As Artificial Intelligence (AI) and machine learning continue to evolve, the impact these technologies have on the manufacturing industry, particularly automation, also grows. This growth presents many opportunities for innovative solutions to common automation challenges.
One of the biggest opportunities is in the application of “machine vision” or “vision integration.” By integrating machine vision cameras and associated tools into your production line, your AI-powered industrial equipment will show vastly improved performance in detecting, analyzing, and correcting problems to help achieve optimal production output.
In addition to improved production efficiency, machine vision tools can also aid your onsite safety. All of these advantages lead to reduced costs, improved resource utilization, increased uptime, optimized human labor, and, ultimately, greater client satisfaction.
“Machine vision is useful for many things,” confirms longtime Ahaus Tool & Engineering controls engineer Larry Wirrig.
“We’re talking anything from basic part presence to inspection of components, to robot guidance for picking and placing those components. It can be extremely helpful for so much of what we do as a manufacturer of custom workholding products and automation solutions.”
Wirrig went on to say that machine vision cameras and elements can additionally assist the production line with ensuring that full assembly of products are completed with all components of the product being oriented properly.
Human supervision is still essential to any production line, regardless of the use of AI-powered equipment. But the integration of machine vision tools allows for a closer, more precise inspection of a fabricated product than a human eye alone could achieve.
“Machine vision is often used at times when you simply can’t do a physical check on your own for the part presence and orientation,” Wirrig continued. “This is when using machine vision can be the only option we have to probe and verify components for full assembly.”
Experienced engineers such as Wirrig can accomplish this task by taking a picture with machine vision cameras and using complementary tools in those cameras to reveal what is required for final steps in proper project completion.
With the aid of on-hand machine learning components, Wirrig and his colleagues on the floor can use character recognition to improve verification of required text on product parts.
“We can better verify the text is correct,” says Wirrig. “And with machine vision, we can also better inspect for quality defects overall. These may include machining defects or, for instance, a nicked O-ring or packaging joint.”
“In the end, leveraging the value of machine vision allows us at Ahaus to better determine that all the components are there in the finished product and are of the correct quality,” concluded Wirrig.
“For all of these reasons and more, we probably use machine vision on seventy-five percent of the projects we work on, and we’re all eager to see where this technology is going next in the near future.”
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