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Research on process optimization and rapid prediction method of thermal vibration stress relief for 2219 aluminum alloy rings

  • Shuguang Chen , Hanjun Gao EMAIL logo , Minghui Lin , Shaofeng Wu and Qiong Wu
Published/Copyright: May 5, 2022
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REVIEWS ON ADVANCED MATERIALS SCIENCE
From the journal REVIEWS ON ADVANCED MATERIALS SCIENCE Volume 61 Issue 1

Abstract

2219 aluminum alloy rings are important part of liquid cryogenic rocket fuel tanks. Residual stress is inevitably introduced in the forming process of the rings due to the nonlinear thermomechanical coupling conditions, which will affect its mechanical properties, fatigue properties, corrosion resistance, and dimensional stability. Thermal vibratory stress relief (TVSR) has great potential in reducing residual stress, and process optimization of TVSR is necessary to further improve its application, but it is rarely reported. In this study, process optimization of roll formed 2219 aluminum alloy rings is conducted. The influence of vibration amplitude, vibration time, vibration frequency, heating time, holding time, and cooling time on TVSR treatment are investigated. Results show that the maximum equivalent residual stress of 2219 aluminum alloy rings can be reduced by 93.6% after optimized TVSR treatment. With the increase in vibration time, heating time, holding time, and cooling time, the maximum equivalent stress decreases. However, the increase in the vibration amplitude results in an increase in the maximum equivalent stress. Further, a genetically optimized artificial neural network intelligent optimization algorithm is applied to quickly predict the TVSR effect of 2219 aluminum alloy rings.

Keywords: 2219 aluminum alloy; thermal vibration stress relief; process optimization; genetically optimized neural networks

1 Introduction

As a heat treatable precipitation hardening Al-Cu alloy, 2219 aluminum alloy is considered to be one of the most promising materials in the aerospace industry owing to its light weight, excellent stress corrosion resistance [1], weldability [2,3], and good mechanical properties over a wide temperature range from −250 to 250°C [4,5]. A typical application of 2219 aluminum alloy is in the liquid cryogenic rocket fuel tanks, whose top and bottom covers are composed of 2219 aluminum alloy, and transition rings. The manufacturing methods of aluminum alloy rings mainly include die-forging forming, bending and welding forming, and ring rolling forming [6]. The ring rolling has high forming accuracy and good mechanical properties, and has received extensive attention in the industry in recent years [7].

The ring rolling forming process of aluminum alloy involves material nonlinearity and boundary conditions (such as temperature and load) nonlinearity. Under the nonlinear thermomechanical coupling conditions, residual stresses will inevitably be generated [8,9]. During the service process of the rings, the residual stresses will gradually release, resulting in deformation of the ring and loss of shape accuracy [10,11,12]. In general, residual stresses affect the mechanical properties, fatigue properties, corrosion resistance, and dimensional stability of high-performance ring parts [13]. Large amplitude and uneven distribution seriously affect the manufacturing accuracy and service reliability of rolled rings. In addition, aluminum alloys usually needs to be machined into thin-walled parts in order to meet the needs of aerospace, and the material removal rate in this process can be as high as 95% [14]. During the machining process, the original residual stress balance state is broken as the material is removed, a new self-equilibrating residual stress field forms within the remaining material with the appearance of warping, torsion, and other deformation phenomena [15]. The residual stress of the blank is one of the important influencing factors of machining deformation [10,11].

In recent years, some meaningful studies in stress relief and homogenization techniques have been conducted. Common stress relief methods include thermal stress relief (TSR) and vibration stress relief (VSR), and thermal vibratory stress relief (TVSR). Tanner and Robinson [16] reported that the residual stress amplitude of aluminum alloy 2014 forging greatly reduced after TSR treatment at 170°C per 12 h. Williams et al. [17] investigated TSR treatment (700°C per 12 h) on residual stress in additive manufacturing 316L stainless steel, and the result showed that the peak residual stress reduced by around 10% in the vertical sample and 40% in the horizontal sample. In order to reduce surface residual stress, Fredj and Sidhom [18] conducted TSR treatment on AISI304 stainless steel. Dong et al. [19] reported that the residual stress of cylindrical welded component is reduced by about 50% after 20 min of VSR treatment. Zhang et al. [20] observed that the average stress relief ratio reached 53% in welded 6082 aluminum alloy plates after ultrasonic VSR treatment. Qu et al. [21] performed VSR treatment on TA15 titanium alloy thick plates, and the results showed that the surface residual stress was reduced by nearly 60%. Wang et al. [22] studied residual stress relaxation and the texture evolution of cold-rolled AZ31 Mg alloys using the VSR technique. Zhang et al. [23] developed an elastic-plastic finite element (FE) model to study the VSR parameters of 304 stainless steel cold-rolled strip.

