Data Analytics – Sandman

The data analytics driven algorithms are the heart of this application.
The predictive modelling provides the optimal sand properties and optimal rejections corresponding to each metal part. The model correlates casting rejection with sand parameter and provides the most optimal sand parameters range to operate, which can result in consistently minimising the optimal rejection predicted by SANDMAN.
For any given data the algorithm predicts the highest influencing parameters which may impact the rejections
But how do we arrive at the level of the exact additive? A unique and complex model framework, for prescriptive analytics, SANDMIX Analytics prescribes Dose- by-Need additive quantity for each batch. This allows foundries to operate and sustain the process stability of their sand system in an optimum and dynamically balanced manner.