Residual stress prediction method considering ultrasonic peening process parameters of welded joint

Abstract

The invention provides a residual stress prediction method considering ultrasonic peening process parameters of a welded joint, and the method specifically comprises the following steps: carrying out thermal-mechanical coupling simulation of a welded structure based on a finite element method, obtaining a residual stress field of the welded structure, taking the residual stress field as initial stress, and building an ultrasonic peening processing finite element model; on the basis of a hypercube Latin sampling method, a parameter combination sample is extracted, corresponding residual stress data after ultrasonic peening are obtained through simulation, and a residual stress prediction function model containing an undetermined coefficient is established with ultrasonic peening parameters as independent variables. And based on a particle swarm optimization algorithm, training to obtain an undetermined coefficient value which enables a prediction effect to be optimal, and completing establishment of a prediction function model. Therefore, the optimal process parameter combination of ultrasonic peening can be obtained so as to improve the mechanical property of a welded joint. According to the residual stress prediction method considering the ultrasonic impact process parameters of the welding joint, a basis is provided for improving the service performance of a large-scale thin-wall part welding structure.

Description

Technical Field

[0001] The invention belongs to the field of advanced manufacturing such as welding processing, and specifically relates to a residual stress prediction method considering ultrasonic impact process parameters of a welding joint. Background Art

[0002] Thin-walled welding structures are widely used in high-end equipment fields such as aerospace, automobiles and radar. The uneven temperature field during welding will produce thermal stress, resulting in large residual deformation and residual stress. In addition, there are problems such as crack defects and coarse structure in the welded joint, which reduce the service performance of the thin-walled welding structure. To address this problem, ultrasonic impact treatment is used as an effective method to reduce welding residual stress. The impact head impacts the metal surface at a high frequency to cause plastic deformation, thereby changing the original stress field and strengthening the impacted part. At present, most studies have shown that ultrasonic impact treatment can effectively reduce welding residual stress, but less attention has been paid to the response relationship between ultrasonic impact process parameters and joint residual stress. Therefore, a welding-ultrasonic impact coupling numerical model is established to study the influence of ultrasonic impact process parameters on welding residual stress. By establishing a prediction function model for the relationship between residual stress after impact and ultrasonic impact process parameters, the optimal ultrasonic impact process parameters are obtained, which will provide a basis for improving the service performance of large-scale thin-walled welding structures. Summary of the invention

[0003] The purpose of the present invention is to provide a residual stress prediction method taking into account the ultrasonic impact process parameters of a welded joint, so as to obtain the optimal ultrasonic impact treatment process parameters and improve the mechanical properties of the welded joint.

[0004] The technical solution of the present invention is: a residual stress prediction method considering the ultrasonic impact process parameters of the welded joint, the steps are as follows:

[0005] Step 1: Perform thermal-mechanical coupling simulation based on the finite element method to obtain the welding residual stress field and proceed to step 2.

[0006] Step 2: Based on the thermal-mechanical coupling simulation, the welding residual stress field is used as the initial stress to establish the finite element model of ultrasonic impact treatment, and then proceed to step 3.

[0007] Step 3: Use the Latin hypercube sampling method to extract parameters within the range of each ultrasonic impact parameter to obtain parameter combination samples. Set the ultrasonic impact treatment finite element model according to the extracted parameter combination samples to obtain the post-impact residual stress data corresponding to different parameter combinations, and then proceed to step 4.

[0008] Step 4: Based on the mapping relationship between different parameter combinations and their corresponding post-impact residual stress data, a prediction function model is established and the process goes to step 5.

[0009] Step 5: Based on the particle swarm optimization algorithm, the undetermined coefficients in the prediction function model are used as the optimization target object, and the fitness function is defined by the prediction error. The particle swarm optimization algorithm parameters are adjusted until the fitness R 2 The value reaches 0.99 to obtain the optimized prediction function model and go to step 6.

[0010] Step 6: Calculate the optimal ultrasonic impact process parameters based on the optimized prediction function model.