Compared with TSR and VSR, TVSR has obvious advantages due to higher efficiency and better effect [24,25]. Lv and Zhang [24] found that the stress relief ratio of TVSR is 42.5% higher than that of VSR. Chen et al. [25] reported that TSR treatment effectively reduced the residual stress of welded 2219 aluminum alloys in the direction perpendicular to the weld, TVSR treatment has more advantages in both longitudinal and transversal directions. Li et al. [26] proposed that higher vibration amplitude and temperature are beneficial to reduce residual stress in 50 mm thick DH36 steel welded plates. Wu et al. [27] compared the effect of TVSR treatment with different heating time on 2219 aluminum alloy. Xu et al. [28] conducted TVSR treatment on a thin-walled 7075 aluminum alloy to control the machining deformation, the results show that the maximum deformation decreased by 58.28% compared to the one without any treatment. The residual stress of SiCp/Al composites can be reduced by more than 70% at a temperature of 185°C and a vibration frequency of 56 Hz [29]. Gao et al. [30] analyzed and summarized the stress relief mechanism of TVSR, and proposed a FE modeling method using the creep equation and temperature-dependent thermophysical parameters to simulate TVSR process, the simulation and experimental results are in good agreement. A FE model for simulating the heat treatment process was established using a similar method by Baere et al. [31].

From the above studies, it can be found that TVSR has great potential and application value in reducing the residual stress. TVSR contains many process conditions as it is a coupling method of TSR and VSR. The process optimization of TVSR is very necessary to further improve its application, but it is rarely reported, which may due to the cost and time consumption of the optimization process. In this study, TVSR process optimization of roll formed 2219 aluminum alloy rings is conducted. The influence of vibration amplitude, vibration time, vibration frequency, heating time, holding time, and cooling time on TVSR treatment are investigated. Further, a genetically optimized artificial neural network intelligent algorithm is applied in order to improve the prediction efficiency of TVSR.

2 Materials and methods

2.1 Specimens and TVSR processes

The 2219 aluminum alloy rings used in this article are produced by Shanghai Xijie Metal Products Co., Ltd (Shanghai, China). As shown in Figure 1, the outer diameter of the ring is 450 mm, inner diameter is 366 mm, and the height is 105 mm. The chemical compositions of 2219 aluminum alloys are shown in Table 1. The general processes for TVSR treatment of aluminum alloy are also given in Figure 1. It can be described as: first the sample is heated to a certain temperature, then subjected to vibration with heat preservation, and after holding for a period of time, it is cooled to room temperature.

Figure 1 The 2219 aluminum alloy ring and general TVSR processes.
Figure 1

The 2219 aluminum alloy ring and general TVSR processes.

Table 1

Chemical composition of 2219 aluminum alloy (%)

Cu Mg Mn Fe Si Zn Ti Zr V Al
6.227 0.005 0.243 0.142 0.056 0.009 0.039 0.179 0.087 The rest

2.2 FE simulations for TVSR

The general modeling and analysis flow of TVSR is shown in Figure 2, and the entire analysis is conducted in the FE analysis software ANSYS. The ring is modeled according to the actual size which is given in Figure 1, and eight nodes element Solid185 is selected for meshing as the element can be used for analysis of plasticity, super-elasticity, creep, and large deformation. In order to limit the rigid body displacement of the workpiece in three directions and ensure the convergence of the calculation, spring elements Combin14 with a smaller stiffness are spread on the surface nodes of the ring, and the stiffness is 10−4 mm·N−1. Before the TVSR numerical simulations, a modal analysis is required to obtain the natural frequencies and modes of the 2219 aluminum alloy ring. We can find that the 7th-order natural frequency of the ring is 619.32 Hz. The relative total deformation of the 7th order is extracted, and it can be seen that the ring vibration is symmetrical. Vibration in TVSR simulation is imposed by nodal displacement, node displacement X can be described as equation (1):

