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Applications of Computer Vision in the Manufacturing industry

August 19, 2022

What is Computer Vision?

Computer vision is one of the branches of artificial intelligence that instructs and equips machines to comprehend the visual environment. Digital pictures and deep learning models can be used by computers to precisely detect, classify, and respond to objects. Computer vision's objective is to give computing systems the ability to accurately recognize an object or person in a digital image and perform the necessary action.

Industry 4.0

Industry 4.0 is modernizing manufacturing and enhancing the competitiveness of the western industrial sector. It is centered on advanced robotics and automation, new forms of human-machine interaction, vast troves of data, and increased connectivity. With the help of the Internet of Things (IoT) and highly effective, automated robotics, manufacturers will be able to gather, analyze, and take action on enormous stockpiles of data like never before. What is the outcome for business owners? - A product of superior caliber at a more affordable price.

Benefits of computer vision in manufacturing

1. Removes errors from quality assurance processes: A worker can easily miss a number of minor issues that can arise during manufacturing processes. Controlling them is essential since the output may suffer from a lack of quality. Additionally, if the problem gets out of hand, it may lead to fines and reputational damage.

2. Increased productivity: According to Deloitte's 2019 Smart Factory report, the use of CV powered robots and other automation systems quickens manufacturing cycles, resulting in a 12% increase in labor productivity and a 10% increase in total production output.

3. Strengthens Security in the Manufacturing Environment: Companies can use computer vision to support secure operations. Additions of face recognition systems and contactless security systems are among the many potential measures. Only those with a valid authorization are permitted access. Automated algorithms can support quality assurance and safety precautions.

4. Cost optimization: Higher productivity combined with decreased machine downtime from automation and CV based maintenance results in lower operating costs overall.

Use cases of computer vision in manufacturing 

1. Predictive maintenance:

Predictive maintenance allows manufacturing organizations to prevent wear and tear on machines and thus reduce the risk of failure. Computer vision can be a savior for both machines and, most crucially, humans. Additionally, it can be used to continuously monitor intricate manufacturing processes in a range of industrial settings.

2. Reading text and barcodes:

Barcodes may be quickly and painlessly scanned with the help of the optical character recognition (OCR) technique. It provides real-time data viewing and analysis for further action. Text recognition methods include barcode recognition, optical mark recognition, and intelligent character recognition (ICR). These technologies can be used by businesses to interpret handwritten text, identify text from scanned photos and documents, route components in production lines, and detect checkboxes.

3. Defects Reduction:

When the quality control procedure is manual, the likelihood of errors remains high. Therefore, it is essential to automate the procedure and reduce potential problems. For higher production standards, computer vision in manufacturing automates the quality-checking process.

4. Improving worker safety:

If safety regulations aren't up to par, accidents in the workplace are to be expected. Businesses, however, cannot afford to be careless. To find trouble spots, computer vision technology analyzes the environment and all the equipment. The system then generates reports and notifies operators when action is required. The technology will send out alerts in the event of accidents to take preventative action.

Industry outlook and what's next

The technologies that enable Industry 4.0 use existing data as well as a variety of additional data sources, such as data from connected assets, to improve manufacturing processes, provide end-to-end information streams throughout the value chain, and introduce new services and business models. With its enormous potential and cutting-edge technology, Industry 4.0 necessitates a sizable initial outlay. Costs would undoubtedly be greater for larger businesses. A significant return on investment is promised by the anticipated payback, which includes connected, smart gadgets and an automated production process.

Conclusion

A growing number of businesses are able to achieve outsized impact across numerous KPIs by utilizing digital technologies across the most efficient use cases. Businesses that can implement Industry 4.0 at scale are restructuring their operations to not just deal with today's most difficult disruptions, but also to get ready for tomorrow's new disruptions.

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