Systematical Study On Milling Cutters And Machining Efficiencies For Granite

With the extensive adoption of computer technology in granite milling processing, granite carving products are widely used in real life and also have a broader application prospect.Nowadays granite processing equipment and technology has been matured, while the efficiency use of the tool has become a big problem on granite milling.The experiencedp rocessing parameters have been always used and improper processing parameters always cause serious wear to cutting tool,even rupture it or shorten its life.The milling force is one of the crucial factors thataffect tool using performances, at the same time tool wear also has a great impact on the processing performances.Currently,more research results on diamond saw blade are obtained in granite processing field,which provide a reference for the practical manufacturing,however, fewer studies in granite milling tools hinder the rapid development of granite milling technology and applications. There fore, factors that influence machining efficiency in granite milling tools were studied. The main work in this paper is as follows:(1) Beginning with indentation fracture theory, through the analysis ofthe contact stress generated by squeezing the sharp indenter with the granite and the crack development process to study on the processingmechanism of granite milling. The indentation simulation experiment used ANSYS/LS-DYNA software to simulate crack development process and stress state when the diamond particles were pressed into the granite. The maximum stress of granite and diamond were obtained through loading different feed speed. We took a conclusion that when thecut depth is certain, the diamond largest stress forceincreased with the feed speed up. Besides, we carried out the indentation experiments by using of the sharp indenter by photomicrographs and had further description and analysis on the crack composition and characteristics.(2) This paper analysed geometrical parameters of diamond milling granite and then deducedthe theoretical formula of the arc length of mutual reaction on workpiece and cutting tool and the average cutting thickness during granite milling.Starting from the equal volume before and after cutting, this paperanalysed the force on single particle diamond microcosmically, then set up and deduced the formula of related characteristic factors. The factors mainly include milling cutter unit length of the static effective grains'number N1, unit area of the static effective grains' number Ns and the dynamic effective grains'number Ad.At same time, the paper made a systematic formula derivation and analyzed the force from microscopic on milling theoretically.Granite engraving model andthe calculating formula of the milling force were both established. The result have shown that milling force increased in proportion to cutting depthin the granite milling process,grown nearly half exponentially with the feed rate, and decreased up to half exponentially with thespindle speed.(3) For the further verification of the theoretical analysisand providing sample data for the subsequent prediction analysis, we established a hardware system for detecting the granite milling force online and used the VC++6.0 to develop a corresponding detection systematic software, makingit possible to test milling force dynamicallyduring processing.(4)Granite milling force tests were performed under detecting experimental platform,so did the single factor test of the diamond milling granite,orthogonal and tool fracture experiment. The effects of process parameters on the milling force were investigated,such as spindle speed n(r/min), feed ing speed v?(mm/min) and cutting depthap(mm).The corresponding 100 groups of test samples were obtained under the single factor test of process parameters, the corresponding 9 groups of test specimenswere obtained under orthogonal test of process parameters; to obtain the critical milling forces of tool fracture, the diamond cutter fracture tests were made and the maximum milling force were measured when diamond milling cutteris at rupture. The experimental data analysis provided sample datum for subsequent neural network modeling and verification.(5) BP neural networks and RBF neural networks were separately used to set up the model that could predict the milling force of the granite carving process; network design,weight initialization, network training and simulationwere realized through calling for the related functions in the toolbox of MATLAB; the feasibility of the forecasting model was verified through the test data which made the forecasting milling force more accurate due to different machining parameters. Based on the test data,the prediction accuracy of neural networks wascornpared. The results have shown thatusing BP neural network to predict the milling force can guarantee the average error of the predicted value being less than 6%, but the monomer error fluctuates heavily,and the average error between it and the theoretical value of the milling force is also considerable; by contrast, not onlyis the milling force of the average error which predicted by RBF neural network prediction model less than the one of BP neural network,but also the monomer error volatility is relatively stable. The average error between it and the theoretical value of the milling force is 2.5173%, compared with BP neural network, which is larger. Also this method is more realistic and feasible. According to the process parameters,it can predict the milling force of diamond cutter during granite carving processing more accurately.(6) We analyzed surface wear of milling tools in granite processing according to the amount of wear from the milling andthe microscopic form of the wear in the surface of milling. We studied the relationship between the amount of wear and the milling process parameters, which included spindle speed n(r/min), feed rate v?(mm/min) and depth of cut ap(mm).We researched the main form of the wear and influence on the tool lifetime through the analyze for surface morphology of milling cutters. Combined with the condition of tool wear, we carried out experiments to explore new tools and brought the plasma thermal spraying technology into the preparation of granite cutting tools, and performed experiments on the relevant tools for tool wear,given the detail analysis for experimental results.Through the analysis for RBF neural network prediction model, tool fracture and tool wear test, this paper has given the steps and concrete examples in improvingefficiency and choosing optimal technological parametersabout granite milling machining process, which provides a referable method and basis for the selection of process parameters in granite milling process. By optimizing the processing parameters,it plays a leading role on reducing production costs and improving processing efficiency in granite milling.