(1) X = p { u 0 } sin ( 2 π f t ) ,

where p is a scale factor, {u 0} is the node relative total displacement of the 7th-order mode on the upper surface of the ring, f is the 7th-order natural frequency of the ring, and t is the time.

Figure 2 General modeling and analysis flow of TVSR.
Figure 2

General modeling and analysis flow of TVSR.

After completing the modal analysis of the ring, the analysis type needs to be converted to transient analysis. The initial residual stress of the 2219 aluminum alloy ring is assigned to the model by the “Inistate” command in ANSYS. The initial residual stress of the 2219 aluminum alloy ring is measured by a drilling method equipment. The schematic of the measurement system and the locations of the residual stress measurement points are shown in Figure 3. When the residual stress measurements are conducted, first, the strain gauges are glued to the polished measurement points and then connected with strain recorder. Next the ring is fixed and the drilling process can be carried out. The residual stress can be calculated from the measured strain variation during the drilling process in the stress analysis interface. Eight points of 2219 ring are measured, the measuring depths of each point are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1 mm.

Figure 3 Scheme of (a) residual stress measuring system and (b) measuring points.
Figure 3

Scheme of (a) residual stress measuring system and (b) measuring points.

The measurement results of the initial residual stress at different depths and directions of the eight measurement points of the 2219 aluminum alloy ring are shown in Figure 4. The more regular the octagon drawn by the residual stresses of eight measuring points, the more uniform the residual stress distribution. On the contrary, the uniformity of the residual stress distribution of the ring is worse. It can be seen that within 1 mm from the outer surface of the 2219 aluminum alloy ring, there are “sharp corners” in the radar images of the residual stress distribution of the eight test points in different directions. Compared with the radial and hoop directions, the residual stress in the shear direction looks more uniform. This may be due to the quenching treatment of the ring, which introduced high amplitude and uneven residual stress during the rapid cooling process.

Figure 4 Residual stress at different depths: (a) 0.1 mm; (b) 0.2 mm; (c) 0.3 mm; (d) 0.4 mm; (e) 0.5 mm; (f) 0.6 mm; (g) 0.7 mm; (h) 0.8 mm; (i) 0.9 mm, and (j) 1.0 mm.
Figure 4

Residual stress at different depths: (a) 0.1 mm; (b) 0.2 mm; (c) 0.3 mm; (d) 0.4 mm; (e) 0.5 mm; (f) 0.6 mm; (g) 0.7 mm; (h) 0.8 mm; (i) 0.9 mm, and (j) 1.0 mm.

In the TVSR process, the peak value of residual stress decreases and the distribution is more uniform. Stress relaxation plays an important role in reducing residual stress, in order to accurately describe the stress in the TVSR process, constitutive equations that reflect the stress relaxation behavior must be included in the FE model. Stress relaxation is another manifestation of the creep phenomenon [32,33]. Therefore, creep equation is adopted in many studies to describe the stress relaxation [34,35,36]. In this study, the stress relaxation behavior in FE simulations are described by the Norton creep equation [37] described as equation (2).

(2) ε ̇ c = C 1 σ C 2 ε C 2 e − C 4 / T ,

where ε ̇ c is the creep strain rate, σ is the stress, ε is the strain, T is the temperature, and C 1, C 2, C 3, and C 4 are constant coefficients and can be determined by stress relaxation tests. Stress relaxation tests are conducted using RD-50 electronic creep endurance testing machine produced by Changchun Kexin Instrument Co., Ltd (Changchun, China). According to the stress measurement results of the 2219 aluminum alloy ring, stress relaxation tests are carried out under the stress conditions of 50, 150, 250, and 350 MPa, at a temperature of 175°C and a holding time of 18 h. The results of stress relaxation tests at four stress levels are shown in Figure 5. The delay function described in equation (3) is chosen to fit the test results for its excellent precision and flexibility.

