An aluminum alloy electric arc additive manufacturing system and method for real-time regulation of porosity defects

Abstract

The application discloses an aluminum alloy electric arc additive manufacturing system and method for real-time regulation of porosity defects, and comprises: an electric arc additive manufacturing subsystem for aluminum alloy electric arc additive manufacturing according to process parameters set by a process parameter adjustment subsystem for a current printing layer; an image acquisition and processing subsystem for shooting images of an electric arc additive manufacturing molten pool deposition forming process and obtaining a molten pool length after processing; a fuzzy PID control subsystem for fuzzy PID control according to an error and a change rate of the error when the error of the molten pool length and a preset value exceeds a preset range to obtain a current molten pool optimal length; and a process parameter adjustment subsystem for extracting optimal process parameters corresponding to the current molten pool optimal length from a preset relationship table and regulating process parameters of a next printing layer to the optimal process parameters, thereby reducing porosity defects. The application can be real-time regulated during deposition to reduce porosity defects.

Description

TECHNICAL FIELD

[0001] The present application relates to additive manufacturing technology, and in particular to an aluminum alloy electric arc additive manufacturing system and method for real-time regulation of porosity defects. BACKGROUND

[0002] Aluminum alloy is widely used in the field of electric arc additive manufacturing due to its small density, good corrosion resistance, high specific strength, and good thermal conductivity. However, this metal material is highly sensitive to oxidation. Before production and manufacturing, if the substrate and welding wire are not polished, there will be certain contaminants on the surface, such as moisture in the air, hydrogen, oil and other hydrocarbons left on the substrate and welding wire, which can easily enter the molten pool during additive manufacturing and cause porosity due to the failure to escape from the inside of the metal part in time, thereby affecting the mechanical properties and fatigue life of the metal part. In severe cases, it can reduce the density and corrosion resistance of the component, causing excessive stress concentration, resulting in the generation of cracks and anisotropy and other defects. In addition, from the perspective of the processing process, the formation of internal porosity defects in aluminum alloy is also easily affected by factors such as temperature, scanning speed, wire feeding speed, pulse frequency, dry elongation, and protective gas capacity. Therefore, for aluminum alloy materials commonly used in electric arc additive manufacturing, certain technical means are needed to improve the internal porosity defects.

[0003] The current methods for improving the internal porosity defects of aluminum alloy mainly include: (1) heat treatment, i.e., through appropriate thermal deformation and heat treatment to compensate for the solidification defects in the electric arc additive manufacturing process, especially for materials with high carbon content, which must be heat treated to effectively reduce residual stress and deformation and further reduce porosity; (2) interlayer cold rolling and cooling, which can effectively reduce residual stress, reduce the degree of surface oxidation of the metal part, refine the structure, and improve the hardness and strength, thereby making the material properties more uniform. And through the thermal plastic deformation of the deposited layer, the anisotropy of the metal part can be significantly reduced, thereby improving the ultimate tensile strength and yield strength in the forming direction, further controlling the solidification rate and temperature gradient to avoid the formation of porosity; (3) raw material control, i.e., ensuring that the substrate surface is clean and tidy, the welding wire is free of contaminants, and the protective gas capacity is sufficient before additive manufacturing, thereby effectively preventing oxidation of the metal part and reducing porosity to some extent.

[0004] Although there have been some studies on methods for improving aluminum alloy porosity defects at home and abroad, most of the technical means are pre-deposition treatment or post-deposition treatment, and few involve real-time regulation of porosity defects during the deposition process. SUMMARY

[0005] Invention purposes: The present application aims at the problems existing in the prior art, and provides an aluminum alloy electric arc additive manufacturing system and method capable of real-time regulation of porosity defects in a deposition process.

