A prediction model of the extrusion deformation with residual stress on 6063 aluminum alloy aeronautical plate considering different extrusion parameters

2020 ◽  
Vol 107 (3-4) ◽  
pp. 1671-1681
Author(s):  
Qiong Wu ◽  
Nian-Pu Xue ◽  
Yi-Du Zhang ◽  
Han-Jun Gao ◽  
Jian Wu
Keyword(s):  
Residual Stress ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
6063 Aluminum Alloy

scholarly journals Experimental Analysis and Prediction Model of Milling-Induced Residual Stress of Aeronautical Aluminum Alloys

2021 ◽  
Vol 11 (13) ◽  
pp. 5881
Author(s):  
Shouhua Yi ◽  
Yunxin Wu ◽  
Hai Gong ◽  
Chenxi Peng ◽  
Yongbiao He
Keyword(s):  
Residual Stress ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
End Milling ◽  
Orthogonal Experiment ◽  
7075 Aluminum Alloy ◽  
Side Milling ◽  
Milling Parameters ◽  
Thin Walled ◽  
Machining Deformation Prediction

Aeronautical thin-walled frame workpieces are usually obtained by milling aluminum alloy plates. The residual stress within the workpiece has a significant influence on the deformation due to the relatively low rigidity of the workpiece. To accurately predict the milling-induced residual stress, this paper describes an orthogonal experiment for milling 7075 aluminum alloy plates. The milling-induced residual stress at different surface depths of the workpiece, without initial stress, is obtained. The influence of the milling parameters on the residual stress is revealed. The parameters include milling speed, feed per tooth, milling width, and cutting depth. The experimental results show that the residual stress depth in the workpiece surface is within 0.12 mm, and the residual stress depth of the end milling is slightly greater than that of the side milling. The calculation models of residual stress and milling parameters for two milling methods are formulated based on regression analysis, and the sensitivity coefficients of parameters to residual stress are calculated. The residual stress prediction model for milling 7075 aluminum alloy plates is proposed based on a back-propagation neural network and genetic algorithm. The findings suggest that the proposed model has a high accuracy, and the prediction error is between 0–14 MPa. It provides basic data for machining deformation prediction of aluminum alloy thin-walled workpieces, which has significant application potential.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

scholarly journals Residual stress in butt welded 5083 aluminum alloy

1970 ◽  
Vol 20 (1) ◽  
pp. 7-13
Author(s):  
Eiji TANAKA ◽  
Katsuhiko HIRATA ◽  
Katsuji TAKEUCHI
Keyword(s):  
Residual Stress ◽  
Aluminum Alloy ◽  
5083 Aluminum Alloy

Polycrystal Simulations Investigating the Effect of Additional Slip System Availability in a 6063 Aluminum Alloy at Elevated Temperature

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Antoinette M. Maniatty ◽  
David J. Littlewood ◽  
Jing Lu
Keyword(s):  
Aluminum Alloy ◽  
Elevated Temperature ◽  
Compression Test ◽  
Elevated Temperatures ◽  
Material Model ◽  
Grain Structure ◽  
Slip Systems ◽  
Modeling Framework ◽  
Element Analysis ◽  
6063 Aluminum Alloy

In order to better understand and predict the intragrain heterogeneous deformation in a 6063 aluminum alloy deformed at an elevated temperature, when additional slip systems beyond the usual octahedral slip systems are active, a modeling framework for analyzing representative polycrystals under these conditions is presented. A model polycrystal that has a similar microstructure to that observed in the material under consideration is modeled with a finite element analysis. A large number of elements per grain (more than 1000) are used to capture well the intragranular heterogeneous response. The polycrystal model is analyzed with three different sets of initial orientations. A compression test is used to calibrate the material model, and a macroscale simulation of the compression test is used to define the deformation history applied to the model polycrystal. In order to reduce boundary condition effects, periodic boundary conditions are applied to the model polycrystal. To investigate the effect of additional slip systems expected to be active at elevated temperatures, the results considering only the 12 {111}⟨110⟩ slip systems are compared to the results with the additional 12 {110}⟨110⟩ and {001}⟨110⟩ slip systems available (i.e., 24 available slip systems). The resulting predicted grain structure and texture are compared to the experimentally observed grain structure and texture in the 6063 aluminum alloy compression sample as well as to the available data in the literature, and the intragranular misorientations are studied.

scholarly journals Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

2022 ◽  
Vol 12 (2) ◽  
pp. 757
Author(s):  
Xiaofeng Wang ◽  
Baochang Liu ◽  
Jiaqi Yun ◽  
Xueqi Wang ◽  
Haoliang Bai
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Thermal Deformation ◽  
Heating Temperature ◽  
Cooling Water ◽  
Interference Fit ◽  
Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

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