A multi-objective collaborative control method for arc additive manufacturing

Abstract

The present invention discloses a multi-objective collaborative control method for arc additive manufacturing, including the following steps: determining the arc additive forming process window, on this basis, carrying out single-factor arc additive process tests and component mechanical property tests, establishing a multivariate nonlinear objective function of process parameters on deposition efficiency, surface quality of deposited layer, and mechanical properties; establishing a finite element thermal-mechanical coupling model to calculate the residual stress and deformation of the component, and obtaining the objective function of residual stress and deformation according to the experimental test and numerical simulation results; using a multi-objective particle swarm method to achieve collaborative control among various indicators, and obtain the optimal process parameter combination. The present invention can simultaneously realize the forming of arc additive components with high efficiency, high surface quality, high mechanical properties, and low residual stress and deformation, thereby realizing the collaborative control of multiple objectives in the arc additive process, which has important reference significance for the collaborative optimization of other additive manufacturing processes.

Description

Technical Field

[0001] The present invention belongs to the field of additive manufacturing, and specifically relates to a multi-objective collaborative control method for arc additive manufacturing. Background Art

[0002] Arc additive manufacturing (AM) uses an electric arc as a heat source to melt metal wire, which is then solidified and deposited along a planned path on a computer-controlled three-dimensional motion platform to form the desired part. This process offers advantages such as low equipment cost, high forming efficiency, energy conservation and environmental protection (the electro-optical conversion rate of lasers is approximately 10%, while that of arcs is over 85%), and ease of automation. Due to its unique advantages, arc AM is becoming an ideal process for the efficient, integrated formation of large metal components.

[0003] Parts formed by arc additive manufacturing should meet requirements in terms of forming efficiency, surface quality, mechanical properties, stress and deformation, etc. However, too many additive process parameters and materials with different properties will lead to significant interactive effects of process variables on component performance, and there is a competitive game relationship between various indicators, making it difficult to achieve coordinated regulation of multiple objectives through a single experimental method. For example, increasing the arc current during deposition can feed more material to improve forming efficiency, but at the same time it will reduce the mechanical properties of the component; increasing the forming speed will help improve the mechanical properties, but too high a forming speed will lead to discontinuities or hump defects on the surface of the deposited layer. Therefore, it is difficult to coordinate the various performance indicators of components in the arc additive process, which seriously restricts the success rate and forming quality of component forming. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-objective coordinated control method for arc additive manufacturing, which can simultaneously achieve the forming of arc additive components with high deposition efficiency, high surface quality, high mechanical properties and low residual stress and deformation, and realize the coordinated control of multiple objectives in the additive process.

[0005] To achieve the above objectives, the present invention provides a multi-objective coordinated control method for arc additive manufacturing, comprising the following steps:

[0006] (1) The main process parameters in the arc additive manufacturing process are selected for single-factor experiments to determine the forming process window containing the main process parameters, and the forming process window is used as a constraint condition for multi-objective optimization;

[0007] (2) Conduct multi-level orthogonal process tests within the forming process window, conduct arc additive deposition tests under different process parameters, measure the deposition efficiency and surface quality of the deposited layer under different process parameters, and obtain the variation law of the process parameters on the deposition efficiency and the surface quality of the deposited layer; conduct mechanical property tests on the deposited samples under different process parameters, measure the tensile strength and elongation under different process parameters, and obtain the variation law of the process parameters on the tensile strength and elongation; finally, establish the deposition efficiency objective function F1, the deposited layer surface quality objective function F2, the tensile strength objective function F3, and the elongation objective function F4 respectively through multivariate nonlinear fitting;

[0008] A finite element thermo-mechanical coupling model of arc additive manufacturing was established to calculate the residual stress and deformation of the arc additive manufacturing sample under different process parameters. The maximum equivalent stress and deformation of the component were obtained. The variation patterns of residual stress and deformation were also obtained. Based on the numerical simulation results, the residual stress objective function F5 and the deformation objective function F6 were established through multivariate nonlinear fitting.

[0009] (3) The multi-objective particle swarm method is used to perform multi-objective optimization on the established objective functions F1, F2, F3, F4, F5, and F6 to achieve multi-objective coordinated control of the arc additive component manufacturing process.