[0006] Technical solutions: The present application provides an aluminum alloy electric arc additive manufacturing system for real-time regulation of porosity defects, comprising:

[0007] An electric arc additive manufacturing subsystem is configured to perform aluminum alloy electric arc additive manufacturing according to process parameters set by a process parameter adjustment subsystem for a current printing layer;

[0008] An image acquisition and processing subsystem is configured to capture images of an electric arc additive manufacturing molten pool deposition forming process and obtain a molten pool length after processing;

[0009] A fuzzy PID control subsystem is configured to perform fuzzy PID control according to an error and a change rate of the error when the error of the molten pool length and a preset value exceeds a preset range, and obtain a current molten pool optimal length;

[0010] A process parameter adjustment subsystem is configured to extract optimal process parameters corresponding to the current molten pool optimal length from a preset relationship table, and regulate process parameters of a next printing layer to be the optimal process parameters, so as to reduce porosity defects, wherein the preset relationship table is a corresponding relationship table of process parameters and molten pool lengths obtained through experiments.

[0011] Further, the electric arc additive manufacturing subsystem comprises a welding gun, a welding machine for controlling the welding gun, a three-dimensional printing platform located below the welding gun, an aluminum alloy substrate placed on the three-dimensional printing platform, and a wire feeder for feeding aluminum alloy welding wires to below the welding gun.

[0012] Further, the image acquisition and processing subsystem comprises an industrial camera and an image processing module connected to the industrial camera, and the image processing module is configured to process images of the electric arc additive manufacturing molten pool deposition forming process to obtain the molten pool length.

[0013] Further, the fuzzy PID control subsystem comprises a fuzzy controller and a PID controller, the fuzzy controller is configured to perform fuzzy control on the error and the change rate of the error according to a preset fuzzy rule table, so as to obtain PID parameters, and the PID controller is configured to adjust the error according to the PID parameters to obtain the current molten pool optimal length.

[0014] Further, the electric arc additive manufacturing subsystem further comprises a working chamber, a protective gas delivery device connected to the working chamber through a pipeline, fixing bolts arranged on a three-dimensional printing platform for fixing the aluminum alloy substrate, a control panel arranged on the outer wall of the working chamber for controlling the scanning parameters, and a limiting alarm lamp arranged on the top of the working chamber for warning the position of the welding gun, wherein the welding gun, the three-dimensional printing platform and the aluminum alloy substrate are located in the working chamber.

[0015] Further, the image processing module comprises:

[0016] an image correction unit for correcting the image of the electric arc additive manufacturing molten pool deposition forming process through a BP neural network;

[0017] an image conversion unit for converting the corrected image into a gray scale image through a Gray operator;

[0018] a target extraction unit for extracting a molten pool partial image from the gray scale image through an ROI target;

[0019] a noise filtering unit for processing the molten pool partial image through Gaussian filtering and image binarization, so as to remove the halo interference of the electric arc and suppress the noise of the image;

[0020] a molten pool contour extraction unit for extracting the molten pool contour from the output image of the noise filtering unit through a convolution array of a Canny operator;

[0021] a minimum circumscribed rectangle extraction unit for obtaining the minimum circumscribed rectangle of the molten pool contour through a Minrect operator;

[0022] a molten pool length extraction unit for obtaining the height of the minimum circumscribed rectangle as the length of the molten pool through a Make.Size function.

[0023] Further, the membership functions of the input variables and the output variables of the fuzzy PID control subsystem are a double Gaussian distribution type function and a triangular function respectively, the preset fuzzy rule table is a fuzzy rule composed of a simple conditional statement, the fuzzy controller performs fuzzy reasoning according to the Mamdani algorithm and the fuzzy rule, and the fuzzy reasoning result is de-fuzzied through the area barycenter method to obtain the PID parameters.

[0024] Further, the process parameters include any one or several of the welding current, the welding voltage, the wire feeding speed, the scanning speed and the interlayer cooling time.

[0025] Further, the initial values corresponding to the welding current, the welding voltage, the wire feeding speed, the scanning speed and the interlayer cooling time are 110 A, 12 V, 5.5 m / min, 480 mm / min and 15 s respectively.

