Computer vision, just like other blooming digital technologies, is a significant element of Industry 4.0. However, the recent advances in image processing and machine learning have enabled new uses of computer vision in manufacturing. As modern thermal imaging-based computer vision systems consist of high-resolution cameras and AI, they have a lot to offer to manufacturers.
Manufacturing and industrial markets can leverage computer vision to increase safety and enhance profitability and productivity. This blog post will walk you through the real-world applications of computer vision in manufacturing.
How computer vision can transform the manufacturing industry
Computer vision has a wide range of applications in the manufacturing industry. Let us look at the top six uses of computer vision in manufacturing.
1. Leak detection
The traditional method of detecting oil, vapor or liquid leaks from plant components is unsafe, error-prone and labor-intensive. With computer vision, industrial companies can monitor and detect leakages more accurately and safely. AI-powered cameras hold the potential to monitor and automatically detect leakage in real-time. Also, upon leakage detection, a notification is sent immediately to the concerned authority.
Let us consider a scenario: A chemical process plant with several pipelines transports toxic gases or liquids. Leakage from any such pipelines can pose a serious threat to operators with conventional condition monitoring. Therefore, a computer vision-based leakage-detection mechanism can save manufacturing companies from mishaps. It helps the chemical plant achieve faster, safe, accurate and remote leakage monitoring and localization.
2. Corrosion detection
In heavy industries, corrosion (rust) poses a substantial threat to workers’ operational safety. Also, the traditional process of detecting and identifying corrosion by human interpretation is subject to error. Computer vision can constantly monitor and automatically detect corrosion. It enforces the deep learning approach to automate the corrosion detection process. Thus, it can help manufacturers reduce human risks and costs associated.
For instance, a mining company with large steel structures requires to check the structures frequently for corrosion. The manual process of rust detection may take a couple of weeks/months, based on the size of the plant. Using machine learning-based computer vision techniques, the mining company effectively identified corrosion. Also, upon rust detection, the system sends an instant alert to managers for maintenance activities.
3. Product assembly
Manual product assembly process takes a lot of time and is quite expensive. However, the growing role of automation in the manufacturing space is replacing these traditional methods with fully automated systems. Tesla has already automated 70% of its manufacturing processes. AI-powered computer vision techniques can help manufacturers automate their product and component assembly processes.
Furthermore, it enables them to maintain their packaging standards while maintaining accuracy and saving time. For instance, a soft drink filling plant must verify if the bottle cap closure or bottle packaging is correctly conducted or not. Computer vision automated the entire process and also ensured that the bottles are precisely packed.
4. Defect detection
Recent progress in computer vision allows users to use advanced deep learning technology for visual product inspections. The process is quite simple: first, thermal cameras capture images of products. The neural network then processes captured images. Furthermore, on detection of defects, it takes pre-defined actions like sending a notification.
Let’s understand this with an instance. During production, a sunflower oil production company was facing the issue of damaged bottles. The manual process of detecting defects was costing them a fortune and causing trouble during transportation. Thus, they embraced visual inspection based on computer vision technology. It enabled them to detect the defect instantly and get notified in real-time.
5. Inventory management
Manual inventory management process requires a significant amount of human resources. Generally, a manufacturing firm undergoes several issues like misplaced inventory, stockouts, employee errors, excess inventory, etc. Using computer vision, manufacturers can automate inventory monitoring and management processes. It lets them avoid stockouts, track inventory movement accurately and execute automatic counting of inventory.
As an illustration, let us consider a manufacturing inventory where managing, placing and keeping track of stocked goods is difficult if done manually. However, an AI-based computer vision method helped them outdo human counterparts by enabling their inventory management team to:
Original Source: https://www.softwebsolutions.com/resources/computer-vision-in-manufacturing.html
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