(3) σ = σ ∞ + a 1 e − t / b 1 + a 2 e − t / b 2 + ⋅ ⋅ ⋅ + a n − 1 e − t / b n − 1 + a n e − t / b n ,

where σ is the instantaneous stress, t is the time, σ ∞ is the remaining stress after stress relaxation, and a 1–a n and b 1–b n are constants determined based on the tests, and the results are shown in Table 2. According to four stress relaxation curve equations, the creep parameters C 1–C 4 of the Norton creep model can be solved, and the results are C 1 = 1.1518 × 10−12, C 2 = 3.40, C 3 = 0.14, and C 4 = 409.75.

Figure 5 Stress relaxation curves of 2219 aluminum alloy at 175℃ under (a) 50 MPa; (b) 150 MPa; (c) 250 MPa; and (d) 350 MPa.
Figure 5

Stress relaxation curves of 2219 aluminum alloy at 175℃ under (a) 50 MPa; (b) 150 MPa; (c) 250 MPa; and (d) 350 MPa.

Table 2

Curve fitting results of stress relaxation of 2219 aluminum alloy

Stress (MPa) Parameters
σ ∞ a 1 b 1 a 2 b 2 a 3 b 3
50 15.859 13.841 55.640 12.801 55.641 7.487 0.832
150 220.261 −23.666 −100.164 −23.669 −100.164 −23.667 −100.164
250 202.553 5.541 0.263 40.062 23.894 10.292 0.045
350 154.149 1096.787 0.022 160.053 31.214 — —

In addition, the simulations of TVSR are thermomechanical coupled. The thermophysical parameters used in the simulations are shown in Figure 6.

Figure 6 Thermophysical parameters of 2219 aluminum alloy [38,39,40]: (a) elastic modulus; (b) specific heat capacity; (c) density; (d) thermal conductivity; (e) thermal expansion coefficient; (f) yield stress.
Figure 6

Thermophysical parameters of 2219 aluminum alloy [38,39,40]: (a) elastic modulus; (b) specific heat capacity; (c) density; (d) thermal conductivity; (e) thermal expansion coefficient; (f) yield stress.

2.3 TVSR processes optimization

Process parameters determine the effect of TVSR. In order to identify the key factors of TVSR and determine the best process parameters, based on the establishment of the simulation model, this section optimizes the parameters of TVSR. As shown in Figure 7, the maximum equivalent stress after TVSR is the optimization objective, six parameters (heating time, vibration time, vibration amplitude, vibration frequency, holding time, and cooling time) in the heating, vibration, holding, and cooling processes of the TVSR are selected for optimization. The modeling process of TVSR in Section 2.2 is parameterized, element data, material data, load data, and boundary data are constant inputs, while the six selected parameters are stored in parameter data as variables. The TVSR simulation models of different variables are solved cyclically, the maximum equivalent stress is extracted from the result file for evaluating the effect of TVSR.

Figure 7 Optimization design process of TVSR parameters.
Figure 7

Optimization design process of TVSR parameters.

In the optimization process of TVSR, a uniform sampling method is used to select three values for each parameter in the design space. The optimization process is very slow due to too many (63) parameter combinations. In order to improve efficiency, the orthogonal arrays method is used for test design, and the six-factor three-level orthogonal table L27(36) is used to combine the parameters. Orthogonal tests of process parameters of TVSR are shown in Table 3.