[0026] The application also provides an aluminum alloy electric arc additive manufacturing method for real-time regulation of porosity defects, comprising:

[0027] Step 1: performing aluminum alloy electric arc additive manufacturing according to the process parameters set for the current printing layer;

[0028] Step 2: taking an image of the electric arc additive manufacturing molten pool deposition forming process, and obtaining a molten pool length after processing;

[0029] Step 3: when the error of the molten pool length and the preset value exceeds the preset range, performing fuzzy PID control according to the error and the change rate of the error to obtain the optimal length of the current molten pool;

[0030] Step 4: extracting the optimal process parameters corresponding to the optimal length of the current molten pool from a preset relationship table, and regulating the process parameters of the next printing layer to be the optimal process parameters, so as to reduce the porosity defects, wherein the preset relationship table is a corresponding relationship table of process parameters and molten pool length obtained through experiments.

[0031] The application has the following beneficial effects: compared with other systems and methods for reducing aluminum alloy porosity defects through pre-treatment or post-treatment, the application can realize real-time improvement of porosity defects, mainly through image processing technology to realize real-time monitoring of the molten pool forming process, and through fuzzy PID control to adjust the length of the molten pool in real time during the molten pool forming process. The change of the length of the molten pool can indirectly reflect the change of the heat input, and the change of the heat input will further affect the porosity defects (change of the number of pores and change of the size of pores) inside the metal part. Therefore, the application controls the heat input of the molten pool when adjusting the length of the molten pool, thereby effectively improving the common porosity defect problem of aluminum alloy, and finally producing an aluminum alloy metal part with good mechanical properties and microstructure, which has the advantages of good stability, high forming precision and the like. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The structure diagram of the aluminum alloy electric arc additive manufacturing system for real-time regulation of porosity defects provided by the application is shown in the figure;

[0033] Figure 2 The structure diagram of the electric arc additive manufacturing subsystem in the application is shown in the figure;

[0034] Figure 3 The structure diagram of the fuzzy PID control subsystem in the application is shown in the figure;

[0035] Figure 4 The membership function diagram of the input variables and output variables of the fuzzy PID control subsystem in the application is shown in the figure;

[0036] Figure 5 The 49 fuzzy rule schematic diagram of the fuzzy PID control subsystem in the application is shown in the figure;

[0037] Figure 6 This is a schematic diagram of the visual rule base of the fuzzy PID control subsystem in this invention;

[0038] Figure 7 A flowchart of the aluminum alloy arc additive manufacturing method for real-time control of porosity defects provided by the present invention;

[0039] Figure 8 This is a schematic diagram of the substrate preheating device in this invention;

[0040] Figure 9 A schematic diagram showing the comparison of porosity defects before and after applying the present invention;

[0041] Among them, 1-welding torch, 2-welding machine, 3-3D printing platform, 4-aluminum alloy substrate, 5-wire feeder, 6-protective gas device, 7-industrial camera, 8-image processing module, 9-fixing bolt, 10-control panel, 11-limit alarm light, 12-working chamber. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] This embodiment provides an aluminum alloy arc additive manufacturing system for real-time control of porosity defects, such as... Figure 1 As shown, the system includes an arc additive manufacturing subsystem, an image acquisition and processing subsystem, a fuzzy PID control subsystem, and a process parameter adjustment subsystem. The arc additive manufacturing subsystem is used to perform aluminum alloy arc additive manufacturing according to the process parameter adjustment subsystem's settings for the current printing layer's process parameters. The image acquisition and processing subsystem is used to capture images of the arc additive manufacturing molten pool deposition process and process them to obtain the molten pool length. The fuzzy PID control subsystem is used to perform fuzzy PID control based on the error and its rate of change when the error between the molten pool length and the preset value exceeds a preset range, obtaining the current optimal molten pool length. The process parameter adjustment subsystem is used to extract the optimal process parameters corresponding to the current optimal molten pool length from a preset relationship table and adjust the process parameters of the next printing layer to these optimal process parameters, thereby reducing porosity defects. The preset relationship table is a table of correspondence between process parameters and molten pool lengths obtained through experiments. The process parameters include welding current, welding voltage, wire feed speed, scanning speed, and interlayer cooling time.

