Computer Vision in Manufacturing: Top 10 Applications
Computer Vision in Manufacturing: Top 10 Applications
Computer vision is a subfield of artificial intelligence that uses machine learning and deep learning to give computers the ability to observe, identify, and evaluate objects in still images and moving movies in the same manner humans do.
Using computational vision for automated artificial intelligence visual inspection, remote monitoring, and automation is quickly gaining prominence. Computer vision has a significant influence on businesses in a wide variety of fields, including retail, security, healthcare, automotive, logistics, agriculture, and manufacturing, to name a few.
This article will present an overview of state of the art, fresh ideas for computer vision projects, and real-world examples of the use of computer vision technology in the manufacturing industry.
Computer Vision Systems
Computer vision systems leverage cameras to capture visual data, machine learning algorithms for analyzing pictures, and conditional logic to execute software use cases. Cameras are the primary source of visual data. Implementations of computer vision that are scalable, efficient, resilient, safe, and private are made more accessible to create thanks to the application of artificial intelligence (AI) to edge devices (also known as edge intelligence).
Applications of Computer Vision In Manufacturing
1. Quality Control with Computer Vision
During the production process, one of the most critical ways that computer vision is used in manufacturing is to automate quality checks. In the field of manufacturing, it is essential to keep quality standards high. Even though this can be done by hand with the help of quality control experts, there is a high chance of making a mistake.
As a result, many manufacturing companies are moving toward using deep learning and computer vision for quality control and inspection tasks.
Adopting these technologies means that people won't have to do as much work, but the process will still be very accurate.
Also, these technologies are cost-effective because they help make operations more efficient and cut down on labor costs. In a manufacturing plant, where space is limited, computer vision can do many inspection tasks faster, better, and more efficiently than humans. It becomes possible to check each and every part, not just a few at a time.
Vision systems also use a consistent standard to make up for differences between people who inspect the same thing. So, an automated inspection can make the process of making consumer goods much more efficient.
Machine vision inspection systems have been using methods that can only be trained once for a specific inspection task and cannot be retrained. Deep learning-based machine learning methods, on the other hand, are much more flexible.
2. Lean Manufacturing with Computer Vision
Lean manufacturing is a way of making things that try to get the most out of a manufacturing operation while wasting as little as possible. Industry 4.0 technologies change lean processes to help the business move forward. They also offer a data-driven way to make decisions and automate tasks with intelligent sensing technologies.
Computer vision is an integral part of Industry 4.0 technologies that are being used for digitizing factories. Expert reports from Deloitte estimate that switching from lean to digital lean will increase EBITDA (earnings before interest, taxes, depreciation, and amortization) by US$20 million per year, decrease costs by 15% per line per year, and increase general equipment effectiveness (OEE) by 11% per year.
For example, computer vision systems can recognize and track events and employee footfall (automated spaghetti diagram) to measure the efficiency of a process, provide analytics to find broken equipment, and make sure that everyone on the plant floor is working as efficiently as possible.
3. Safety in Manufacturing with Computer Vision
People who work in manufacturing businesses get hurt on the job. For employers, ensuring their workers are safe is very important because it directly affects production.
Manual monitoring methods are often insufficient because the person in charge may not be able to watch multiple screens simultaneously. And these kinds of mistakes can have huge effects on the workers and the company.
Using computer vision technologies, workers can find any safety measures problems, create dashboards reports, and send out alerts.
It is also possible to set up automatic alerts in case of an accident so that the management can take action immediately.
4. Supply Chain Management with Computer Vision
Optimizing the supply chain process helps manufacturing plants cut costs and make customers happier at the same time. In the past, humans had to keep an eye on different parts of the supply chain process. However, this is no longer the case as computer vision has improved.
Several manufacturing companies use computer vision applications to do things like manage warehouses, keep track of inventory, and make the company run more efficiently.
For example, companies like Amazon and Walmart are working to implement drone systems to monitor warehouse inventories. For example, camera streams are processed in real-time to find empty containers and determine the best way to restock them.