[0011] Compared with the prior art, the present invention has the following significant advantages: by establishing a prediction function model between ultrasonic impact process parameters and residual stress, the present invention can accurately predict the influence of different parameter combinations on residual stress, and can obtain the optimal impact process parameters through an optimization algorithm, thereby improving the mechanical properties of the welded joint, reducing experimental time and cost, and improving the design efficiency of the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of a residual stress prediction method considering ultrasonic impact process parameters of welding joints described in the present invention.

[0013] Figure 2 It is a finite element model of the T-shaped thin-walled part welding structure in an embodiment of the present invention.

[0014] Figure 3 It is a comparison diagram of the stress curves of the impact path before and after ultrasonic impact treatment of the welding structure in the embodiment of the present invention.

[0015] Figure 4 It is a fitness evolution curve diagram of the particle swarm algorithm in an embodiment of the present invention.

[0016] Figure 5 It is a comparison diagram between the predicted value and the actual value of the prediction function in the embodiment of the present invention.

[0017] Figure 6 1 is a distribution diagram of weld grain size before and after ultrasonic impact of a welded structure in an embodiment of the present invention, wherein (a) represents before impact, and (b) represents after impact.

[0018] Figure 7 1 is a graph showing the average hardness of the welded structure before and after ultrasonic impact in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0021] The following will further introduce the specific implementation method, as well as the technical difficulties and inventive points of this invention in combination with this design example.

[0022] Combination Figure 1 , a residual stress prediction method considering the ultrasonic impact process parameters of the welded joint, the steps are as follows:

[0023] Step 1: Perform thermal-mechanical coupling simulation based on the finite element method to obtain the welding residual stress field, as follows:

[0024] The uneven temperature field during welding will produce thermal stress, which will cause residual deformation and residual stress. Based on the finite element method, a welding finite element model is established, thermal-mechanical coupling simulation is completed, and the welding residual stress field is obtained.

[0025] Go to step 2.

[0026] Step 2: Based on the thermal-mechanical coupling simulation, the welding residual stress field is used as the initial stress to establish the finite element model of ultrasonic impact treatment, as follows:

[0027] Ultrasonic impact treatment is an effective method to reduce welding residual stress. It uses an impact head to impact the metal surface at a high frequency to cause plastic deformation, thereby changing the original stress field and strengthening the impacted part. A finite element model of ultrasonic impact treatment is established based on the welding model. The residual stress field obtained by thermal-mechanical coupling simulation is loaded into the model as the initial stress condition, and a velocity load parallel to the weld direction is applied to the ultrasonic impact head to achieve its movement along the weld direction; a displacement load of reciprocating motion perpendicular to the weld surface is applied to simulate the continuous vibration of the impact needle.

[0028] Go to step 3.

[0029] Step 3: Use the Latin hypercube sampling method to extract parameters within the range of each ultrasonic impact parameter (including impact frequency, impact amplitude and impact velocity) to obtain parameter combination samples. Set the ultrasonic impact treatment finite element model according to the extracted parameter combination samples to obtain the residual stress data after impact corresponding to different parameter combinations, as follows:

[0030] When establishing a prediction function model, the accuracy of the model depends on the coverage and representativeness of the training sample data. Latin hypercube sampling is an efficient sampling method that can select sample points more evenly in the multidimensional parameter space, avoiding sample concentration and bias problems, and making the prediction function model more applicable and accurate under different process conditions.

[0031] Within the range of ultrasonic impact treatment parameters, the Latin hypercube sampling method is used to extract parameters to obtain parameter combination samples. The ultrasonic impact simulation is carried out on all parameter combination samples through the established ultrasonic impact treatment finite element model. The stress at the position of the maximum stress point before impact on the ultrasonic impact path is used as an indicator to obtain the post-impact residual stress data corresponding to samples with different parameter combinations.

[0032] Go to step 4.

