Data driven robust optimization of grinding process under uncertainty

Uncertain process parameters present in industrial grinding circuits (IGC) increase the difficulty in modeling IGC. Conventionally, researchers have resorted to box approach for sampling in the uncertain parameter space, mimicking the uncertain parameter realizations, to observe their effects in objective functions and constraints. In case data are scattered in the uncertain parameter space, sampling in the entire range, as done in the box approach, might lead to erroneous results. To mitigate this problem, a sampling technique to generate data points inside the admissible regions is proposed leading to accurate identification of uncertain space. The proposed technique uses neuro-fuzzy c means clustering to create optimal number of clusters in the uncertain parameter space. Data points are generated using SOBOL sampling technique within each cluster boundary obtained by Delaunay triangulations. Using the proposed sampling technique in robust optimization setting and comparing with the box sampling for various sample sizes (500, 1000, 2000, 3000, 4000 and 5000), the efficacy of the proposed method has been established.