What manufacturers need to know about optimizing operations with machine vision

From healthcare and pharmaceuticals to food and beverage, manufacturing processes around the world remain inefficient. Despite the best efforts of engineering teams, subpar product design, lack of effective communication, and human error lead to almost $8 billion waste per year.

It goes without saying that this significantly affects a company’s bottom line and the environment, making it a critical issue. Therefore, manufacturing companies are exploring various solutions such as machine vision to increase efficiency, streamline manufacturing processes, reduce waste and drive innovation.

Simply put, computer vision is a field of artificial intelligence (AI) that allows computers to interpret and understand visual information from sources like images and video. Take advantage of large amounts of data, process input images, label objects in these images, and find patterns within them. Although this technology has been around for years, recent advances have meant that current systems are now 99% accurate compared to 50% less than a decade ago.

However, only 10% of organizations are currently using computer vision to power their business operations. However, more and more manufacturing companies are researching or implementing this technology as the benefits become more visible. Now is the time to dive deeper.

Understanding how machine vision sees

Data collection is key to making a computer vision system work flawlessly. First, cameras and sensors installed on the assembly line capture images and upload them to a server. The system then learns to identify the various parts and stages of the production process and classifies defects and anomalies based on the type and severity of the problem. As the system receives more data and feedback from the assembly line, it continually evolves and improves.

To illustrate this, suppose you are running a pharmaceutical production line. In that case, a computer vision system can allow you to accurately verify pill size, shape variations, defects, or total count. When there’s a problem during the manufacturing process, you’ll receive alerts, analysis reports, and actionable insights through notifications on connected devices.

Range of manufacturer use cases for machine vision

From equipment failures to poor planning and quality control issues, numerous factors can cause bottlenecks and slowdowns in the manufacturing process. But computer vision systems can detect and track the movement of products and machinery in a production facility, allowing manufacturing companies to fix these problems.

For example, through computer vision, companies can monitor equipment and machinery for signs of wear. In this way, project managers can schedule maintenance and repairs more effectively, thereby reducing downtime. When equipment and machinery are in good working order, companies can maintain production levels, reduce the risk of workplace accidents, and meet health and safety requirements.

Another main use of machine vision is to improve product quality. Manufacturers understand that ensuring their products meet standards, defect-free, and regulatory requirements can be a real challenge, especially when large quantities are involved. Computer vision can help them accurately inspect products at high speeds and find even the smallest defects that human operators may miss, improving product quality and reducing waste.

On top of it all, implementing a computer vision system allows companies to detect safety equipment and improper equipment use, crowded scaffolding, and falling objects, while also assessing safety levels. Therefore, this technology can help prevent accidents and save thousands of people of work-related injuries.

Considerations for implementing machine vision

Machine vision is a rapidly evolving technology that has the potential to revolutionize the manufacturing industry. But it’s crucial that companies understand the realities of implementing this innovative technology before jumping on the bandwagon.

Since each product and its defects are unique, implementing a computer vision model that works for one product line does not guarantee that it will work the same way for another.

Therefore, to make more informed decisions, avoid excessive spending, and determine which machine vision solution will be most useful, companies must:

• Identify your specific needs and set goals.
• Investigate available machine vision options.
• Carry out pilot tests to evaluate the performance of the solution.
• Make sure the solution can scale to meet your future needs as they grow.

Although computer vision solutions have the power to help manufacturers save time and money, implementing them can be a significant investment.

This is because before implementing a solution, organizations must prepare the infrastructure and do the necessary legwork, which means investing in cameras, installation, and data collection tools.

The bottom line is that computer vision is transforming the manufacturing industry by harnessing the power of visual information. It allows companies to increase product quality, reduce waste and create a safer work environment for their employees. However, since this technology offers unique solutions for each use case and requires expensive hardware, manufacturing companies must set specific goals to optimize the use of machine vision.

Sunil Kardam is the head of logistics and supply chain for SBU in Gramener.