Table 3

Orthogonal test of process parameters of TVSR

Vibration amplitude ratio Vibration time (min) Vibration frequency (Hz) Heating time (h) Holding time (h) Cooling time (h)
1 1 2 619.321 0.5 1 1
2 1 5 619.321 1 1.5 2
3 1 10 619.321 1.5 2 3
4 1 10 982.105 1 1.5 1
5 1 2 982.105 1.5 2 2
6 1 5 982.105 0.5 1 3
7 1 5 1702.953 1.5 2 1
8 1 10 1702.953 0.5 1 2
9 1 2 1702.953 1 1.5 3
10 2 5 619.321 1 2 1
11 2 10 619.321 1.5 1 2
12 2 2 619.321 0.5 1.5 3
13 2 2 982.105 1.5 1 1
14 2 5 982.105 0.5 1.5 2
15 2 10 982.105 1 2 3
16 2 10 1702.953 0.5 1.5 1
17 2 2 1702.953 1 2 2
18 2 5 1702.953 1.5 1 3
19 3 10 619.321 1.5 1.5 1
20 3 2 619.321 0.5 2 2
21 3 5 619.321 1 1 3
22 3 5 982.105 0.5 2 1
23 3 10 982.105 1 1 2
24 3 2 982.105 1.5 1.5 3
25 3 2 1702.953 1 1 1
26 3 5 1702.953 1.5 1.5 2
27 3 10 1702.953 0.5 2 3

2.4 Rapid prediction based on genetically optimized neural networks

In order to quickly evaluate the TVSR effect of the 2219 aluminum alloy ring, the intelligent optimization prediction algorithm is adopted in this article. Artificial Neural Networks are widely used due to their high computing speed, strong adaptability and fault tolerance, and self-organization ability. However, the training of neural networks is prone to fall into local optimum and prolong the convergence time. Genetic algorithm has a strong adaptive global optimization search ability, which is particularly effective for solving high-dimensional undifferentiable and discontinuous problems. The genetic optimization neural network, which is composed of the two algorithms to avoid their inherent shortcomings, is more accurate and effective.

The structure of the TVSR effect prediction model based on genetically optimized neural networks is shown in Figure 8. First, the neural network structure design for TVSR effect prediction is carried out. The six parameters that affect TVSR are used as input, and the maximum displacement and maximum stress are used as output. Therefore, the number of nodes in the input layer and output layer are six and two respectively. The number of hidden layers selected is two, and the number of neurons is eight. The prediction error of the artificial neural network is defined by the loss function:

(4) min w , b ∈ Ω f ( w , b ) = ∑ i = 1 n | y i − o i ( w , b ) | 2 ,

where n is the total number of training samples, w and b represent the neural network weight and threshold, respectively, Ω is the range of neural network weights and thresholds, y i is the simulation calculated value of the ith sample, and o i is the predicted value of the ith sample.

Figure 8 The structure of the TVSR effect prediction model based on genetically optimized neural networks.
Figure 8

The structure of the TVSR effect prediction model based on genetically optimized neural networks.

In order to minimize the loss function, the weights and thresholds are optimized by genetic algorithm. The weight value matrix from the input layer to the first hidden layer is W 1, the threshold matrix of the first hidden layer is B 1, the weight value matrix from the first hidden layer to the second hidden layer is W 2, the threshold matrix of the second hidden layer is B 2, the weight value matrix from the second hidden layer to the output layer is W 3, and the threshold matrix of the output layer is B 3. W 1, B 1, W 2, B 2, W 3, and B 3 are encoded and the initial population of genetically optimized neural networks is obtained. The minimum mean absolute error of the maximum equivalent stress between simulation and genetically optimized neural networks is used as a target for performing the optimization calculations. The mean absolute error is described as equation (5).

(5) MEA ( y , y ^ ) = 1 m ∑ i = 1 m | y i − y i ^ | ,

where y i is the ith actual output in the training set, y i ^ is the ith predicted output in the training set, and m is the number of training set samples.

3 Results and discussion

3.1 The influence of TVSR process parameters

According to the TVSR process parameters in Table 3, multiple solutions are obtained, and the solution results are shown in Table 4. The orthogonal test table cannot be directly compared between the groups. In order to identify the key parameters of TVSR, range analysis and gray correlation analysis are used to examine the correlation between the indicators.

