The Research And Development Of Intelligent Numerical Control Tool Management System For Small And Medium Enterprises

Nowadays,NC machining technology has become the most widely used processing technology as its high efficiency and processing quality,which leads numerical control(NC)tool to become one of the most important manufacturing resources.In this case,the management of the NC tool resources determines the quality of the product and the economic benefit of the enterprise to a great extent.However,most enterprises,especially small and medium enterprises,still utilize the traditional management mode,which has been difficult to meet the requirements of the growing number of NC tools and then,it becomes a restriction on the further development.Based on the analysis of the tool management system at home and abroad and the requirements of one machine tool Co.,Ltd as well as one gear transmission Co.,Ltd,this paper develops a set of intelligent tool management system for small and medium-sized enterprises in order to help the enterprise management department to master the real-time information of all the cutting tools.By this way,the indirect cost of cutting tools,such as redundant procurement,loss and improper assembly of cutting tools,could be reduced hopefully.Firstly,the technology of tool coding and identification is studied.A flexible coding scheme covering parts and combined tools is proposed to facilitate the tool identification and information tracking.In addition,based on the analysis of the characters of parts and combined tools,a DM code based part management strategy and a RFID based combined tool management scheme are proposed respectively in this paper.As a result,the tool information tracking and automatic recognition are realized.Secondly,based on the analysis of the traditional tool selection process and the characters of processing constraints,a rule-based reasoning(RBR)method based on Prolog is proposed to match the mandatory constraints,and a fitness calculation method is also proposed to match the case.On this basis,a hybrid reasoning method,combining CBR and RBR could be obtained,which provides decision support for tool selection in workshop.Thirdly,a tool wear monitoring model based on an improved artificial bee colony algorithm and extreme learning machine(ELM)is built.In order to get the monitoring vector of this model,several methods like time domain analysis and empirical mode decomposition(EMD)are applied to extract and select the features of the sensor signals.At the same time,the tool wear is used as the output of this monitoring model.The results show that compared with the traditional neural network algorithm and the ultimate learning machine,the model has high precision,simple structure,and the recognition accuracy on the test set is over 97%,which can meet the actual production requirements.Finally,in terms of the above theoretical research and the requirement analysis,an intelligent tool management system based on Browser/Server(B/S)framework is developed.Seven modules,that is,system management,tool inventory management,tool purchase management,basic information maintenance,tool wear management and tool selection management,are designed to guarantee the effective and accurate management of the cutting tools.At present,the system has been applied in a digital workshop of the mentioned machine tool Co.,Ltd.It works well and improves the information and intelligence of the tool management in the enterprise.