Application Research of Data Mining in MES Quality Management

Abstract

With the increasing market competition, enterprises have continuously raised the requirements for product quality in order to gain a favorable position in the market competition. In industrial production, MES quality information management mainly involves the collection, statistical analysis, and utilization of information data that affect quality in daily production. In actual industrial production, the formation of product quality is the result of the interaction of numerous factors. Due to the many factors and scenarios involved, traditional data statistical analysis methods cannot accurately and effectively analyze the collected relevant data, and fail to fully mine the value of data. This paper proposes the application of data mining technology to MES quality information management systems and elaborates on the use of the K-means algorithm and Apriori association rule algorithm to analyze the related processing rules of parts in the production and processing process. The algorithm model is used to analyze the actual production and processing data of a certain enterprise, and finally, the value and application of parts association processing rules in actual enterprise production management are summarized.