Sampling via the aggregation value for data-driven manufacturing.

Data-driven modelling has shown promising potential in many industrial applications, while the expensive and time-consuming labelling of experimental and simulation data restricts its further development. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. In this research, the aggregation value is defined using a novel representation for the value of a sample set by modelling the invisible redundant information as the overlaps of neighbouring values. The sampling problem is hence converted to the maximisation of the submodular function over the aggregation value. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods. The research outcome also indicates its appealing potential to reduce labelling efforts for more data-scarcity scenarios.