In a manufacturing process the later a defect is noticed, the more expensive it is to rectify the defect.
Using sensor data such as temperature, carbon dioxide levels, pressure and the presence of other gasses, it was possible to predict the quality of lime (used for cement), which was measured by carbon dioxide levels.
How to Customize
- Sensor data at every stage of the manufacturing process and data representing the quality of the output (sensor data, images, video or sound)
- Applicable to steel production, lime production, or any other production process
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.