Tuesday, March 02, 2021 9:09:07 AM
When adopting new technologies where there’s a lot of uncertainty, like additive manufacturing, an important step is using NDT after the part’s been made. Nondestructive testing can be very expensive, especially if it incorporates capital equipment CT scanners (used to analyze the structural integrity of manufactured parts). Sensors in the machines can link to models that are built up from a large data set learned from the manufacturing process for specific parts. Once sensor data is available, it’s possible to build a machine learning model using the sensor data—for example, to correlate with a defect observed in the CT scan. The sensor data can flag parts that the analytic model suggests are likely to be defective without requiring the part to be CT-scanned. Only those parts would be scanned instead of routinely scanning all parts as they come off the line."
"What Is the Future of AI in Manufacturing?
This scenario suggests an opportunity to effectively package an end-to-end work process to sell to a manufacturer. It could include everything from software to the physical machinery in the factory, the digital twin of the machinery, the ordering system that exchanges data with the factory’s supply-chain systems, and the analytics to monitor the process and collect data as inputs move through the system. Essentially, creating “factory in a box” systems.
Factory in a Box
Such a system would allow you, as a manufacturer, to look at the part that you made today, compare it to the part you made yesterday, see that quality assurance is being done, and analyze the NDT that’s been done for each process on the line. The feedback would help you understand exactly what parameters you used to make those parts and then, from the sensor data, see where there are defects.
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