[0033] Step 4: Based on the mapping relationship between different parameter combinations and their corresponding post-impact residual stress data, a prediction function model is established, as follows:

[0034] First, the effects of impact frequency, impact amplitude and impact velocity on the residual stress value after impact are comprehensively considered; the number of times the ultrasonic impact head impacts the specimen will increase with the increase of impact frequency and the decrease of impact velocity, and the impact depth will increase with the increase of impact amplitude. Therefore, it is believed that the impact degree is proportional to the impact frequency and impact amplitude, and inversely proportional to the impact velocity. Therefore, the comprehensive variable X is defined to satisfy the following formula:

[0035]

[0036] Among them, x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact speed.

[0037] Draw a graph of the comprehensive variable X and the relevant stress value. According to the above graph, it is preliminarily judged that the two are roughly exponentially related, so the following relationship function is defined:

[0038]

[0039] Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, n, and m are all unknown coefficients.

[0040] Then, the correlation between stress value and impact frequency, impact amplitude and impact velocity was considered, and the variation law of related stress value was analyzed when two parameters were unchanged and the third parameter was changed. According to the data curve, the relationship between impact amplitude, impact velocity and stress value is approximately parabolic, and the relationship between impact frequency and stress value fluctuates up and down but is positively correlated as a whole. Therefore, the prediction function model is:

[0041]

[0042] Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, b, c, d, e, f, g, h, k, n, and m are all unknown coefficients.

[0043] Go to step 5.

[0044] Step 5: Based on the particle swarm optimization algorithm, the undetermined coefficients in the prediction function model are used as the optimization target object, and the fitness function is defined by the prediction error. The particle swarm optimization algorithm parameters are adjusted until the fitness R 2 The value reaches 0.99 to obtain the optimized prediction function model, as follows:

[0045] The particle swarm optimization algorithm is used to train the prediction function model to meet the prediction requirements. In the particle swarm algorithm, particles move in the solution space and update their own speed and position by tracking the individual optimal value and the global optimal value of the group until the fitness no longer changes or the maximum number of iterations is reached. In order to balance the global search ability in the early stage and the convergence speed in the later stage, the particle speed inertia weight is set to a linearly decreasing weight;

[0046] The undetermined coefficient is taken as the optimization target object, and the prediction error is set as the inverse of the mean square error between the predicted stress value obtained by the prediction function model and the actual stress value of the sample. The prediction error is used as the fitness function to optimize the undetermined coefficient. The formula is as follows:

[0047]

[0048] Among them, MSE is the mean square error; H is the number of samples, y actual,i is the actual residual stress value of the i-th sample, y predicted,i is the model prediction value under the i-th sample parameter, and F is the fitness.

[0049] By adjusting the particle swarm algorithm parameters until the fitting degree R 2 The value reaches 0.99, and the final output is the optimal coefficient that makes the predicted value consistent with the actual value, thereby obtaining the optimized prediction function model.

[0050] Go to step 6.

[0051] Step 6: Calculate the optimal ultrasonic impact process parameters based on the optimized prediction function model, as follows:

[0052] The optimized prediction function model can accurately predict the residual stress under different ultrasonic impact parameters. The function is optimized and the impact frequency, impact amplitude, impact velocity and other parameters with the lowest output function value are obtained to obtain the best process parameter combination and obtain good mechanical properties of the welded joint.

[0053] Example 1

[0054] Combination Figure 1 The present invention provides a residual stress prediction method considering the ultrasonic impact process parameters of a welded joint, and the steps are as follows:

[0055] Step 1: The uneven temperature field during welding will generate thermal stress, resulting in large residual deformation and residual stress. Based on the finite element method, a welding finite element model is established. Figure 2 , complete the thermal-mechanical coupling simulation, obtain the welding residual stress field data, and proceed to step 2.

[0056] Step 2: Ultrasonic impact treatment can effectively reduce welding residual stress and strengthen the impacted parts, such as Figure 6 , Figure 7 As shown in the figure, based on the thermal-mechanical coupling simulation, the finite element model of ultrasonic impact treatment is established to obtain the residual stress after impact. Figure 3 , as follows:

[0057] On the basis of the welding model, an impact head is added to establish an ultrasonic impact model. The residual stress field obtained by the thermal-mechanical coupling simulation is loaded into the model as the initial stress condition. A velocity load parallel to the weld direction is applied to the ultrasonic impact head to achieve its movement along the weld direction. A displacement load with reciprocating motion perpendicular to the weld surface is applied to simulate the continuous vibration of the impact needle. The simulation can obtain the residual stress field after impact, and then proceed to step 3.

