When I think about my interactions with technology each day, the reality of artificial intelligence strikes me as far more tangible than I originally assumed. I asked Alexa to reorder trash bags, I used Google Maps to find the fastest route to work, I scrolled through social media to catch up on my friends and family. On a larger scale, I’ve used Lyft, Siri, Amazon, Netflix and more. These companies are using machine learning and AI tools to create powerful changes in consumer behavior.
This makes investing in AI tools not just a business luxury but a business necessity. This includes spray technology. We’re making significant investments in tools used to visualize, diagnose and, eventually, heal a spray. The SprayScan mPT, for instance, uses a laser to capture a spray’s footprint. This image or video can be uploaded to the SprayScan software to assess and compare sprays and nozzles.
While this is groundbreaking, it is only the beginning. Imagine an integrated system where a laser and camera exist in an application at the point of spraying. This combination is constantly feeding images to the SprayScan software in real-time. As the spray nozzle deteriorates, clogs, corrodes or fails, we’ll be notified by the software instantly.
Now, we’re not done yet. While we could stop here and have the system send a signal to the user saying, “Something’s not right.” But, what if, prior to integrating the system, we used machine learning to teach the system to recognize when something is not right, diagnose the problem. and suggest a solution. So rather than be notified that something is going wrong in your spray application, you’re notified of a solution to the problem backed by aggregated data from hundreds of other applications just like yours.
This is the future of spray systems and manufacturing applications everywhere. AI tools that diagnose and heal your application. And we’re only knocking at the door…