Table 4

The simulation results of orthogonal test of TVSR process parameters

1 2 3 4 5 6 7 8 9
Maximum equivalent stress (MPa) 27.47 21.72 20.88 69.86 61.92 63.60 24.05 26.80 21.48
Stress relief rate (%) 92.6 91.8 93.4 93.3 93.3 93.4 76.6 78.8 80.3
10 11 12 13 14 15 16 17 18
Maximum equivalent stress (MPa) 21.77 21.78 21.64 76.20 69.15 64.13 22.56 21.66 21.43
Stress relief rate (%) 93.1 93.4 93.4 93.4 93.3 93.3 78.1 77.3 79.4
19 20 21 22 23 24 25 26 27
Maximum equivalent stress (MPa) 21.44 21.77 21.73 71.59 73.94 67.07 26.24 27.71 21.18
Stress relief rate (%) 92.0 91.5 93.5 78.1 77.3 79.4 92.0 91.5 93.5

The results of range analysis are shown in Table 5. K i and k i (i = 1, 2, and 3) are the sum and average of the indicators at the ith level of a certain factor, the range R is the difference between the maximum value k max and the minimum value k min. It can be found that the vibration amplitude ratio, vibration time, vibration frequency, heating time, holding time, and cooling time all have an impact on the effect of TVSR. With the increase in vibration time, heating time, holding time, and cooling time, the maximum equivalent stress of 2219 aluminum alloy ring after TVSR decreases. However, the increase in the vibration amplitude ratio results in an increase in the maximum equivalent stress. The variation range of R reflects the influence of each parameter on the maximum equivalent stress. The greater the R value, the greater the influence of the parameter. The influence of the TVSR process parameters on the maximum equivalent stress from strong to weak is: vibration frequency > cooling time > holding time > vibration amplitude ratio > heating time > vibration time.

Table 5

Results of maximum equivalent stress range analysis

Vibration amplitude ratio Vibration time (min) Vibration frequency (Hz) Heating time (h) Holding time (h) Cooling time (h)
K 1 337.7707772 345.4555830 200.2074626 345.7482561 359.1812595 361.1780468
K 2 340.3225238 342.7363319 617.4440205 342.5349137 342.6253943 346.4371340
K 3 352.6591030 342.5604891 213.1009208 342.4692342 328.9457502 323.1372233
k 1 112.5902591 115.1518610 66.7358209 115.2494187 119.7270865 120.3926823
k 2 113.4408413 114.2454440 205.8146735 114.1783046 114.2084648 115.4790447
k 3 117.5530343 114.1868297 71.0336403 114.1564114 109.6485834 107.7124078
R 4.9627753 0.9650313 139.0788526 1.093007 10.0785031 12.6802745

The gray correlation analysis result of the orthogonal test of TVSR is shown in Figure 9. A larger grey relational degree represents a greater influence of the parameter on the target. The results show that, overall, thermal-related parameters have a greater influence on the effect of TVSR than vibration-related parameters. The order of the influence of the parameters determined according to the degree of correlation on the maximum equivalent stress of the ring is: holding time > vibration frequency > cooling time > heating time > vibration amplitude ratio > vibration time. Combining the results of range analysis and gray correlation analysis, holding time and vibration frequency are key parameters of TVSR of the ring. Although a larger frequency can better reduce the residual stress of the ring, it also means that the output power of the vibration equipment is greater, resulting in an increase in cost. Therefore, based on the above analysis, vibration amplitude ratio = 1, vibration time = 5 min, vibration frequency = 619.321 Hz, heating time = 1 h, holding time = 1 h, cooling time = 2 h are preferred TVSR process parameters.

Figure 9 Gray correlation degree of maximum equivalent stress.
Figure 9

Gray correlation degree of maximum equivalent stress.

3.2 Prediction results based on genetically optimized neural networks

The simulation results of orthogonal test of TVSR process parameters are used as samples to conduct supervised training on genetically optimized neural networks. The mean absolute error of the maximum equivalent stress during the training process is shown in Figure 10. The mean absolute error is about 0.32 at the beginning, as the number of training increases, the error of the genetically optimized neural networks decreases rapidly. After 970 trainings, the error decreases to 0.016, indicating that the neural network can basically be used to quickly predict the maximum equivalent stress of the ring after TVSR.

Figure 10 Result of average absolute error.
Figure 10

Result of average absolute error.

In order to verify the accuracy of the established prediction model based on genetically optimized neural networks, 12 sets of numerical models, which are different from the TVSR parameters in the orthogonal test, are solved. The result is shown in Figure 11. It can be found that the maximum error is about 25 MPa, which is acceptable compared to the larger initial residual stress (up to −500 MPa) of the 2219 aluminum alloy ring. Moreover, if the number of samples is further increased, the prediction accuracy will be predictably higher.

