Multi-variety and small-batch production quality forecasting by novel data-driven grey Weibull model

Abstract

With the coming of intelligent manufacturing, multi-variety and small-batch production mode has gradually become popular. Aiming at the characteristics of high dimensional information and limited samples in this mode, a novel data-driven grey Weibull model is established for product quality prediction. Firstly, a high-dimensional information from the production process is integrated as the process variation using a data-driven method. Then, the quality prediction function is deducted by considering that process variation follows Weibull distribution-based mechanism, the Hausdorff difference scheme is adopted to weaken the error from the difference to the differential, heuristic algorithm is selected to optimize the distribution parameters of the model. Finally, an experimental analysis is designed using the dataset from some personalized customization manufacturer in China. Results show that the proposed model is not only superior to the other eight models in terms of stability and prediction accuracy, but also boasts the features of amalgamation of data-driven and mechanism-driven methods, which can simultaneously process high-dimensional information and limited samples in multi-variety and small-batch production system.