed. When the model meets the desired standards, it’s deployed on the production line, working in real-time to catch defects, maintain quality control, and increase overall efficiency.

An example of detecting defects on metals using computer vision. (Source)

3D Imaging

3D imaging can be used in manufacturing to scan products for any defects or in virtual prototyping.

3D imaging cameras can capture the outer surface, even the inner parts, and create a digital model. Then, the generated 3D model can be compared to an ideal, defect-free reference model to spot minor scratches, imperfections, or inconsistencies.

Advanced systems can even generate 3D models in the prototyping stage. Researchers can use the prototype to test them with real-time constraints and analyze them for further improvements. This way, industries can reduce the waste of raw materials and enhance product quality.

Edge Computing and IoT Integration

When computer vision systems are integrated with edge computing and IoT, they can get faster responses. It eliminates transmission time and speeds up processing. With data handled locally, cameras and sensors can analyze information right on the factory floor instead of relying on distant cloud servers.

Such systems can provide real-time feedback, which is crucial for detecting defects, monitoring equipment, and tracking products through production. Edge AI makes computer vision applications faster and more reliable, helping manufacturers improve efficiency and accuracy.

Robotics and Vision Systems

Humans can't perform high-accuracy operations like soldering in circuit boards or spot welding perfectly every single time. However, a robotic arm with computer vision can meet the accuracy standards. For instance, assembling a car body requires around 5,000 to 7,000 precise welding spots - a challenge that vision-equipped robots are built to handle consistently.

Benefits of Implementing Computer Vision in Manufacturing

We’ve seen how computer vision applications work. Now, let’s take a look at the benefits they bring to manufacturing:

  • Cost Reduction: In manufacturing, the longer a defect goes undetected, the more costly it becomes to repair. Detecting defects early in the production line reduces operational costs and minimizes raw material waste. Once trained to meet industry standards, the AI system can detect even the smallest defects consistently.
  • Improved Product Quality: Computer vision systems reduce human errors, so only products meeting 100% quality standards reach the end of the production line. 
  • Scalability and Flexibility: Computer vision systems offer flexibility to adapt to various standards. They can be adjusted up or down based on industry requirements. By tweaking algorithms and using different vision models, industries can tailor computer vision systems to their needs. If demand increases, these systems can be scaled up with minimal effort.

Case Studies of Computer Vision in Manufacturing

Next, let’s walk through a few real-life case studies of large-scale and international manufacturing companies using computer vision systems to improve product quality, increase efficiency, etc.

Assessing Car Damage with Computer Vision

Using computer vision techniques like instance segmentation, damaged parts of a car can be detected. Volvo, a luxury vehicle manufacturer in Sweden, is using AI-powered automated systems to offer better car services to its customers. The automated systems, equipped with advanced machine learning and computer vision techniques, inspect the damaged vehicle, provide real-time feedback, and estimate the repair cost.

By assessing car damages with computer vision, car manufacturers and insurance companies can also process inspection operations faster and smoother. Customers can also benefit from faster repairs and quick claims.

Electronics Manufacturing

An electronic board can contain around 5,000 to 8,000 solder joints, making it nearly impossible for humans to inspect each one accurately with the naked eye. Manufacturers are using PCB inspection cameras with computer vision to improve flaw detection in circuit boards. Bosch uses AI-driven systems to check whether all the elements in a circuit board are soldered with perfection.

How does this work? PCB inspection cameras capture high-quality images of the circuit board. Computer vision algorithms then extract specific information, such as joints, corners, and textures. The extracted information is processed and analyzed to detect defects or misplaced joints. A visual inspector reviews the defects spotted by AI and makes any necessary adjustments.

Food and Beverage Industry

Computer vision systems are also becoming common in the food and beverage industry. For instance, a German bottle cap manufacturer has introduced a machine vision inspection system to improve quality control for ceramic beer bottle caps. Previously inspected by humans, the caps are now checked automatically using computer vision. 

An Example of Detecting Loose Bottle Caps. (Source)

The system analyzes each cap for flaws in shape, material, and print quality, comparing them to a perfect model to catch issues like color variations, missing edges, or other defects. If a defect is found, the cap is redirected for further inspection. It inspects up to 120 caps per minute, increasing accuracy and efficiency while reducing the need for manual labor.

Conclusion

Computer vision is playing a major role in reshaping the manufacturing industry. By automating tasks with improved accuracy, vision systems are changing the traditional way how manufacturing industries worked in the past. Using AI and vision systems to detect flaws in product lines greatly reduces the human workload and lets them focus more on other demanding operations.

Training and fine-tuning these computer vision models help the industry meet market standards, improve efficiency, reduce costs, and enhance overall product quality. As these technologies become more flexible and are recognized by many institutions, it is the right time for industries to explore the integration of vision systems to stay competitive in this rapidly evolving industry.

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