[0044] like Figure 2As shown, the electric arc additive manufacturing subsystem includes a welding gun 1, a welding machine 2 for controlling the welding gun 1, a three-dimensional printing platform 3 located below the welding gun 1, an aluminum alloy substrate 4 placed on the three-dimensional printing platform 3, a wire feeder 5 for feeding aluminum alloy wire to below the welding gun 1, a working chamber 12, a shielding gas delivery device 6 connected to the working chamber 12 through a pipeline, fixing bolts 9 arranged on the three-dimensional printing platform 3 for fixing the aluminum alloy substrate 4, a control panel 10 arranged on the outer wall of the working chamber 12 for controlling scanning parameters, a limit alarm lamp 11 arranged on the top of the working chamber 12 for warning the position of the welding gun, and the welding gun 1, the three-dimensional printing platform 3 and the aluminum alloy substrate 4 are located in the working chamber 12. The welding machine 2 can realize the setting of welding current and welding voltage, which is controlled by the process parameter adjustment subsystem, the control panel 10 can set the scanning parameters such as scanning speed and interlayer cooling time, which is controlled by the process parameter adjustment subsystem, and the wire feeder 5 can realize the setting of wire feeding speed, which is controlled by the process parameter adjustment subsystem. The shielding gas device 6 is opened before welding, which can prevent the aluminum alloy from being excessively oxidized by air during the additive manufacturing process to change the geometric characteristics and corrosion resistance of the metal part, wherein the shielding gas is pure argon with a concentration of 100%. The limit alarm lamp 11 is installed above the working chamber for detecting the position of the welding gun. When the position of the welding gun is too high or too low, the limit alarm lamp will give a red warning signal and ring an alarm; when the position of the welding gun is about to exceed the appropriate range, the limit alarm lamp gives a yellow prompt signal; when the position of the welding gun is within the appropriate range, the limit alarm lamp gives a green safety signal.

[0045] As Figure 2As shown, the image acquisition and processing subsystem includes an industrial camera 7 and an image processing module 8 connected with the industrial camera 7. The industrial camera 7 is installed above the welding torch 1 to take images of the electric arc additive manufacturing molten pool deposition forming process from above, and the industrial camera is also provided with a filter for eliminating interference caused by strong arc light in the electric arc additive manufacturing process. The image processing module 8 is used to process the images of the industrial camera 7 to obtain the molten pool length. The image processing module 8 specifically includes: an image correction unit for correcting the images of the electric arc additive manufacturing molten pool deposition forming process through a BP neural network; an image conversion unit for converting the corrected images into gray scale images through a Gray operator; a target extraction unit for extracting the molten pool partial image from the gray scale images through ROI target extraction; a noise filtering unit for processing the molten pool partial image through Gaussian filtering and image binarization, so as to remove the halo interference of the electric arc and suppress the noise of the image; a molten pool contour extraction unit for extracting the molten pool contour from the image output by the noise filtering unit through a convolution array of a Canny operator; a minimum circumscribed rectangle extraction unit for obtaining the minimum circumscribed rectangle of the molten pool contour through a Minrect operator; and a molten pool length extraction unit for obtaining the height of the minimum circumscribed rectangle as the molten pool length through a Make.Size function. In specific implementation, the image processing module 8 can be implemented as one or more computers.

[0046] As shown in Figure 3 , the fuzzy PID control subsystem includes a fuzzy controller and a PID controller. The fuzzy controller is used to perform fuzzy control on the error and the change rate of the error according to a preset fuzzy rule table, so as to obtain PID parameters. The PID controller is used to adjust the error according to the PID parameters, so as to obtain the current optimal molten pool length. Wherein, the error E between the molten pool length L and the preset value L' is E = L - L', and the change rate of the error is Through a large number of comparative experiments on the molten pool length threshold setting in the early stage, the optimal molten pool length is obtained as 10 mm, so the preset value L' can be set as 10 mm. On this basis, the error range of the length is allowed to be ±0.5 mm, so the fuzzy PID controller designed by the system allows the molten pool length threshold range to be (10 ± 0.5) mm. If the molten pool length exceeds or is lower than the above threshold, the fuzzy PID controller will regulate the molten pool length. In the process of fuzzy control, the controlled object of the system is determined as the molten pool length, the error and the change rate of the error are determined as the input variables of the fuzzy controller, and the output variable is the PID parameter ΔK p , ΔK i , ΔK d . The input variables and the output variable are taken as fuzzy variables together. The membership function of the input variable of the fuzzy controller designed by the system is a double-Gaussian distribution type function, and the membership function of the output variable is a triangular function. The membership function graph is shown in Figure 4.

