AI is rapidly shifting from an abstract buzzword to a practical tool, especially in manufacturing environments where downtime, quality, and safety are constant concerns. What’s changing isn’t just the technology; it’s how accessible it has become. With industrial cameras and predictive maintenance apps, AI can now solve very specific operational problems.
Steel, aluminum, and specialty metals manufacturers often ask me how they can put AI to work on their plant floors. Below are some of the most common real‑world use cases they’re exploring.
Foreign Object Detection
An obvious AI use case is foreign object detection. Detecting unwanted objects not only prevents the production of faulty products but also mitigates downtime by preventing blocked or starved lines. Additionally, it can prevent damage depending on the nature of the foreign object and its method of entry. This application typically requires a Cognex-type industrial camera and can be easily implemented in the right lighting conditions using Excel-based configurations.
Monitoring Sensorless Objects
AI can also monitor objects that lack sensors. For instance, an AI system can monitor rotating spools, ensuring they are always in motion, and send email notifications if they stop. This prevents damage caused by increased friction when spools hang up. With normal ambient temperature and no hazardous material concerns, this can be achieved with a webcam and the Python library, PyTorch.
Quality Control
AI-powered vision systems can be used for quality control. Detecting imperfections like dents, nicks, and scratches on steel coils is crucial for both outgoing finished products and incoming materials. Identifying issues early can prevent significant scrap and wasted time. This type of solution enhances overall product quality and efficiency.
Emission Control
Emission prevention is another compelling use for an AI application. For instance, molten metal can produce harmful emissions under certain conditions. These emissions not only damage air quality but also lead to fines from regulatory bodies like the EPA. The solution involves using an IP camera and a Python library to detect these emission “burps,” triggering alerts to operators who can then adjust conditions to reduce emissions.
What’s Next
AI is transforming manufacturing by providing customized solutions tailored to specific needs and data. While the path to an AI-based solution may be riskier compared to traditional upgrades, the end result is a highly customized and efficient system. Companies are already finding competitive advantages with AI.
If the use cases above interest you, or if you’ve got your own ideas, contact me. Flexware will help you think outside the box and leverage AI to optimize your manufacturing processes.
