Quick detection of product quality based on clustering hypersphere model

Abstract

• A k -means clustering method is used to segment the original data for reducing false detection rate. • A mirror sphere is designed as the reference point for speeding up the detection process. • Proposed method based on the clustering hypersphere model can adapt to various shape distributions of the process data. With the rapid development of industry 4.0, intelligent methods for detecting product quality have attached considerable interests. In traditional quality control methods, the process data is required to meet the requirements of independent and identical distribution, which limits the industrial applications. In this paper, a fast product quality detection method based on a clustering hypersphere model is proposed. First, to make the detection boundary more flexible, a simplified classification method based on k -means clustering is designed. Next, the smallest closed hypersphere is built for each subset, and its radius and center are calculated by a sequence minimum optimization algorithm. Then, by exploring the data distribution in the feature space, the accurate original image of the mirror sphere center can be obtained. Finally, compared with the traditional methods, the proposed method can achieve lower false-detection rate and faster detection speed according to the simulation and experimental results.