A hybrid data mining approach to quality assurance of manufacturing process

Quality assurance (QA) is a process employed to ensure a certain level of quality in a product or service. One of the techniques in QA is to predict the product quality based on the product features. However, traditional QA techniques have faced some drawbacks such as heavily depending on the collection and analysis of data and frequently dealing with uncertainty processing. In order to improve the effectiveness during a QA process, a hybrid approach incorporated with data mining techniques such as rough set theory (RST), fuzzy logic (FL) and genetic algorithm (GA) is proposed in this paper. Based on an empirical case study, the proposed solution approach provides great promise in QA.