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.

工业研磨电路（IGC）中存在的不确定过程参数增加了对IGC建模的难度。传统上，研究人员采用盒方法在不确定的参数空间中进行采样，模仿不确定的参数实现，以观察其对目标函数和约束的影响。如果数据散布在不确定的参数空间中，那么按照盒方法进行的整个范围的采样可能会导致错误的结果。为了缓解这个问题，一种采样技术可以在内部生成数据点提出了允许区域，以精确识别不确定空间。所提出的技术使用神经模糊c均值聚类在不确定的参数空间中创建最佳数目的聚类。在通过Delaunay三角剖分获得的每个群集边界内，使用SOBOL采样技术生成数据点。使用所提出的采样技术进行鲁棒性优化设置，并与各种样本大小（500、1000、2000、3000、4000和5000）的箱式采样进行比较，从而确定了所提出方法的有效性。