[0010] Furthermore, in step (3), the specific process of the multi-objective particle swarm optimization method is as follows:

[0011] (3-1) Initializing the particle swarm: Determine the dimension of the particle motion space according to the number of process parameters, and determine the particle motion range according to the forming process window;

[0012] (3-2) Calculating the particle fitness based on the objective function, i.e., the deposition efficiency, surface quality of the deposited layer, tensile strength, elongation, residual application, and deformation of the particles under the corresponding process parameter combination;

[0013] (3-3) According to the Pareto dominance relationship, the non-dominated solutions are stored in the external set, and the one with the lowest particle density is selected as the global optimal solution;

[0014] (3-4) The velocity and position iteration formulas of the standard particle swarm method are used to update the velocity and position of the particles, that is, the new additive process parameter combination corresponding to the particles;

[0015] (3-5) Calculate the new fitness of the particle and update the individual extreme value and the global extreme value, that is, update the optimal solution;

[0016] (3-6) Update the external set using the density adaptive grid method;

[0017] (3-7) Determine whether the termination condition is met. The termination condition is the number of iterations or the error precision. If it is met, the operation ends. If it is not met, return to step (3-2) to recalculate the particle fitness and start a new iteration;

[0018] (3-8) After the cycle is completed, the externally concentrated particles are output. The position of the particles in the multidimensional space is the combination of process parameters after multi-objective optimization, that is, the combination of process parameters simultaneously meets the relative optimality of deposition efficiency, surface quality of the deposited layer, tensile strength, elongation, residual stress and deformation.

[0019] Preferably, in step (1), the process parameters are arc power supply current, forming speed, wire feeding speed and pulse frequency.

[0020] Preferably, in step (2), by observing the surface morphology of the deposited layer and calculating the deposition efficiency, the variation pattern of the surface quality and deposition efficiency of the deposited layer under different process parameters is obtained.

[0021] Furthermore, in step (2), the deposition efficiency refers to the deposition volume of the material per unit time, which is obtained by measuring the cross-sectional area of ​​the deposition layer and multiplying it by the forming speed; the surface quality of the deposition layer is judged by observing whether there are discontinuities, humps or melt collapse defects on the surface of the deposition layer.

[0022] Preferably, in step (2), a tensile test is performed using a universal tensile testing machine to test the tensile strength and elongation of the deposited sample.

[0023] Furthermore, in step (2), when establishing the arc additive finite element thermal-mechanical coupling model, the material physical parameters, heat source parameters, boundary conditions, initial conditions and constraint conditions are set according to actual process tests; wherein the material physical parameters include material density, thermal conductivity, specific heat, Poisson's ratio, thermal expansion coefficient, and Young's modulus;

[0024] The steps of establishing the arc additive finite element thermal-mechanical coupling model are as follows:

[0025] The arc heat source adopts a double ellipsoid heat source model as the arc heat input. The double ellipsoid heat source includes a front half ellipsoid heat source and a rear half ellipsoid heat source. The control equation of the front half ellipsoid heat source is:

[0026]

[0027] The control equation of the heat source of the second half of the ellipsoid is:

[0028]

[0029] Among them, q f ,q r are the heat flux densities of the front and back halves of the ellipsoid, respectively; qarc is the heat flux density of the heat source as a whole; f f 、f r are the energy distribution coefficients of the front and back halves of the ellipsoid, respectively, and f f +f r =2;a f 、a r are the semi-axis lengths of the front and back halves of the ellipsoid, respectively, and b l 、c l are the shared width and depth of the front and back halves of the ellipsoid, respectively; x, y, z are the coordinates of the heat source space point;

[0030] The birth-and-death element method is used to simulate the layer-by-layer accumulation of materials during the arc additive process. The initial conditions are: the initial temperature is set to 25°C, and the boundary conditions consider the heat loss caused by convection and radiation. The thermo-elastoplastic model is used to calculate the displacement strain and stress of the node through the temperature increment of each step. During the arc additive process, the three corners of the substrate are fixed to the workbench by special fixtures, and the constraint conditions are set to fix the degrees of freedom of the three corner nodes in the X, Y, and Z directions respectively.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] (1) This method is aimed at arc additive manufacturing process. On the basis of determining the objective function, it uses the intelligent optimization method - multi-objective particle swarm method to achieve multi-objective coordinated control, and realizes the forming of arc additive components with high deposition efficiency, high surface quality, high mechanical properties, and low residual stress and deformation. It solves the problem that multiple performance indicators of components are difficult to take into account due to the influence or even contradiction between various indicators.