[0058] Step 3: Based on the Latin hypercube sampling method, parameter combination samples are extracted within the ultrasonic impact parameter range, and the ultrasonic impact treatment finite element model is set according to the extracted parameter combination samples to obtain the corresponding residual stress data after impact, as follows:

[0059] When establishing a prediction function model, the accuracy of the model depends on the coverage and representativeness of the training sample data. Latin hypercube sampling is an efficient sampling method that can select sample points more evenly in the multidimensional parameter space, avoiding sample concentration and bias problems, and making the prediction function model more applicable and accurate under different process conditions.

[0060] The ultrasonic impact parameters selected in this example are three factors: impact frequency, impact amplitude and impact velocity. The impact frequency range is 12 to 24 kHz, the impact amplitude range is 1 to 6 μm, and the impact velocity is 30 to 80 mm / s. Within the range of each ultrasonic impact treatment parameter, the Latin hypercube sampling method is used to extract 36 parameter combination samples to ensure that the established prediction function model can more accurately describe the relationship between the ultrasonic impact parameters and the welding residual stress.

[0061] The ultrasonic impact simulation is completed for all parameter combination samples through the established ultrasonic impact model. The stress at the maximum stress point before impact on the ultrasonic impact path is used as an indicator to obtain the post-impact residual stress data corresponding to each parameter combination sample.

[0062] Step 4: Based on the mapping relationship between different parameter combinations and their corresponding post-impact residual stress data, a prediction function model is established, as follows:

[0063] First, the effects of impact frequency, impact amplitude and impact velocity on the residual stress value after impact are comprehensively considered; the number of times the ultrasonic impact head impacts the specimen will increase with the increase of impact frequency and the decrease of impact velocity, and the impact depth will increase with the increase of impact amplitude. Therefore, it is believed that the impact degree is proportional to the impact frequency and impact amplitude, and inversely proportional to the impact velocity. Therefore, the comprehensive variable X is defined to satisfy the following formula:

[0064]

[0065] Among them, x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact speed.

[0066] Draw a graph of the comprehensive variable X and the relevant stress value. According to the above graph, it is preliminarily judged that the two are roughly exponentially related, so the following relationship function is defined:

[0067]

[0068] Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, n, and m are all unknown coefficients.

[0069] Then, the correlation between stress value and impact frequency, impact amplitude and impact velocity was considered, and the variation law of related stress value was analyzed when two parameters were unchanged and the third parameter was changed. According to the data curve, the relationship between impact amplitude, impact velocity and stress value is approximately parabolic, and the relationship between impact frequency and stress value fluctuates up and down but is positively correlated as a whole. Therefore, the prediction function model is:

[0070]

[0071] Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, b, c, d, e, f, g, h, k, n, and m are all unknown coefficients.

[0072] Step 5: Based on the particle swarm optimization algorithm, the undetermined coefficients in the prediction function model are used as the optimization target object, and the fitness function is defined by the prediction error. The particle swarm algorithm parameters are adjusted according to the results of each run, and the process is repeated until the fitness R 2The value reaches 0.99, and the coefficient that makes the model prediction effect the best is finally determined, as follows:

[0073] The prediction function model is trained using the particle swarm optimization algorithm to meet the prediction requirements. In the particle swarm optimization algorithm, particles move in the solution space and update their own speed and position by tracking the individual optimal value and the global optimal value of the group until the fitness no longer changes or the maximum number of iterations is reached. In order to balance the global search capability in the early stage and the convergence speed in the later stage, the particle speed inertia weight is set to a linearly decreasing weight from 0.9 to 0.4.

