Modeling, Perception And Reconstruction On Tool Temperature Distribution In Titanium Alloy Cutting

Titanium alloys are considered as significant materials for manufacturing the critical structural component in aero-engine such as compressor,hood and casing due to its exceptional combination of high strength-to-weight ratio,good mechanical property and high corrosion resistance.They are more and more widely be used in automotive,pharmaceutical and chemical industries.However,titanium alloys are also well-known hard-to-machine materials.Machining titanium alloys generate local high strain,high strain-rate along with sudden temperature rise in the cutting interface,resulting in a typical thermo-mechanical coupled process.The temperature distribution in the cutting interface is hard to be measured by the current means.On the other hand,the temperature field around the cutting interface offers essential information to monitor tool wear,ensure surface quality and detect thermal stress,and it is an important parameter for the cutting process.Therefore,it is of great academic and engineering importance to investigate the thermo-mechanical deforming mechanism in titanium alloys cutting,so as to explore the high-resolution reconstruction and on-line monitoring method for the temperature field of cutting tool.This dissertation focuses on developing modeling,perceptual reconstruction and online monitoring method for the cutting temperature field in Ti-6Al-4V alloy cutting.By studying the visco-plastic deformation behavior of Ti-6Al-4V materials,a temperature-dependent improved material constitutive model that capable of accurately predicting the cutting force,chip morphology and interfacial heat source is proposed.To overcome the problems in direct temperature measurement,a hybrid macro-micro cutting tool temperature reconstruction method is investigated.With low-resolution measurements as basis,high-accuracy temperature field around cutting interface can be reconstructed by the proposed method.Besides,by combining the three-dimensional temperature field reconstruction method with the data-driven artificial neural network(ANN)model,an on-line maximum temperature monitoring method of the cutting tool is established.The remainder of the dissertation offers the following contents:Firstly,by analyzing the normalized experimental data of split Hopkinson pressure bar under different deformation conditions,an improved temperature-dependent material constitutive model is proposed to consider the thermal sensitivity property and microstructure evolution of Ti-6Al-4V alloy.After identifying the parameters of the constitutive model in orthogonal cutting experiment,the improved material model represents different deformation mechanisms under different temperatures.The predicted results are validated by comparing with the cutting forces,chip morphologies,contact-lengthes and shear angles that measured in cutting experiments.By implementing the improved Ti-6Al-4V material model,the cutting simulation accurately predicts the evolution trend of cutting force,feed force and chip morphology in different cutting speed.The thermo-mechnical coupling behavior and adiabatic shear in the cutting interface can also be accurately simulated.Secondly,to overcome the difficulties in directly measuring the temperature distribution in cutting interface,a hybrid method that combines the macro-scale tool heat transfer and micro-scale machining mechanics model is proposed.The boundary conditions of contact heat souce and convection are determined respectively by cutting simulation and fin model.Under different cutting conditions,the high-resolution reconstruction of the cutting tool temperature field can be obtained by numerical updating the initial boundary conditions with temperature measurements using the thermal imager.The hybrid method is illustrated experimentally on a customized orthogonal cutting testbed and validated on a lathe-turning center with inserted thermocouples.Finally,a maximum temperature monitoring method of cutting tool is proposed to solve the problem in on-line monitoring of cutting condition.The model data in a wide range of cutting condition is combined with an artificial neural network(ANN)model.And the tool temperature field that measured by infrared imager is divided into two regions;namely,a far field for solving the heat-transfer coefficient between the tool and ambient temperature,and a near field where the ANN is trained to account for the unknown heat variations at the frictional contact interface.The ANN method,which does not rely on high-resolution images to achieve high-fidelity monitoring,is capable of detecting and interpolating the obscured temperature by the continuity condition of surface temperature field,thus maintaining a reliable full-process and long-term temperature monitoring.