关键词: 数据挖掘, 分步聚类, T.K-means算法, MapReduce架构, 铣削参数优化

Abstract: With the advent of the big data era,the traditional data mining technology can no longer meet the requirements of massive data processing under the background of intelligent manufacturing.In view of the lack of computing capacity and low mining efficiency in massive data mining,a step-by-step clustering method was introduced,and then the classical K-means clustering algorithm was improved on the MapReduce framework of Hadoop platform under a cloud computing environment.Using this framework,a new algorithm for solving the massive data mining tasks was generated.The performance of T.K-means algorithm was examined in mining the virtual data of milling Ti-6Al-4V(TC4) titanium alloy.Then the relationship between operating parameters and performance indicators was produced and used to guide optimization operation of the machine tool.Mining results showed that the proposed algorithm could be effectively applied in the determination of the optimization targeted values to achieve the purpose of optimizing the surface roughness of workpiece.The obtained optimization targeted values were representative of optimum operation conditions of the surface roughness of TC4 titanium alloy milling by machine tool.

Key words: data mining, step by step clustering, T.K-means algorithm, MapReduce framework, milling parameters optimization