[0074] The 11 undetermined coefficients a, b, c, d, e, f, g, h, k, n, and m are taken as optimization target objects, and the inverse of the mean square error between the predicted stress value obtained by the prediction function model and the actual stress value of the sample is taken as the fitness function to optimize the undetermined coefficients. The formula is as follows:

[0075]

[0076] Where MSE is the mean square error; H is the number of samples, y actual,i is the actual residual stress value of the i-th sample, y predicted,i is the model prediction value under the i-th sample parameter, and F is the fitness.

[0077] According to the coefficient value and mean square error output of each program run, the optimization algorithm parameters such as search space range, number of evolutions and population size are adjusted, and the process is repeated. The final fitness curve is as follows: Figure 4 The prediction function curve is as follows Figure 5 As shown. At this time, the predicted value is consistent with the actual value to a high degree, and the correlation coefficient R 2 It reaches 0.99, indicating that the established prediction function model has a good prediction effect.

[0078] Step 6: Calculate the optimal ultrasonic impact process parameters based on the optimized prediction function model, as follows:

[0079] The optimized prediction function model can accurately predict the residual stress under different ultrasonic impact parameters. The function is optimized and the impact frequency, impact amplitude, impact velocity and other parameters with the lowest output function value are obtained to obtain the best process parameter combination and obtain good mechanical properties of the welded joint.

Claims

1. A residual stress prediction method considering ultrasonic impact process parameters of welded joints, characterized in that: Here are the steps: Step 1: Perform thermal-mechanical coupling simulation based on the finite element method to obtain the welding residual stress field and proceed to step 2; Step 2: Based on the thermal-mechanical coupling simulation, the welding residual stress field is used as the initial stress to establish the finite element model of ultrasonic impact treatment, and then proceed to step 3; Step 3: Use Latin hypercube sampling method to extract parameters within each ultrasonic impact parameter range to obtain parameter combination samples, set the ultrasonic impact treatment finite element model according to the extracted parameter combination samples, obtain the residual stress data after impact corresponding to different parameter combinations, and proceed to step 4; Step 4: Based on the mapping relationship between different parameter combinations and their corresponding residual stress data after impact, a prediction function model is established, and then the process goes to step 5; Step 5: Based on the particle swarm optimization algorithm, the undetermined coefficients in the prediction function model are used as the optimization target object, and the fitness function is defined by the prediction error. The particle swarm optimization algorithm parameters are adjusted until the fitness R 2 The value reaches 0.99 to obtain the optimized prediction function model, and then go to step 6; Step 6: Calculate the optimal ultrasonic impact process parameters based on the optimized prediction function model.

2. A residual stress prediction method considering ultrasonic impact process parameters of welded joints according to claim 1, characterized in that: In step 1, a thermal-mechanical coupling simulation is performed based on the finite element method to obtain the welding residual stress field, as follows: The uneven temperature field during welding will produce thermal stress, which will cause residual deformation and residual stress. Based on the finite element method, a welding finite element model is established, thermal-mechanical coupling simulation is completed, and the welding residual stress field is obtained.

3. A residual stress prediction method considering ultrasonic impact process parameters of welded joints as claimed in claim 2, characterized in that: In step 2, based on the thermal-mechanical coupling simulation, the welding residual stress field is used as the initial stress to establish the finite element model of ultrasonic impact treatment, as follows: An ultrasonic impact model is established based on the welding model. The welding residual stress field obtained by thermal-mechanical coupling simulation is loaded into the ultrasonic impact model as the initial stress condition. An ultrasonic impact head is set and a displacement load of reciprocating motion perpendicular to the weld surface and a velocity load parallel to the weld are applied to it to simulate the continuous vibration and horizontal movement of the impact needle, and a finite element model of ultrasonic impact treatment is established.