[0033] (2) This method uses a combination of process test and numerical simulation to obtain the influence of process parameters on various indicators, which can obtain sufficient data to establish mathematical models for various optimization objectives and improve the accuracy of the regression model. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0035] Figure 2 This is a schematic diagram of the grid division and constraint conditions when calculating stress and deformation using the thermal-mechanical coupling model of the present invention;

[0036] Figure 3 This is a flow chart of the multi-objective collaborative control of arc additive manufacturing achieved by the present invention using a multi-objective particle swarm method. DETAILED DESCRIPTION

[0037] The present invention will be further described below with reference to the accompanying drawings. It should be noted that the data in the examples are all simplified and use non-precise ratios, which are only used to simply and clearly illustrate the implementation method of the present invention.

[0038] In this embodiment, 2319 aluminum alloy welding wire is used as the raw material. Due to its excellent specific strength, toughness, fatigue resistance, corrosion resistance and other properties, it is increasingly widely used in aerospace, rail transportation, deep space exploration, ocean transportation and other fields.

[0039] like Figure 1 As shown, a multi-objective collaborative control method for arc additive manufacturing includes the following steps:

[0040] (1) Three process parameters that are controllable, easily adjustable, and have a significant impact on forming in the arc additive manufacturing process are selected for single-factor multi-level experiments. The arc power supply current (I), forming speed (v), wire feed speed (Q), and power supply pulse frequency (f) are selected to obtain a process window for good forming containing these four process parameters. Among them, the initial range of current I is 160-240A, the initial range of forming speed is 2-6mm / s, and the wire feed speed is 160-200cm / min. The above forming process window, that is, the value range of each process parameter, is used as the constraint condition for multi-objective optimization.

[0041] (2) Multi-level orthogonal process experiments were conducted within the forming process window. Different process parameters were selected for arc additive deposition experiments. The deposition efficiency was calculated and the surface quality of the deposited layer was observed to obtain the variation pattern of process parameters on the deposition efficiency and the surface quality of the deposited layer. The deposition efficiency refers to the deposition volume of the material per unit time, which is obtained by measuring the cross-sectional area of ​​the deposited layer and multiplying it by the forming speed. The surface quality of the deposited layer is judged by observing whether there are discontinuities, humps or melt collapse defects on the surface of the deposited layer. Therefore, the deposition efficiency objective function F1(I,v,Q,f) and the surface quality objective function F2(I,v,Q,f) were established by using experimental measurement data and multivariate nonlinear fitting.

[0042] The mechanical properties of the deposited samples under different process parameters were tested. The tensile strength and elongation of the deposited samples under different process parameters were measured using a universal tensile testing machine. The changing rules of the process parameters on tensile strength and elongation were obtained, and the tensile strength target function F3(I,v,Q,f) and elongation target function F4(I,V,Q,f) were established.

[0043] A finite element thermal-mechanical coupling model of arc additive manufacturing was constructed to calculate the residual stress and deformation during the forming process.

[0044] In this embodiment, the wire and the forming substrate are both made of 2319 aluminum alloy. The material physical properties include density, thermal conductivity, specific heat, Poisson's ratio, thermal expansion coefficient, Young's modulus, etc. At the same time, considering the temperature change, the substrate and deposited layer dimensions in the finite element model are established based on the process test data.

[0045] The arc heat source adopts a double ellipsoid heat source model as the arc heat input. The control equation of the first half of the ellipsoid heat source is:

[0046]

[0047] The heat source control equation of the rear semi-ellipsoid is:

[0048]

[0049] Among them, q f ,q r are the heat flux densities of the front and back halves of the ellipsoid, respectively; q arc is the heat flux density of the heat source as a whole; f f 、f r are the energy distribution coefficients of the front and rear halves of the ellipsoid, respectively, and f f +f r =2;a f 、a r are the semi-axis lengths of the front and back halves of the ellipsoid, respectively, and b l 、c l are the shared width and depth of the front and back halves of the ellipsoid, respectively; x, y, z are the coordinates of the heat source point in space;

[0050] The birth-death element method was used to simulate the layer-by-layer accumulation of materials during the arc additive process. The initial temperature of the system was set to room temperature (25°C), and the boundary conditions considered heat loss caused by convection and radiation. The displacement strain and stress of the node were calculated using the thermoelastic model based on the temperature increment at each step. During the arc additive process, the three corners of the substrate were fixed to the workbench by a special fixture. In order to simulate the fixture fixing effect, Figure 2 As shown in the figure, in the finite element thermal-mechanical coupling model, the constraint conditions are set to fix the degrees of freedom of the three corner nodes in the X, Y, and Z directions respectively.

