Milling Parameters Optimization Of Titanium Alloy Based On Data Mining Technology

With the emergence of a series of concepts such as industrial Internet,German Industry 4.0 and Chinese Manufacturers 2025,"intelligence" has become the development trend of today's manufacturing industry,and cutting is one of the main processing methods in the data processing industry.Using data processing technology to optimize data processing parameters is an effective method to improve production efficiency,reduce production cost and optimize slice processing.Cutting path planning,parameter optimization and so on.Data analysis has begun to be used in this field,so the optimization of milling parameters based on data processing technology is studied,which is crucial for intelligent variability of resection processes.First of all,a data analysis platform was constructed from titanium alloy based on Hadoop architecture to analyze the properties related to titanium alloy production corresponding to the corresponding characteristics of big data.The selection of Cloudera CDH product to be developed,as well as the basic knowledge of Flume,Kafka message collection,real-time data processing and Hbase real-time data storage provide prerequisites for future data collection.Secondly,the improvement of K-means central clustering algorithm is completed.This paper analyzes the shortcomings of K-means central clustering algorithm in actual mining application,and improves K-means central clustering algorithm by adding splitting and merging operations to the algorithm and introducing the concept of distributed clustering to cluster performance indicators and milling parameters respectively.Generated virtual data processing: response surface method is used to create a surface roughness and reduction rate,speed,depth of the axial and radial milling parameters: the mathematical model using alternative method to create virtual data attribute and data transmission characteristics by determining the mathematical model of the scope of the four parameters of milling can be generated by virtual data.Therefore,the improved K-means algorithm was implemented on a large data platform and applied to the virtual data created to extract a set of parameters corresponding to the surface roughness and maximum separation speed of the material,and the existence of a set of parameters was verified through experiments effective.