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In CNC machining, the detection of tool status is of great importance, as tool damage not only affects the quality and efficiency of the process, but can also lead to serious machine and personal accidents. The damage of the tool is caused by wear and damage. The wear is the phenomenon of the surface material consumed by the contact and friction between the tool and the workpiece during the machining process; and the damage is caused by chipping, breaking, plastic deformation, etc. Loss of cutting ability, which includes brittle fracture and plastic breakage. Brittle fracture is the chipping, chipping and spalling of the tool under mechanical and impact effects without obvious wear. The plastic breakage is a phenomenon in which the tool is plastically flowed on the surface layer in contact with the workpiece due to high temperature and high pressure during cutting, and the cutting ability is lost. At present, the detection of tools mainly adopts three strategies: manual detection, offline detection and online detection. Manual inspection is the inspection of the state of the tool by the worker during the processing; offline inspection is to specifically test the tool before processing, and predict its life to see if it is qualified for the current processing; online detection is also called real-time detection, that is The tool is detected in real time during the machining process, and the corresponding processing is performed according to the detected result. At present, there are many algorithms for tool detection. Some use the theoretical calculation of the change of the stress on the tool to judge the damage of the tool. Some use the method of time series analysis to detect the tool, and some use the neural network technology. The tool is tested, and the other uses the wavelet transform theory and neural network technology to detect the tool, but they are mainly discussed theoretically. Considering that the plastic damage of the tool in NC machining is relatively rare, and the safety relationship between the wear and the CNC is not very large and it can be processed by offline detection. This paper takes the ball-end tool commonly used in NC machining as the research object, and it is in the brittle fracture. The real-time detection of brittle fractures is studied, and the occurrence of such fractures will have a serious impact on the quality of the processing and the machine itself. We believe that there are tiny cracks in the tool itself, and use the neural network to establish the load model of the ball-end tool. It is judged by online detection whether the micro-crack will expand under the load condition at this time. If it is possible to expand, we think that the load is Dangerous and reduce the load on the tool by reducing the feed of the tool to ensure the safety of the tool. The real-time detection of the tool The establishment of the ball-end tool load model As mentioned above, the load on the tool during CNC machining is related to many factors, but considering the characteristics of the ball-end tool and the need for real-time machining, this paper only considers the influence. The factors of the spindle speed, feed rate, depth of cutting, and cutting performance of the machined material are the four factors of the ball load.
F=f(s,v,h,m)...................(1)
among them:
F——load vector;
H——the depth of the cutting;
S——the speed of the spindle;
m - the cutting performance of the material.
v - the amount of feed;
Obviously, Equation 1 only gives a general relationship between the load and various influencing factors. In order to find the specific expression of the relationship between the load and each influencing factor, the specific magnitude of each factor's influence on the load must be obtained. Or use mathematical methods such as differential geometry to make complex derivations, or use experimental methods to obtain the influence coefficient of each factor, but the model thus established is difficult to adapt to the changing environment, and the real-time detection effect used in NC machining is not very satisfactory. This paper uses neural network technology to process the model and use it in real-time detection of tools. The real-time detection principle of the tool The principle of real-time detection of the tool is to measure the cutting depth and feed amount of the tool in real time and input the neural network controller to calculate the load of the spindle and the material type of the machined part. The load input detector is obtained. Calculate and compare, if the load exceeds the crack propagation load under the fatigue condition of the tool, reduce the feed rate of the tool, and feed back the decrease of the feed speed to the input information of the CNC controller, so that the CNC controller Make the appropriate controls to change the size of the load to a safe level. The real-time detection principle of the tool is as follows. It is assumed that the microcracks at the joint with the machine tool are composite cracks of type I, II, and III cracks, and the proportion of the three types of cracks is determined according to the magnitudes of the normal stress and the shear stress, so that Formulas can be established based on various specific crack types. As for a, we are based on the average crack length of the tool during its service life. The average length can be detected by non-destructive testing of tools in different life periods.
Summary This paper proposes a method for real-time detection of ball-end tool in NC machining by neural network. This method can calculate the load of the ball-end tool in the real-time process, and judge whether the load exceeds through real-time detection. The load level of the crack propagation of the tool under stress fatigue conditions and corresponding treatment. The method simplifies the factors affecting the load reasonably, making the algorithm of the control model very efficient, so it is especially suitable for real-time detection. Although this paper takes the ball-end tool in numerical control as the research object, in fact, the principle of this method can also be used in other machining and other tools, such as electric machining.