[0047] The fuzzy controller determines the language subset describing the fuzzy variables as {positive large, positive medium, positive small, zero, negative small, negative medium, negative large}, corresponding to {PB, PM, PS, ZO, NS, NM, NB} respectively. Based on expert experience and the error E and its rate of change EC, 49 fuzzy rules composed of simple conditional statements are designed. These fuzzy rules can realize the conversion of parameter values ​​from the physical domain to the fuzzy domain. According to the fuzzy rule table, the fuzzy domains of the error of the molten pool length and the rate of change of the error are set to [-6, 6] respectively, and the fuzzy domains of the gain change are: ΔK p For [-3, 3], ΔK i For [-0.8, 0.8], ΔK d The range is [-3, 3]. The fuzzy rules for the output variable U are shown in Table 1 below, ΔK p The fuzzy rule table is shown in Table 2 below, ΔK i The fuzzy rule table is shown in Table 3 below, ΔK d The fuzzy rule table is shown in Table 4 below.

[0048] Table 1. Fuzzy rule table for output variable U

[0049]

[0050] Table 2 ΔK p Fuzzy rule table

[0051]

[0052] Table 3 ΔK i Fuzzy rule table

[0053]

[0054] Table 4 ΔK d Fuzzy rule table

[0055]

[0056] In the aforementioned fuzzy rule table, the fuzzy statements are related by OR, and u1 can be calculated from the fuzzy rule determined by the first statement. Similarly, the control quantities u2, ..., u can be calculated from the remaining conditional statements. 49 Then the fuzzy set output by the system is U = u1 + u2 + ... + u 49Therefore, the corresponding fuzzy control table for this system can be obtained. Furthermore, fuzzy rules are stored in the rule base as simple conditional statements. The fuzzy controller first transforms the physical domains of the input variables, namely error E and the rate of change of error EC, into a fuzzy domain. During the fuzzification process, converting precise input quantities into fuzzy quantities requires multiplying by a correlation coefficient, which is the quantization factor. If the physical domain of a variable is [-x, x] and the fuzzy domain is [-n, n], then the quantization factor... Then, fuzzy inference is performed based on the rule base and the Mamdani algorithm to obtain the final fuzzy set of control variables. The fuzzy rule tuning table for this system is detailed below. Figure 5 For a detailed diagram of the system's visual rule base, please see [link / reference]. Figure 6 Finally, defuzzification is performed. For fuzzy sets, the area centroid method is used, where the centroid of the area enclosed by the membership function curve and the horizontal axis is taken as the final output value of the fuzzy inference. Where u0 is the x-coordinate of the centroid of the area, μ u (u) is the membership function corresponding to the fuzzy set on the universe of discourse.

[0057] The output of the fuzzy controller is the gain K of the PID controller. p K i K d The change in ΔK p ΔK i ΔK d The three gain changes and the error rate of change mentioned above serve as inputs to the PID controller, acting within it. The parameters obtained by the PID controller after parameter self-tuning are as follows: K in the expression p0 K i0 K d0 K represents the gain coefficient of the PID controller in the previous sampling period. kp K ki K kd ΔK is the quantization factor of the fuzzy controller. p ΔK i ΔK d K represents the gain change of the PID controller. p K i K d This is the gain coefficient of the PID controller in this sampling period.

