Research On The Intelligent Identification Method Of Hard And Brittle Stone Processing Status

A country's overall national strength and its international competitiveness are often measured by the manufacturing industry.Traditional manufacturing machining method is always processed by using the cutting parameters such as rotation speed,feed speed and depth of cut.However,the traditional manufacturing machining method cannot be monitored based on the cutting condition,and this method will reduce the efficiency of cutting process and cutting quality.Therefore,the manufacturing system must have the ability to realize the objective sensing and dynamic adjustment of production system base on the monitoring information of manufacturing process.The effect of process condition monitoring is directly related to the various operation links of the monitoring signal,and is mainly affected by the signal acquisition,filtering,extracted feature and optimization.Scholars have also done a lot of researches on the intelligent identification and damage diagnosis of the processing status at home and abroad.The source of this article is the National Natural Science Foundation of China(51705341).For the intelligent identification of processing status of the hard and brittle stone,the following research contents are proposed:(1)The removal mechanism of stone is analyzed in this article,and the influence of physical and chemical properties of stone to the processing performance,as well as the relationship between cutting force and cutting parameters through cutting force experiment.The experimental results show that the cutting force decrease with the spindle speed,and increase with the feed rate and depth of cut,and the influence of cutting depth on cutting force and the inhomogeneity of stone will cause the fluctuation of cutting force.The cutting force information can be used as cutting parameter.The basis for dynamic adjustment provides theoretical support.(2)The transmission of the milling force and the electric mechanical conversion process are studied.The research results show that it is advisable to measure the milling force indirectly by feeding the servo current.Next,the collected current signal and cutting force signal are analyzed to find the frequency band,it provides a theoretical basis for the intelligent identification of cutting states based on cutting force signals and current signals.(3)Aiming at the recognition of cutting state during cutting process,an intelligent recognition algorithm for cutting load and current signal based on density clustering algorithm is proposed.This method is established by using density clustering algorithm to identify and judge cutting force load and current signal characteristics.The cluster classification algorithm uses the distance metric as the cluster classification criterion.The core point of the cluster is used as the cutting load and the current signal learning object.The cutting load and the ideal value of the current signal are used to make the judgment,and the intelligent recognition of the cutting state is realized.The paper applies the algorithm and combines Agent technology to intelligently identify and visualize the cutting force load conditions under high-speed milling conditions.This article takes the stone processing process as the research object.Research shows,the algorithm can realize the intelligent identification and judgment of the cutting process status,which can improve processing efficiency and processing quality.This research lays a theoretical foundation for intelligent optimization of cutting parameters and provides a new perspective for the future development of intelligent manufacturing.