4. A residual stress prediction method considering ultrasonic impact process parameters of welded joints as claimed in claim 3, characterized in that: In step 3, the Latin hypercube sampling method is used to extract parameters within the range of each ultrasonic impact parameter to obtain parameter combination samples. The ultrasonic impact treatment finite element model is set according to the extracted parameter combination samples to obtain the residual stress data after impact corresponding to different parameter combinations, as follows: Within the range of ultrasonic impact treatment parameters, the Latin hypercube sampling method is used to extract parameters to obtain parameter combination samples. The ultrasonic impact simulation is carried out on all parameter combination samples through the established ultrasonic impact treatment finite element model. The stress at the position of the maximum stress point before impact on the ultrasonic impact path is used as an indicator to obtain the post-impact residual stress data corresponding to samples with different parameter combinations.

5. A residual stress prediction method considering ultrasonic impact process parameters of welded joints as claimed in claim 4, characterized in that: Ultrasonic impact treatment parameters include impact frequency, impact amplitude and impact speed.

6. A residual stress prediction method considering ultrasonic impact process parameters of welded joints as claimed in claim 5, characterized in that: In step 4, a prediction function model is established based on the mapping relationship between different parameter combinations and their corresponding post-impact residual stress data, as follows: First, the effects of impact frequency, impact amplitude and impact velocity on the residual stress value after impact are comprehensively considered; the number of times the ultrasonic impact head impacts the specimen will increase with the increase of impact frequency and the decrease of impact velocity, and the impact depth will increase with the increase of impact amplitude. Therefore, it is believed that the impact degree is proportional to the impact frequency and impact amplitude, and inversely proportional to the impact velocity. Therefore, the comprehensive variable X is defined to satisfy the following formula: Among them, x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact speed; Draw a graph of the comprehensive variable X and the relevant stress value. According to the above graph, it is preliminarily judged that the two are roughly exponentially related, so the following relationship function is defined: Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, n, and m are all unknown coefficients; Then, the correlation between stress value and impact frequency, impact amplitude and impact velocity was considered, and the variation law of related stress value was analyzed when two parameters were unchanged and the third parameter was changed. According to the data curve, the relationship between impact amplitude, impact velocity and stress value is approximately parabolic, and the relationship between impact frequency and stress value fluctuates up and down but is positively correlated as a whole. Therefore, the prediction function model is: Among them, y is the residual stress after impact; x1 is the impact frequency; x2 is the impact amplitude; x3 is the impact velocity; a, b, c, d, e, f, g, h, k, n, and m are all unknown coefficients.

7. A residual stress prediction method considering ultrasonic impact process parameters of welded joints according to claim 6, characterized in that: In step 5, based on the particle swarm optimization algorithm, the undetermined coefficients in the prediction function model are used as the optimization target object, and the fitness function is defined by the prediction error. The particle swarm optimization algorithm parameters are adjusted until the fitness R 2 The value reaches 0.99 to obtain the optimized prediction function model, as follows: The particle swarm optimization algorithm is used to train the prediction function model to meet the prediction requirements. In the particle swarm algorithm, particles move in the solution space and update their own speed and position by tracking the individual optimal value and the global optimal value of the group until the fitness no longer changes or the maximum number of iterations is reached. In order to balance the global search ability in the early stage and the convergence speed in the later stage, the particle speed inertia weight is set to a linearly decreasing weight; The undetermined coefficient is taken as the optimization target object, and the prediction error is set as the inverse of the mean square error between the predicted stress value obtained by the prediction function model and the actual stress value of the sample. The prediction error is used as the fitness function to optimize the undetermined coefficient. The formula is as follows: Among them, MSE is the mean square error; H is the number of samples, y actual,i is the actual residual stress value of the i-th sample, y predicted,i is the model prediction value under the i-th sample parameter, and F is the fitness; By adjusting the particle swarm algorithm parameters until the fitting degree R 2 The value reaches 0.99, and the final output is the optimal coefficient that makes the predicted value consistent with the actual value, thereby obtaining the optimized prediction function model.

8. A residual stress prediction method considering ultrasonic impact process parameters of welded joints as claimed in claim 7, characterized in that: In step 6, the optimal ultrasonic impact process parameters are calculated based on the optimized prediction function model, as follows: The optimized prediction function model is optimized with parameter variables as the optimization target to obtain the best process parameter combination that minimizes the residual stress value after impact.

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