Tool Wear Analysis And Tool Wear Rate Prediction For BTA Drilling

Tool wear is one of the most important problems in the cutting field and also one of the prominent problems in deep hole processing.In many high precision deep hole machining processes,it is not allowed to stop and return the tool in the middle to avoid damaging the surface of the machined hole.Furthermore,deep hole tools are usually very expensive,the rational use of tools can improve the quality of production and processing and reduce the cost of processing.With the continuous progress of science and technology,the rapid replacement of mechanical processing products,high strength,high hardness and a variety of new materials which is hard to process,the quality of deep hole processing,processing efficiency and tool use efficiency have much higher requirements.In actual machining process,however,only 38% of the deep hole processing cutting tools run out of their life.In this article,the wear depth and wear rate of BTA machining tools were analyzed and predicted,thus the effective use of the cutting tool will be realized,It is of great importance for the normal conduct of deep hole and the improvement of drilling quality.In this paper,the static analysis,modal analysis and harmonic response analysis of BTA cutter are performed with ANSYS software,the deformation of the tool under different axial forces and the first ten order natural frequencies of the tool are obtained,the critical speed of the tool was also obtained.the process of BTA drilling was simulated by use Deform-3D software,the effects of workpiece material,tool material,machining mode and initial machining temperature on tool wear were studied.The simulation values of tool wear rate,temperature,axial force and torque were recorded based on TC4.A fuzzy neural network model with 27 fuzzy rules was established by using temperature,axial force and torque as input and tool wear rate as output.Based on the network,the effective prediction of tool wear rate is realized and it is compared with the traditional BP network model.,And the influence factors of tool wear rate and the simulation value of wear rate are verified in this article.The results show that BTA tool wear is affected by many factors,including workpiece material,tool material,relative motion of tool and workpiece,and initial machining temperature.The wear rate of BTA tool increases with the increase of temperature,axial force and torque.Using fuzzy neural network to predict tool wear rate can overcome thedisadvantage that traditional BP neural network is difficult to express the specific relationship between input and output,network training is easy to fall into a local minimum value,and the prediction error is large and so on.Predicting tool wear rate,then controling tool wear rate on time by relevent means,throughing this way we can use of tools effectively and reduce the risk of workpiece quality deterioration due to tool wear.It is significant to improve the tool life and the quality of deep hole machining.