Using uncertain chemical and thermal data to predict product quality in a casting process

Process and casting data from different sources have been collected and merged for the purpose of predicting, and determining what factors affect, the quality of cast products in a foundry. One problem is that the measurements cannot be directly aligned, since they are collected at different points in time, and instead they have to be approximated for specific time points, hence introducing uncertainty. An approach for addressing this problem is investigated, where uncertain numeric feature values are represented by intervals and random forests are extended to handle such intervals. A preliminary experiment shows that the suggested way of forming the intervals, together with the extension of random forests, results in higher predictive performance compared to using single (expected) values for the uncertain features together with standard random forests.