What are the Use Cases of Computer Vision in Manufacturing ?
Manufacturing computer vision focuses on creating artificial systems that can capture, process, and therefore understand physical input from the physical world (mainly factories and other industrial settings) to bring realistic and humane operational benefits to work various performance related functions.
The most primitive forms of computer vision, in production and in other aspects, can recognize specific objects and trigger a response according to a rule based criterion, that is by identifying certain objects in captured images and verifying whether they match n 'group of given parameters. However, this method tends to create a lot of false positives and isn’t very good at handling the nuances and variations that often occur when dealing with unstructured data sources like images or video
Consumer expectations for high quality products are increasing, and maintaining that quality requires accuracy and consistency. To meet this standard, many businesses are automating their production lines to eliminate human error by shifting repetitive tasks to bots Computer vision systems combine the human eye with computer processing power to automate assembly for maximum efficiency, flexibility, and efficiency
2. Predictive maintenance:
Heavy machinery and equipment working in manufacturing plants decline over time; this can lead to errors and crashes These downtimes can be expensive and can lead to significant losses. Computer vision technology can enable consistent and accurate monitoring of manufacturing equipment and alert engineers to maintenance before issues arise Combined with Top IOT use cases in manufacturing Industry and deep learning, these computer vision driven predictive supervision systems can deliver high accuracy and consistency.
3. Quality assessment:
Every manufacturer wants the products coming off the assembly line to be flawless; however, this is a challenge because common products can have defects. Checking conveyor belt items for defects can be a very repetitive and error prone task if done manually
This task is automated by implementing a machine vision quality and defect inspection system implemented through high speed cameras, which inspect all products on a conveyor belt with image and video annotations
These methods are much more accurate and much more efficient than manual quality checks
4. 3D visual surveillance:
3D vision monitoring refers to the analysis of the production process’ 3D model created by the computer vision system. The system creates an accurate representation of the production process and monitors the process through multiple cameras to detect faults or errors
5. Security improvements
Working in an Artificial intelligence in manufacturing industry t can be dangerous since it involves working around with heavy equipment and sometimes in extreme temperatures Factories usually have surveillance systems in place by the manufacturer to monitor employees. This manual inspection can be tedious and inefficient
A manufacturing facility safety monitoring system can monitor employees from door to exit to ensure compliance with all safety guidelines and regulations In the event of an accident, the system equipped with high speed cameras will alert doctors or the relevant authorities
4. Computer Vision Guided Die Cutting
Rotary and Laser Die Cutting is the most popular die cutting technology in the manufacturing process. Rotary uses hard tools and metal drills while laser uses a powerful laser beam. Although laser die cutting is highly accurate, cutting solid materials is challenging and rotary cutting can be used to cut just about anything.
Manufacturers can implement computer vision systems to make rotary die cutting as accurate as laser cutting for any type of design. Once the design process is fed to the computer vision system, the system will guide the die cutting machine, whether laser or rotary cutting, to make the cut accurately
5. Safety and Security Standards
Workers in the manufacturing industry work in extremely dangerous conditions, so the risk of injury is much higher. Failure to adhere to safety and security standards can result in serious personal injury or even death These industries are required to comply with safety standards enforced by regulatory authorities and companies that fail to comply with these standards are subject to penalties For a better approach talk with the AI Experts from a Artificial intelligence development services in USA
While manufacturing companies have cameras installed to monitor the movement of workers in the plant to ensure safety levels, it is basically a manual monitoring system where the worker has to sit back and constantly monitor the video footage. Manual systems are prone to errors and this error can have serious consequences
7. Packaging quality:
In some manufacturing companies, it is important to calculate the number of parts produced before packing them in a container. Doing this work manually can cause a lot of errors. This problem is most common in pharmaceuticals and commercial products The inclusion of a computer vision system in the packaging process to calculate the number of parts, verifies whether the packaging standards are being followed.
8 Barcode Analysis:
Another important feature is barcode verification. Most products have a barcode. The packaging department should check that the printed barcodes are correct and legible Manually verifying the barcodes of thousands of products requires many man hours and is error-prone and costly. Computer Vision Use Cases in the Manufacturing can verify barcodes easily and replace any products with incorrect barcodes
9 Inventory management:
Computer vision systems can help count stock, maintain inventory status in the warehouse, and automate and alert management if anything that needs to be processed is below demand. Computer vision systems can avoid human errors in stock counting
Benefits of Computer vision applications in manufacturing
The increasing implementation of computer vision in manufacturing processes certainly represents one of the key aspects of this change, as it contributes significantly to the strengthening of the manufacturing sector in terms of:
More productivity: The deployment of computer vision robots and other 24/7 automation systems simplifies the manufacturing cycle, resulting in 12% growth in worker productivity and 10% in total output, according to a report by Deloitte and MAPI's 2019 Smart Factory.
Cost optimization: Increased productivity, coupled with reduced plant downtime achieved through machine-based maintenance and computer vision (up to 50%, according to McKinsey estimates), also translates into reductions normal in operating expenses
Improved quality: Computer vision driven robots perform in a more precise manner, ensuring better product quality and an overall 10 20% cost savings in QA operational costs, as demonstrated by McKinsey.
Worker safety: Manufacturing computer vision can also be used to identify inefficiencies that could endanger factory workers, as well as monitor worker conditions and detect signs of fatigue or discomfort
If you are interested read here to know about Computer Vision Applications in 2022
How computer vision can change an industry: Computer vision has many applications in manufacturing Let’s take a look at the most important applications of computer vision in manufacturing.
1.
Identify leaks:
Traditional methods for detecting oil, gas or liquid leaks from plant tissue are unsafe, prone to error and labor intensive With computer vision, industrial companies can monitor and identify leaks more accurately and safely AI powered cameras hold the ability to monitor and automatically detect leaks in real time. And, when a leak is detected, a notification is immediately sent to the appropriate department
2. Leak detection: In heavy industry, corrosion (rust) poses a serious occupational safety hazard to workers Also, the traditional method of leak detection and diagnosis through human interpretation may be flawed Computer vision can continuously monitor and automatically detect leaks It implements a Applications of deep learning to automate the leak detection process Thus, it can help manufacturers reduce human risk and associated costs.
3 Collection of materials:
The manual assembly process is time consuming and very expensive However, the increasing role of automation in the industrial space has replaced these traditional systems with fully automated systems
Conclusion
To develop such Ai technology , you need to consult t the best Mobile app development company in USA , Manufacturing computer vision and machine learning, along with many other technologies involved in the digitization of manufacturing processes, has proven to be a valuable partner for manufacturing enterprises, resulting in significant reductions in costs, output and quality stronger, more efficient, and increased worker safety. Of course, organizations should not take the adoption of computer vision lightly, since its very implementation may end up being more tricky than expected
Author Bio:
I am Harika. I work as a SEO Executive at USM Business systems, The best Mobile app development company IN USA , experienced in the creation of iOS and Android apps As a technical content writer, I am curious to explore and write the Articles on latest Mobile app development trends, Artificial intelligence and Internet of Things, For more reference you can Also follow me on LinkedIn