Exploiting engineering data to build decision metrics (2/2)
In our previous blog post, we’ve discussed Cluster Analysis in detail. Cluster Analysis (or clustering) is a so-called unsupervised machine learning approach available to Optimus users to help them structure their engineering data sets. In this post, we’re digging deeper into another unsupervised machine learning technique available in Optimus, the Self-Organizing Maps (SOM).
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Exploiting engineering data to build decision metrics
Engineers regularly get buried under massive amounts of data generated through simulation and physical testing. As a result they spend a lot of time and effort to access and identify the data that matter most – assuming they have sufficient time to exploit all the data and turn these into decision metrics.
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A manufacturer’s perspective on critical PIDO advancements
Meeting up with leading manufacturers at last year’s Optimus World Conference provided great opportunity to learn about their latest process integration and design optimization (PIDO) achievements. They clearly expressed that more Optimus PIDO technology advancements will play a critical role in meeting the development challenges they see ahead of them.
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