[0051] The established thermomechanical coupling model was used to calculate the residual stress and deformation within the arc additive manufacturing sample under different process parameters, and experimental verification was performed. Because experimental measurements can only obtain stress and deformation at selected points, and measurement errors are inherent, numerical simulation was used as the primary means to obtain data on the influence of process parameters on residual stress and deformation. This data was used to establish the residual stress objective function F5(I, v, Q, f) and the deformation objective function F6(I, v, Q, f).

[0052] (3) Multi-objective coordinated control of arc additive manufacturing.

[0053] like Figure 3 As shown in the figure, six objective functions are determined based on the arc additive manufacturing process. Among them, deposition efficiency, deposited layer surface quality, tensile strength, and elongation have maximum polarity, while residual stress and deformation have minimum polarity. A multi-objective particle swarm method is used for multi-objective optimization to achieve coordinated control of multiple optimization indicators. The specific steps are as follows:

[0054] (3-1) Initializing the particle swarm: In this embodiment, the number of particles is selected to be 100. The particle motion space is determined to be a four-dimensional space based on the number of the four selected process parameters. The particle motion range is determined based on the range of process parameter values ​​in the process window. That is, the initial range of the arc power supply current is 160-240 A, the initial range of the forming speed is 2-6 mm / s, the wire feeding speed is 160-200 cm / min, and the power supply pulse frequency range is 1-8 Hz.

[0055] (3-2) Calculating the particle fitness based on the objective function, i.e., the particle deposition efficiency, surface quality, tensile strength, elongation, residual application, and deformation under the corresponding process parameter combination;

[0056] (3-3) According to the Pareto dominance relationship, the non-dominated solutions are stored in the external set, and the one with low particle density is selected as the global optimal solution;

[0057] (3-4) The velocity and position iteration formulas of the standard particle swarm method are used to update the velocity and position of the particles, that is, the new additive process parameter combination corresponding to the particles;

[0058] (3-5) After the particle position is updated, the new particle fitness is calculated, and the individual extreme value and the global extreme value are updated;

[0059] (3-6) Update and external set using density adaptive grid method;

[0060] (3-7) Determine whether the termination condition is met (the number of iterations is set to 500 in this embodiment). If it is met, the operation ends; if it is not met, the particle fitness is recalculated and a new iteration begins;

[0061] (3-8) After the cycle is completed, the external concentrated particles are output. The coordinates of the particles in three-dimensional space are the four process parameter combinations after multi-objective optimization, namely, power current 180A, forming speed 4mm / s, wire feeding speed 180cm / min, and pulse frequency 5Hz. That is, the combination of process parameters obtained satisfies the requirements of maximizing forming efficiency, surface quality, tensile strength, and elongation while minimizing residual stress and deformation of the formed component.

Claims

1. A multi-objective collaborative control method for arc additive manufacturing, characterized in that: The following steps are involved: (1) The main process parameters in the arc additive manufacturing process are selected for single factor experiments to determine the forming process window containing the main process parameters, and the forming process window is used as a constraint condition for multi-objective optimization; (2) Conduct multi-level orthogonal process tests within the forming process window, conduct arc additive deposition tests under different process parameters, measure the deposition efficiency and surface quality of the deposited layer under different process parameters, and obtain the change law of process parameters on deposition efficiency and surface quality of the deposited layer; The mechanical properties of the deposited samples under different process parameters were tested, and the tensile strength and elongation under different process parameters were measured respectively, and the variation law of the process parameters on the tensile strength and elongation was obtained; finally, the deposition efficiency objective function F1, the deposition layer surface quality objective function F2, the tensile strength objective function F3 and the elongation objective function F4 were established respectively through multivariate nonlinear fitting; The arc additive finite element thermomechanical coupling model is established, and the residual stress and deformation of the arc additive sample under different process parameters are calculated respectively, and the maximum equivalent stress and deformation of the component are obtained. At the same time, the variation law of residual stress and deformation is obtained. Based on the numerical simulation results, the residual stress objective function F5 and the deformation objective function F6 are established respectively through multivariate nonlinear fitting. (3) The multi-objective particle swarm method is used to perform multi-objective optimization on the established objective functions F1, F2, F3, F4, F5, and F6 to achieve multi-objective coordinated control of the arc additive component manufacturing process.