[0058] The process parameter adjusting subsystem obtains the relationship between the molten pool length and the process parameters according to the preset relationship table. The coupling relationship between the molten pool length and the process parameters is established through experiments and system identification methods. For example, the molten pool length printed by the wire feeding speed of 5.5 m / min is about 10 mm. When the wire feeding speed becomes 6.0 m / min or other values, the molten pool length will also change accordingly. Similarly, the coupling relationship between the molten pool length and other process parameters can also be established through system identification methods. Therefore, when the process parameters change, the molten pool length will also change accordingly. The process parameter adjusting subsystem sets the parameters according to the obtained optimal process parameters.

[0059] In specific implementation, the image processing module 8, the fuzzy PID control subsystem and the process parameter adjusting subsystem can be respectively instantiated as a computer, or can be integrated on a computer.

[0060] In specific implementation, in order to achieve better manufacturing effect and reduce defects, the model of the aluminum alloy wire material and the substrate can be ER5356. The substrate preheating temperature is set to 135℃, which can effectively reduce the temperature gradient and thermal stress in the metal wire deposition process, thereby inhibiting the generation of porosity defects. The protective gas flow is set to 15 L / min. Below 15 L / min, the metal product cannot be fully protected from oxidation interference by the external environment; above 15 L / min, it is easy to cause waste of protective gas and increase the loss of experimental materials. The initial welding voltage and current of the welding machine are 12 V and 110 A, respectively. The initial wire feeding speed of the wire feeder is 5.5 m / min, which will affect the number and pore size of the porosity to some extent; when the wire feeding speed is too low, the small bubbles in the metal product cannot escape in time, and there are more gas holes remaining in the product; when the wire feeding speed gradually increases, the heat input increases, the molten pool size increases, and the bubbles have sufficient escape time, so there are fewer remaining gas holes, but the surface height of the molten pool on the side of the metal product fluctuates seriously, which needs to be processed accordingly. The initial scanning speed of the welding torch is 480 mm / min; this parameter has a certain influence on the liquid residence time and solidification rate of the molten pool, especially on the morphology of the porosity and the escape time of the gas inside the product. The faster the scanning speed, the shorter the residence time of the liquid metal, and the smaller the size of the bubbles formed inside the product. Since the bubble size is small, it cannot escape from the product in time, so there are more small pores in the metal product. The deposition mode of the welding torch is staggered deposition, which can effectively avoid excessive heat accumulation at the arc starting and extinguishing positions. In order to avoid excessive heat input of the molten pool, a 15s interlayer cooling time is set after each layer of molten pool deposition, so as to reduce the size of the residual stress and avoid the collapse of the workpiece.

[0061] The embodiment also provides an aluminum alloy electric arc additive manufacturing method for real-time regulation of porosity defects, as shown in the formula (1), which comprises the following steps: Figure 7

[0062] Step 1: Perform aluminum alloy electric arc additive manufacturing according to the process parameters set for the current printing layer.

[0063] In this embodiment, ER5356 aluminum alloy with a wire diameter of 1.0 mm is selected as the welding wire of the system, and ER5356 aluminum alloy with a size of 250 mm x 250 mm is selected as the substrate of the system. Before welding begins, the substrate surface needs to be cleaned with anhydrous ethanol to remove oil stains, moisture, hydrogen and other hydrocarbons remaining on the welding wire, so that the above components do not enter the molten pool during the additive manufacturing process and cause porosity due to solidification. At the same time, the substrate needs to be polished to remove the surface oxide layer. When the porosity is too large, it can easily reduce the fatigue life and static mechanical properties of the part, ultimately affecting the forming effect of the part. After cleaning the surface of the substrate, the substrate needs to be preheated, and the substrate preheating device is as shown in Figure 8 The preheating temperature of the substrate preheating device is set to 135°C, which can effectively reduce the temperature gradient and thermal stress during the metal wire deposition process, thereby inhibiting the generation of porosity defects. After preheating the substrate, the substrate is fixed on the three-dimensional printing platform with bolts to ensure that its position remains constant during welding; after fixing the position of the substrate, the position of the industrial camera is calibrated according to the principle of convex lens imaging, so as to obtain the intrinsic matrix of the camera. Through coordinate transformation between the picture coordinate system and the world coordinate system, the specific position of the camera is determined. If the tangential distortion or radial distortion of the convex lens of the camera is caused by process or some uncontrollable reasons, the image captured by the camera needs to be corrected through coordinate transformation. After calibrating the position of the industrial camera, the ER5356 aluminum alloy welding wire is sent to the lower end of the welding gun through the wire feeder, and the wire feeding speed and scanning speed are set to 5.5 m / min and 480 mm / min, respectively. At the same time, open the protective gas device filled with 100% pure argon. The image processing module sets the length threshold of each layer of the molten pool, i.e. the length is stable at (10±0.5) mm; turn on the welding machine, set the welding voltage and welding current to 12V and 110A respectively, and set the protective gas flow to 15L / min to fully protect the aluminum alloy metal part from being oxidized and disturbed by the external environment.