5. Defect Detection with Computer Vision
Using computer vision in manufacturing makes it easy to find things that aren't right. It can be hard to find minor product flaws by hand during the manufacturing process. Also, sending out an order with a broken product can increase production costs and make customers unhappy.
There's no doubt that this could be bad for the business. Adopting a computer vision system for defect detection can help you avoid these problems by keeping a close eye on the manufacturing process and quickly spotting pieces that aren't right.
In the example, a real-time deep learning system is used to find defects. Different AI algorithms were used to test it, such as YOLOv3 (which took 32 hours to train), Faster-RCNN, FPN, and a single shot detector (SSD) neural network with a 98% accuracy rate. Compared to machine vision, deep learning is a lot more stable and adaptable, and the hardware is about the same price.
6. Automated Assembly with Computer Vision
All over the world, manufacturing companies are quickly switching to computer vision-based systems to automate the process of putting together products. Computer vision speeds up the process and makes sure that the product is put together correctly.
With this technology, specialized software is used to make a 3D model design, which the computer vision system then uses to put together the parts. The CV is beneficial for factories that make things with small or fragile parts that can't be handled well by hand. With the help of computer vision systems, the assembly process can be closely watched and finished with little to no mistakes.
7. Monitoring and Forecasting with Computer Vision
In factories, goods are made with the help of specialized equipment. When these machines are used often, they may show signs of wear or even break down, which could cause product defects or losses. Using computer vision technologies is a much better way to find these kinds of changes in manufacturing equipment than having a person look for them.
Such technologies have been used to find flaws in tiny machine parts in real-time. This makes it easier to find and fix factors that could have slowed down production if they hadn't been seen in time.
Deep learning is used to figure out what's wrong with industrial equipment, find leaks, and make predictions. Intelligent fault diagnosis systems have been developed with the help of machine learning techniques. Deep learning has been used, for example, to find cracks in things like boxes and gas pipes.
8. Real-time Barcode Reading
Most products today have a barcode that makes it easy to find them. Before sending their products to the market, companies that make things need to make sure that the barcodes are printed correctly. Verifying each barcode by hand takes a lot of time and costs money in terms of labor.
Even with these manual checks, mistakes will still happen. Computer vision systems are a better way to read accurate barcodes in this case. It can check a lot of barcodes in a short amount of time and do it well.
These kinds of systems can be set up to send any product with wrong or broken barcode to the manufacturing department for review.
9. Product Packing with Computer Vision
Product packing is very important for companies that make things because it keeps the item from getting broken. Often, manufacturing companies use computer vision-based systems to keep their packaging standards up to par. These kinds of technologies are used a lot in computer vision applications for the pharmaceutical industry, where it is crucial to count the number of pieces before packaging.
These companies must always keep a certain number of whole tablets or capsules in each bottle. Computer vision helps do the job with the accuracy and speed that is needed. Also, the system can be set up to look for damage on the final packaging and send back any items that are damaged.
Another use case is inspecting goods as they come in to find problems early and automatically keep track of possible insurance claims.
10. Productivity Analytics
Analytics of productivity monitor the effects of changes made in the workplace, including how staff members allocate their time and resources and how they use different technologies. These types of data have the potential to deliver insightful knowledge on time management, staff productivity, and workplace collaboration.
Lean management solutions based on computer vision seek to measure and evaluate processes in an objective manner using camera-based vision systems.
Industry Expectations: Future of Manufacturing
Computer vision can be used in a lot of different ways in manufacturing, and many of these uses come from other industries. Human detection and tracking are already used a lot in retail applications, for example. But optimizing production and automating safety systems often give more value than other use cases because they improve efficiency.
As deep learning technology gets better, we expect it to be used in a lot more applications, making it possible to power computer vision on a large scale.
New technologies like Edge AI make it possible to use deep learning in AI vision systems that can be scaled up for use in the real world. Moving machine learning from the cloud to the edge is the next big thing.
The goal is to use computers connected to cameras to do image processing on the device itself. Edge ML makes it possible to run computer vision apps that are fast, reliable, and private.
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