ABSTRACT
Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R2 value of 0.919.
ABSTRACT
Titanium alloy materials are used in a variety of engineering applications in the aerospace, aircraft, electronics, and shipbuilding industries, and due to the continuous improvement of the contemporary age, surface integrity needs to be improved for engineering applications. Belt grinding parameters and levels directly affect the surface integrity of titanium alloys (TC4), which further affects the fatigue life of the titanium alloys during service. In order to investigate the surface integrity of titanium alloys at different roughness levels, the surfaces were repeatedly ground with the same type and different models of abrasive belts. The results showed that at roughness Ra levels of 0.4 µm to 0.2 µm, the compressive residual stresses decreased with increasing linear velocity and there were problems with large surface morphological defects. At the roughness Ra of 0.2 µm or less, grinding improves the surface morphology, the compressive residual stress increases with increasing feed rate, and the surface hardness decreases with increasing linear velocity. In addition, the research facilitates the engineering of grinding parameters and levels that affect surface integrity under different roughness conditions, providing a theoretical basis and practical reference.
ABSTRACT
It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.
ABSTRACT
The surface quality and profile accuracy of a radar fiberglass radome are determined by the manufacturing of the fiber-reinforced-plastic (FRP) complex curved mold. The surface quality, thickness uniformity, and shape accuracy of the mold seriously affect the temperature and deformation control during the manufacturing process of the radome, thus affecting the antenna's serviceability, including its wave permeability and stability. Abrasive belt grinding is an effective method for processing FRP materials. However, issues regarding the profile fitting of the abrasive belt section line contact state and its influence on the precision of complex curved surfaces have not been solved, which seriously affects the processing quality. Here, an FRP complex curved surface mold surface based on the least-squares method was established. The local two-dimensional line contact and profile contour trajectory were obtained by the algorithm of optimal trajectory planning. Based on this, a grinding experiment was carried out. The experiments showed that the surface roughness based on this method was reduced from 0.503 to 0.289 µm, and the contour accuracy was improved by 16.9% compared with the conventional error. Through our analysis, the following conclusions can be drawn: the algorithm can effectively solve the problem of line contact surface fitting and significantly improve the precision of an FRP complex surface.
ABSTRACT
Titanium alloy materials are widely used in the design of key parts, such as aeroengine blades and integral blades. The surface residual stress has a great influence on the fatigue life of the parts mentioned above. Presently, abrasive belt grinding can form residual stress on the surface. However, the formation mechanism has not yet been revealed, providing the impetus for the present study. First of all, the surface residual stress is characterized based on Bragg's law. The influence of contact force, reciprocating frequency, and feed speed on the residual stress of a titanium alloy abrasive belt grinding is obtained using an experimental method. The residual stress model is simulated by the tensile force on the surface of the model, and the fatigue life of the bar under a sinusoidal tensile load is analyzed by simulating the fatigue test of the titanium alloy bar. Finally, fatigue testing and fracture analysis are carried out. The experimental results show that with the increase of the grinding contact force, increase of the reciprocating frequency, and decrease of the feed speed, the residual compressive stress on the surface of the parts increases and the fatigue life is higher at the same working stress level. It also shows that the residual compressive stress produced by abrasive belt grinding is in the range of 120â»300 MPa. The fatigue simulation curve's inflection point appears at the level of 550 MPa. The error between the simulation data and the experimental data is less than 10%, which shows the accuracy of the simulation experiment. The fracture morphology at room temperature is a ductile fracture with fine equiaxed dimples.
