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applications of computer vision

Modern consumers are well-informed and more confident about their choices. Manufacturers have to deliver products that exactly match consumer expectations, continuously innovate, and ensure delivery at the right time. So, manufacturers need to transform their operations altogether. In recent years, artificial intelligence has emerged at the forefront of modern manufacturing operations to enable manufacturers to revolutionize processes and innovate faster. Applications of Computer vision (CV) are one of the AI solutions that has radically transformed the way manufacturers operate – and that is our topic for this blog.

In this piece, we shall cover nine of its best use cases applications in the manufacturing landscape.

1. Product Defect Detection:

Product defect is one of the most common causes of worry among manufacturers. Even with extreme scrutiny and inspection, some product defects remain undetected by the human eye. Recurring defects cause delivery delays and compromise brand name.

AI-powered computer vision detects product issues along the assembly line and sends timely alerts, ensuring all products are free from errors and manufactured within the allotted time.

2. Predictive Maintenance:

Corrosion and damage to the machines are common during the manufacturing process. Although engineers can track and predict maintenance requirements for machines, the process can be quite costly, time-consuming, and prone to error.

Machine learning can help manufacturers track their product’s health in real-time and can predict future corrosion based on data gathered.

3. Employee Safety:

The amount spent on ensuring workers’ well being significantly impacts a company’s budget and undermines its ability to invest in new ventures. Businesses spend a whopping $170 billion a year on costs associated with occupational injuries and illnesses, and these expenditures come straight out of the company’s profits (OSHA).  Therefore, it is crucial to protect your workers at all costs within the workplace vicinity.

Artificial intelligence can provide a virtual map to effectively analyze the factory floor area and protect your workers against potential hazards. From detecting spillage of hazardous materials to tracking workers’ whereabouts in real-time, CV can address workplace safety needs.

4. Productivity Tracking:

Maximum efficiency has been a goal for all manufacturers but has never been quite attainable. If employees are not working to their full potential, it can drive down performance and increase time to market.

With CV, employee monitoring makes it a much more realistic goal. Through real-time monitoring with the help of artificial intelligence algorithms, motion measurement is possible. This leads to the identification and elimination of non-value-adding activities to significantly increase productivity in the workplace.

5. Inventory Management:

Inventory management is another crucial operation during manufacturing. From warehousing to delivery, there should be complete transparency and smooth communication to ensure the success of the supply chain and avoid logistic failures. However, inventory is accurate only 63% of the time (Flexis).

This signifies that manual tracking of the inventory takes a lot of time and results in frequent errors. Applications of computer vision can offer enhanced visibility into the warehouse workflow and significantly improve your supply chain management by counting products, automating warehouse operations, and tracking product stock’s status in real-time.

6. Barcode Analysis:

Barcodes are very important to manage information about products. While printing them is quite easy, rechecking them to see if they are readable or not is quite a hassle. Manual checking requires workers to read barcodes one by one, which can be time-consuming. 

CV can check the barcodes for readability and accuracy through a scanner before they reach the packaging stage. This automation saves employees’ precious time and energy. It also streamlines record-keeping and reduces wastage.

7. Quality Packaging:

Packing is the most critical part of computer vision in manufacturing as inaccurate packaging can result in product damage and result in significant loss of revenue. Consumer dissatisfaction with the product can also damage brand reputation.

While manual tracking of accurate packaging can be an arduous process, automation of packaging quality control is possible through these applications of computer vision, once manufacturing is completed. The products that don’t get accurately packed are sent back for re-packing immediately, thus saving brands from returns and exchanges.

Conclusion:

CV’s vast range of capabilities can dramatically improve the performance of both employees and equipment. It can create safer workplaces and help manufacturers to build and develop smart factories.

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Sanveed Adnan

Sanveed is a Content Strategist at Xavor Corporation, where she plans, creates, and manages content. When she is not working, you can find her binge-watching TV shows, painting or reading philosophy articles.