Figure 11 Maximum equivalent stress results of simulation and prediction.
Figure 11

Maximum equivalent stress results of simulation and prediction.

4 Conclusion

In this study, process optimization of roll formed 2219 aluminum alloy rings is conducted. The influence of vibration amplitude, vibration time, vibration frequency, heating time, holding time, and cooling time on TVSR treatment are investigated. In addition, a genetically optimized artificial neural network intelligent optimization algorithm is applied to quickly predict the TVSR effect of the 2219 aluminum alloy rings. Results can be summarized as:

  1. Different TVSR parameters will reduce the different residual stresses of the rings. The maximum equivalent residual stress of the 2219 aluminum alloy rings can be reduced by at least 76.6% after TVSR, and the maximum reduction rate is 93.6%.

  2. With the increase in vibration time, heating time, holding time, and cooling time, the maximum equivalent stress of 2219 aluminum alloy rings after TVSR decreases. However, the increase in the vibration amplitude ratio results in an increase in the maximum equivalent stress.

  3. Holding time and vibration frequency are key parameters of TVSR of the 2219 aluminum alloy rings.

  4. A genetically optimized neural network can be used to quickly predict the effect of TVSR treatment of the 2219 aluminum alloy rings.

Acknowledgment

We acknowledge financial supports from the National Natural Science Foundation of China (52005018), and Defense Industrial Technology Development Program (JCKY2020601C004).

  1. Funding information: The research has been founded by the National Natural Science Foundation of China (52005018) and the Defense Industrial Technology Development Program (JCKY2020601C004).

  2. Author contributions: Shuguang Chen: writing, editing, visualization, methodology, and formal analysis; Hanjun Gao: data curation, editing supervision, discussion, project administration, and funding acquisition; Minghui Lin: experiment; Shaofeng Wu: writing – original draft, conceptualization, formal analysis, visualization, and data curation; Qiong Wu: editing supervision, project administration, and funding acquisition; all authors have discussed and agreed to the published version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The data used to support the findings of this study are available from the corresponding author upon request.

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Received: 2022-01-10
Revised: 2022-02-22
Accepted: 2022-02-23
Published Online: 2022-05-05

© 2022 Shuguang Chen et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/rams-2022-0028
Keywords for this article
2219 aluminum alloy; thermal vibration stress relief; process optimization; genetically optimized neural networks
Creative Commons
BY 4.0

Articles in the same Issue

  1. Review Articles
  2. State of the art, challenges, and emerging trends: Geopolymer composite reinforced by dispersed steel fibers
  3. A review on the properties of concrete reinforced with recycled steel fiber from waste tires
  4. Copper ternary oxides as photocathodes for solar-driven CO2 reduction
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  8. Delivery systems in nanocosmeceuticals
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  13. Deterioration characteristics of recycled aggregate concrete subjected to coupling effect with salt and frost
  14. Novel approach to improve shale stability using super-amphiphobic nanoscale materials in water-based drilling fluids and its field application
  15. Research on the low-frequency multiline spectrum vibration control of offshore platforms
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  17. Response analysis and optimization of the air spring with epistemic uncertainties
  18. Molecular dynamics of C–S–H production in graphene oxide environment
  19. Residual stress relief mechanisms of 2219 Al–Cu alloy by thermal stress relief method
  20. Characteristics and microstructures of the GFRP waste powder/GGBS-based geopolymer paste and concrete
  21. Development and performance evaluation of a novel environmentally friendly adsorbent for waste water-based drilling fluids
  22. Determination of shear stresses in the measurement area of a modified wood sample
  23. Influence of ettringite on the crack self-repairing of cement-based materials in a hydraulic environment
  24. Multiple load recognition and fatigue assessment on longitudinal stop of railway freight car
  25. Synthesis and characterization of nano-SiO2@octadecylbisimidazoline quaternary ammonium salt used as acidizing corrosion inhibitor
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  30. Mechanical behavior and mechanism investigation on the optimized and novel bio-inspired nonpneumatic composite tires
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