2. The arc additive manufacturing multi-objective collaborative control method according to claim 1, characterized in that: In step (3), the specific process of the multi-objective particle swarm method is: (3-1) Initializing the particle swarm: determining the dimension of the particle motion space according to the number of process parameters, and determining the particle motion range according to the forming process window; (3-2) Calculating the particle fitness according to the objective function, i.e., the deposition efficiency, surface quality of the deposited layer, tensile strength, elongation, residual application and deformation of the particle under the corresponding process parameter combination; (3-3) According to the Pareto dominance relationship, the non-dominated solutions are stored in the external set, and the one with the lowest particle density is selected as the global optimal solution; (3-4) using the speed and position iteration formula of the standard particle swarm method to update the speed and position of the particles, that is, the new additive process parameter combination corresponding to the particles; (3-5) Calculate the new fitness of the particle and update the individual extreme value and the global extreme value, that is, update the optimal solution; (3-6) Update the external set using the density adaptive grid method; (3-7) Determine whether the termination condition is met, the termination condition is the number of iterations or the error precision, if it is met, the operation is terminated, if not, return to step (3-2) to recalculate the particle fitness and start a new iteration; (3-8) After the cycle is completed, the externally concentrated particles are output. The position of the particles in the multidimensional space is the combination of process parameters after multi-objective optimization. That is, the combination of process parameters obtained simultaneously satisfies the relative optimality of deposition efficiency, surface quality of the deposited layer, tensile strength, elongation, residual stress, and deformation.

3. The arc additive manufacturing multi-objective collaborative control method according to claim 1 or 2, characterized in that: In step (1), the process parameters are arc power supply current, forming speed, wire feeding speed and pulse frequency.

4. The arc additive manufacturing multi-objective collaborative control method according to claim 3, characterized in that: In step (2), by observing the surface morphology of the deposited layer and calculating the deposition efficiency, the variation patterns of the surface quality and deposition efficiency of the deposited layer under different process parameters are obtained.

5. The arc additive manufacturing multi-objective collaborative control method according to claim 4, characterized in that: In step (2), the deposition efficiency refers to the deposition volume of the material per unit time, which is obtained by measuring the cross-sectional area of ​​the deposition layer and multiplying it by the forming speed; the surface quality of the deposition layer is judged by observing whether there are discontinuities, humps or melt collapse defects on the surface of the deposition layer.

6. The arc additive manufacturing multi-objective collaborative control method according to claim 1 or 2, characterized in that: In step (2), a tensile test is performed using a universal tensile testing machine to test the tensile strength and elongation of the deposited sample.

7. The arc additive manufacturing multi-objective collaborative control method according to claim 1 or 2, characterized in that: In step (2), when establishing the arc additive finite element thermal-mechanical coupling model, the material physical parameters, heat source parameters, boundary conditions, initial conditions and constraint conditions are set according to actual process tests; wherein the material physical parameters include material density, thermal conductivity, specific heat, Poisson's ratio, thermal expansion coefficient and Young's modulus; The steps of establishing the arc additive finite element thermal-mechanical coupling model are as follows: The arc heat source adopts a double ellipsoid heat source model as the arc heat input. The double ellipsoid heat source includes a front half ellipsoid heat source and a rear half ellipsoid heat source. The control equation of the front half ellipsoid heat source is: The control equation of the ellipsoid heat source in the second half is: Among them, q f ,q r are the heat flux densities of the front and rear halves of the ellipsoid, respectively; q arc is the heat flux density of the heat source as a whole; f f 、f r are the energy distribution coefficients of the front and rear halves of the ellipsoid, respectively, and f f +f r =2; a f 、a r are the semi-axis lengths of the front and rear halves of the ellipsoid, respectively, and b l 、c l are the shared width and depth of the front and rear halves of the ellipsoid, respectively; x, y, z are the coordinates of the heat source space point; The birth-and-death unit method is used to simulate the layer-by-layer accumulation of materials in the arc additive process. The initial conditions are: the initial temperature is set to 25°C, and the boundary conditions consider the heat loss caused by convection and radiation. The thermo-elastoplastic model is used to calculate the displacement strain and stress of the node through the temperature increment of each step. In the arc additive process, the three corners of the substrate are fixed on the workbench by special fixtures, and the constraint conditions are set to fix the degrees of freedom of the three corner nodes in the XYZ directions respectively.

Images