[0064] Step 2: Capture images of the molten pool deposition forming process of the electric arc additive manufacturing, and obtain the length of the molten pool after processing.

[0065] ​The processing method includes: correcting the image of the molten pool deposition process in arc additive manufacturing using a BP neural network; converting the corrected image into a grayscale image using the Gray operator; extracting the molten pool portion of the grayscale image using the ROI target; processing the molten pool portion of the image using Gaussian filtering and image binarization to remove arc halo interference and suppress image noise; extracting the molten pool contour from the noise filtering unit output image using a convolution array of the Canny operator; obtaining the minimum bounding rectangle of the molten pool contour using the Minrect operator; and obtaining the height of the minimum bounding rectangle using the Make.Size function, which is used as the molten pool length.

[0066] Step 3: When the error between the length of the molten pool and the preset value exceeds the preset range, fuzzy PID control is performed based on the error and the rate of change of the error to obtain the current optimal length of the molten pool.

[0067] Step 4: Extract the optimal process parameters corresponding to the current optimal length of the molten pool from the preset relationship table, and adjust the process parameters of the next printing layer to the optimal process parameters to reduce porosity defects. The preset relationship table is a table of correspondence between process parameters and molten pool length obtained through experiments.

[0068] The method in this embodiment corresponds to the system in Embodiment 1 and has the functions and effects of the method in Embodiment 1, which will not be described again.

[0069] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0070] To verify the improved benefits of this invention, open-loop control experiments and closed-loop control experiments were conducted. The initial process parameters involved in both experiments were the same, while the closed-loop control experiment applied a fuzzy PID control system. For a detailed comparison of porosity defects before and after applying fuzzy PID control, please refer to the schematic diagram. Figure 9 , Figure 9 (a) A diagram of porosity defects in the third layer of the molten pool without the application of fuzzy PID control. Figure 9 (b) in the diagram shows the porosity defects in the third layer of the molten pool after applying fuzzy PID control. It can be seen that the present invention can significantly reduce porosity defects.

Claims

1. An aluminum alloy electric arc additive manufacturing system for real-time regulation of porosity defects, comprising: The method comprises the following steps: An electric arc additive manufacturing subsystem is used to perform aluminum alloy electric arc additive manufacturing according to process parameter adjustment of a current printing layer; An image acquisition and processing subsystem is used to take images of the electric arc additive manufacturing molten pool deposition forming process and process the images to obtain the molten pool length; A fuzzy PID control subsystem is used to perform fuzzy PID control according to the error and the change rate of the error when the error of the molten pool length and the preset value exceeds the preset range, to obtain the current optimal molten pool length; A process parameter adjustment subsystem is used to extract the optimal process parameter corresponding to the current optimal molten pool length from a preset relationship table, and to adjust the process parameter of the next printing layer to the optimal process parameter, so as to reduce the porosity defect, wherein the preset relationship table is a correspondence table of process parameters and molten pool lengths obtained through experiments; The fuzzy PID control subsystem comprises a fuzzy controller and a PID controller, the fuzzy controller is used to perform fuzzy control on the error and the change rate of the error according to a preset fuzzy rule table, to obtain PID parameters, and the PID controller is used to adjust the error according to the PID parameters, to obtain the current optimal molten pool length; The membership functions of the input variables and the output variables of the fuzzy PID control subsystem are double Gaussian distribution functions and triangular functions respectively, the preset fuzzy rule table is a fuzzy rule composed of simple conditional sentences, the fuzzy controller performs fuzzy reasoning according to the Mamdani algorithm and the fuzzy rule, and the fuzzy reasoning result is de-fuzzied through the area barycenter method to obtain the PID parameters.

2. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 1, wherein: The electric arc additive manufacturing subsystem comprises a welding gun (1), a welding machine (2) for controlling the welding gun (1), a three-dimensional printing platform (3) located below the welding gun (1), an aluminum alloy substrate (4) placed on the three-dimensional printing platform (3), and a wire feeder (5) for feeding aluminum alloy welding wire to the position below the welding gun (1).

3. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 1, wherein: The image acquisition and processing subsystem comprises an industrial camera (7) and an image processing module (8) connected with the industrial camera (7), and the image processing module (8) is used to process the images of the electric arc additive manufacturing molten pool deposition forming process taken by the industrial camera (7) to obtain the molten pool length.

4. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 2, characterized in that: The electric arc additive manufacturing subsystem further comprises a working chamber (12), a protective gas delivery device (6) connected with the working chamber (12) through a pipeline, a fixing bolt (9) arranged on the three-dimensional printing platform (3) for fixing the aluminum alloy substrate (4), a control panel (10) arranged on the outer wall of the working chamber (12) for controlling the scanning parameters, a limit alarm lamp (11) arranged on the top of the working chamber (12) for warning the position of the welding gun, and the welding gun (1), the three-dimensional printing platform (3), and the aluminum alloy substrate (4) are located in the working chamber (12).

5. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 3, wherein: The image processing module (8) comprises: An image correction unit is used to correct the images of the electric arc additive manufacturing molten pool deposition forming process through a BP neural network; An image conversion unit is used to convert the corrected images into gray scale images through a Gray operator; The target extraction unit is configured to extract a molten pool part image in the gray image through a ROI target. The noise filtering unit is configured to process the molten pool part image by using Gaussian filtering and image binarization, so as to remove halo interference of the electric arc and suppress noise of the image. The molten pool contour extraction unit is configured to extract a molten pool contour in the image output by the noise filtering unit through a convolution array of a Canny operator. The minimum circumscribed rectangle extraction unit is configured to obtain a minimum circumscribed rectangle of the molten pool contour by using a Minrect operator. The molten pool length extraction unit is configured to obtain a height of the minimum circumscribed rectangle as a molten pool length by using a Make.Size function.

6. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 1, wherein: The process parameters include any one or several of a welding current, a welding voltage, a wire feeding speed, a scanning speed and an interlayer cooling time.

7. The real-time porosity defect regulated aluminum alloy electric arc additive manufacturing system according to claim 6, characterized in that: The initial values of the welding current, the welding voltage, the wire feeding speed, the scanning speed and the interlayer cooling time are 110 A, 12 V, 5.5 m / min, 480 mm / min and 15 s respectively.

8. A method of real-time porosity defect mitigation in aluminum alloy arc additive manufacturing based on the system of claim 1, wherein, The method comprises: Step 1: performing aluminum alloy electric arc additive manufacturing according to process parameters set for a current printing layer; Step 2: taking an image of a molten pool deposition forming process of the electric arc additive manufacturing, and obtaining a molten pool length after processing; Step 3: when an error of the molten pool length and a preset value exceeds a preset range, performing fuzzy PID control according to the error and a change rate of the error to obtain an optimal length of the current molten pool; Step 4: extracting optimal process parameters corresponding to the optimal length of the current molten pool from a preset relationship table, and adjusting process parameters of a next printing layer to the optimal process parameters, so as to reduce porosity defects, wherein the preset relationship table is a corresponding relationship table of process parameters and molten pool lengths obtained through experiments.

Images