8 use cases of computer vision in production

The manufacturing industry is implementing various automation solutions as part of Industry 4.0, the next revolution in manufacturing. In order to change the way products are produced, under industrial automation, the manufacturing industry is adopting various advanced technologies such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), computer vision, robotics, and others. In particular, computer vision has taken center stage and revolutionized various segments of the manufacturing process with its intelligent automation solutions.

How is computer vision transforming the manufacturing industry through automation?

1. Product assembly

Computer vision applications play an important role in the assembly of products and components in manufacturing. As part of Industry 4.0 automation, much of the manufacturing industry is adopting computer vision to perform fully automated assembly and product management processes. For example, almost 70% of Tesla’s manufacturing process is automated. 3D modeling projects are created using computer software. Based on these designs, the computer vision system accurately guides the assembly process. Here, computer vision systems constantly monitor and guide robotic arms and workers on the assembly line.

2. Defect detection

The manufacturing industry strives for 100% accuracy in detecting defects in manufactured products. The discovery of defects at the end of the manufacturing process or after delivery to the customer can lead to an increase in production costs. These losses are comparatively much higher than the cost of implementing an AI-based computer vision defect detection system.

The computer vision-based application collects real-time data from cameras and uses machine learning algorithms to analyze data streams and, based on predefined quality standards, detect defects and provide a deviation percentage. Based on such data, failures in the production process can be traced.

PJSC “Chelyabinsk Iron and Steel Works” applied computer vision technology to automate the process of controlling steel defects. The implemented video analytics system allowed the plant to identify more than 20 classes of defects, including scratches and microcracks from 0.3 mm in size, and ensure an accuracy of defect recognition from 97%. Thanks to the solution , the company increased the flaw detection process by six times.

3. 3D Vision system

The computer vision system is used on the production line to perform the duties people face. In this case, the system uses high resolution images to build a complete 3D model of the components and their connectors. As the components pass through the manufacturing plant, the computer vision system captures different images from different angles to create a 3D model. These images, when combined and fed into artificial intelligence algorithms, reveal any incorrect streams or minor deviations from design and standards. This technology is highly trusted in manufacturing industries such as automotive, electronics, oil and gas, and energy.

To speed up the rock analysis process at the exploration stage, improve the accuracy of determining the quality and parameters of the core by eliminating the human factor, reduce the cost of attracting contractors, and systematize the data obtained, the Gazprom Neft team developed and implemented an automatic geological rock recognition system. They developed an algorithm for obtaining a machine learning model, taking into account expert assessments using computer vision methods to obtain image descriptors, on the basis of which a decision was made about whether an image segment belongs to a specific target class. Thanks to the use of technology, the company has achieved a reduction in core analysis time by 12 times,and the annual savings on laboratory core tests amounted to 85 million rubles.

4. Cutting with computer vision

Rotary and laser die cutting are the most common cutting technologies in the manufacturing process. The rotary method uses hard tools and steel blades, while the laser uses high-speed laser light. Although laser die cutting is more accurate, cutting hard materials is challenging and rotary cutting can be used to cut any material.

The manufacturing industry can deploy computer vision systems to perform rotary die cutting with the same precision as a laser to cut any design. Once the design sample is loaded into the computer vision system, the system will guide the machine, whether it be laser or rotary die cutting, to perform the cut accurately.

5. Preventive Maintenance

Some manufacturing processes occur at critical temperatures and environmental conditions, so material degradation or corrosion is a common occurrence. Without proper maintenance, this leads to deformation of the equipment, its failure and stop the production process.

Computer vision systems allow you to control equipment based on various indicators. If any deviation from the indicators indicates corrosion, computer vision systems can alert the appropriate managers to the need for preventive measures and maintenance.

6. Occupational health and safety

Machine vision is used to analyze images and compare them with the existing data set in order to detect anomalies and prevent dangerous situations on production sites, production lines.

Manufacturing workers work in extremely hazardous environments. Failure to follow safety and protection standards can result in serious injury or even death.

In the event of an accident, the computer vision system can alert managers and staff to where the accident occurred and the severity of the accident so that the production process can be stopped at a particular site and ensure the safety of employees.

Rosenergoatom Concernimplemented an automated video analysis system at the Kola NPP to monitor compliance with safety regulations, which is capable of detecting 26 types of violations by 19 parameters. Cameras monitor the personnel in the process of performing work. Real-time video from cameras is checked by a neural network for wearing personal protective equipment. If a violation is detected, the information is immediately transmitted to the dispatcher and shift supervisor for prompt response. If before the use of computer vision technology, up to 80 violations per week were recorded at one nuclear power plant, and dispatchers monitored compliance with safety regulations by manually viewing video from cameras and analyzing violations after the fact, then after the implementation of the solution, the possibility of missing violations due to the “human factor” is eliminated – they the number was reduced to eight per week,the machine automatically fixes 95-98% of violations.

7. Packing inspection

In some manufacturing companies, it is important to keep a count of the number of products produced before they are packaged. Performing this task manually can lead to many errors, which becomes a serious problem, for example, in pharmaceuticals and retail.

Deploying a computer vision system in the packaging process to count the number of pieces allows you to verify that packaging standards are met. Computer vision based inspection can track if an item has the desired color, length and width, if there are any edges, if the package is filled to the required level.

8. Barcode analysis and warehouse logistics

Virtually every product sold today has a barcode, scanning which is not a task that humans can perform quickly and efficiently on a large production batch scale. Implementing computer vision on a manufacturing site can both improve the parts management process, speed up order processing, and improve the tracking system.

Computer vision systems can help count goods, maintain inventory levels in warehouses, and automate and alert managers if any material required for production is below demand.

With a computer vision system based on barcode data, these systems can help inventory managers locate items in a warehouse and avoid human error.

In many cases, a mislabeled product can be harmful. Computer vision helps manufacturing companies identify such items (as well as misaligned or wrinkled labels), match them to a database, and track them.

Amazon warehouses in the US have been automating the process using computer vision for several years now. Instead of hand-held scanners, which often get in the way of workers handling larger items, AI cameras and scanners follow the process and automatically keep track of which products go into which bins.

Benefits of using computer vision and video surveillance technologies and new business results

The use of intelligent solutions using machine vision and video analytics allows companies to achieve benefits that positively affect the overall economic effect.

  • Saving time. A fully automated system not only works much faster, but can also work 24/7 if required.
  • Accuracy. Computer vision-based decision making allows manufacturing companies to achieve a higher level of accuracy within accepted tolerances. The combination of special equipment and advanced machine vision algorithms achieves a near-perfect level of precision in production and quality control.
  • Repeatability. When it comes to repetitive operations and monotonous tasks, computer vision solutions are more efficient. The fully automated system speeds up production time and reduces costs on many levels.
  • Cost reduction. In addition to reducing labor costs (because fewer people are required to manage the process), better product quality is achieved with less waste by reducing errors or deviations from standards.

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