Electric arc additive manufacturing morphology online self-adaptive regulation and control method

Abstract

The invention belongs to the technical field of electric arc additive manufacturing, and discloses an electric arc additive manufacturing morphology online self-adaptive regulation and control method. According to the method, the forming track and the forming parameters of the unformed part are dynamically adjusted according to the deviation between the actual formed part morphology and the theoretical morphology of the part. In this way, an interlayer closed-loop control mechanism is formed, the forming deviation can be reduced, and the flatness of the surface of the deposition layer can be improved. The method can be widely applied to the field of electric arc additive manufacturing, the problem of poor accumulated forming precision of electric arc additive manufacturing is effectively solved, the forming quality and the surface flatness can be improved in the additive manufacturing process, meanwhile, the process stability is guaranteed, and the more accurate and reliable manufacturing process is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of arc additive manufacturing and relates to an online adaptive control method for arc additive manufacturing morphology. Background Art

[0002] Additive manufacturing technology, commonly known as 3D printing technology, is a layer-by-layer discrete manufacturing technology based on digital three-dimensional CAD models. By adding materials in the form of powder, wire or thin sheets layer by layer, the overall forming of the part structure can be quickly achieved. Among them, direct energy deposition technology is a major additive manufacturing technology route, which is widely used in the field of rapid prototyping of medium and large metal components. This technology uses electron beams, lasers, plasma arcs / arcs, etc. as heat sources, and realizes the process of rapid additive manufacturing by melting metal wires at a certain speed and injecting them into the molten pool.

[0003] However, due to the large heat input of the fused deposition technology, the large size of the molten pool, and the relatively high height of the single-layer deposition, the precision of the formed components is low, the surface quality is poor, and defects are prone to occur (Shi X, Yan C, Feng W, et al. Effect of high layer thickness on surface quality and defect behavior of Ti-6Al-4V fabricated by selective laser melting [J]. Optics & Laser Technology, 2020, 132: 106471.). In addition, before plasma arc molten deposition forming, slicing and layering processing is required, in which the slice layer thickness is preset to a fixed value. However, during the deposition process, there are many influencing factors, which cause the deposition width and height of each layer to deviate from the theoretical design. As the deposition process proceeds, these deviations will gradually accumulate and amplify, eventually leading to the inability to carry out the deposition process or even the scrapping of the formed parts (Xiong J, Zhang Y, Pi Y. Control of deposition height in WAAM using visual inspection of previous and current layers [J]. Journal of Intelligent Manufacturing, 2021, 32 (8): 2209-2217.). Therefore, it is urgent to design a device and method to ensure the stability, reliability and smooth progress of the deposition process.

[0004] Therefore, the present invention aims to provide a method for solving the above-mentioned problems, so as to realize closed-loop control of surface morphology in arc additive manufacturing. By dynamically planning and regulating the lower layer forming parameters based on the upper layer morphology, the device and method of the present invention can reduce the forming deviation, improve the surface flatness of the deposited layer, and ultimately realize a more accurate and reliable manufacturing process. Summary of the invention

[0005] In the actual forming process of arc additive manufacturing, fluctuations in process parameters and changes in forming boundary conditions will lead to differences between the actual deposition morphology, size and theoretical preset values. In addition, in the layer-by-layer discrete deposition process, the morphology of the upper deposition layer directly affects the forming of the lower layer, which causes this difference to accumulate and transfer in the open-loop forming process, affecting the subsequent surface flatness and forming quality.

[0006] In view of the problem of poor forming accuracy in arc additive manufacturing technology, the present invention aims to provide an online adaptive control method for arc additive manufacturing morphology, which dynamically plans and controls the lower layer forming parameters based on the upper layer morphology, effectively improving the surface quality and forming accuracy of arc additive formed parts. It can effectively solve the problem of poor forming accuracy in existing arc additive manufacturing technology.

[0007] The technical solution of the present invention:

[0008] An online adaptive control method for arc additive manufacturing morphology, the steps are as follows:

[0009] Step 1: Slice the 3D model of the part to be formed in layers, plan the forming path, and obtain the spatial trajectory and process parameters of the part deposition forming; import the planned spatial trajectory and process parameters into the CNC system and the arc forming system, and the CNC system drives the arc forming system to perform additive forming layer by layer on the substrate according to the planned forming path;

[0010] Step 2: During the arc additive manufacturing process, a topography sensor is used to obtain the surface topography information of the deposited layer online, and the result is transmitted to the computer control system;

[0011] Step 3: The computer control system analyzes the actual morphology information of the deposited layer and obtains the morphology deviation by comparing it with the expected morphology information;

[0012] Step 4: According to the morphology difference results, dynamically plan and update the subsequent forming trajectory and forming process parameters to suppress the surface morphology difference and improve the forming quality and accuracy.

[0013] Furthermore, the arc additive manufacturing morphology online adaptive control method is implemented based on an arc additive manufacturing forming morphology online detection and adaptive control system, including a numerical control system, a motion platform, a computer control system, an arc additive forming system and a morphology detection system.

[0014] Furthermore, the numerical control system drives the arc forming system or the motion platform to form relative motion according to a preset forming trajectory. During the motion, the wire feeding mechanism of the arc forming system melts the metal wire and deposits it on the surface of the forming substrate, thereby obtaining a complete metal part of the desired shape.

[0015] Furthermore, the topography sensor is a structured light sensor, which is used to monitor the topography of the deposited layer online and feed back the topography results to the computer control system in real time in the form of a three-dimensional point cloud. The three-dimensional point cloud needs to be preprocessed first to remove noise points and segment the effective point cloud data of the part surface. The actual forming surface height corresponding to each point is calculated by taking the discrete point as the center and calculating the average height of all topography point clouds within a spatial range with a radius of R.

[0016] Furthermore, it is difficult to accurately reflect the dynamic characteristics of the control process by only using the morphology deviation as the control input, and the control process is prone to over-adjustment. It is best to take into account the morphology of the first two layers, that is, the variation of the inter-layer morphology deviation, to make the control curve converge more quickly. Assume that the part to be formed is composed of n layers of deposition. represents the expected height of the surface of the nth deposition layer at (x,y),

[0017] The actual deposition height is measured:

[0018] ce n (x,y)=e n (x,y)-e n-1 (x,y)

[0019] Among them, e n (x, y) is the height deviation of the topography at the current layer (x, y), e n-1 (x, y) is the height deviation of the topography at the position (x, y) of the previous layer of the current layer, ce n (x, y) is the height deviation change between the two layers.

[0020] Furthermore, in step 3, according to the morphology difference results, the subsequent forming trajectory and forming process parameters are dynamically planned and updated, and a fuzzy control method is adopted; the fuzzy control method adopts a dual-input Mamdani type two-dimensional fuzzy logic controller, which has a fuzzy generator and a fuzzy eliminator; the morphology height deviation e at the current layer (x, y) position is calculated. n (x,y) and shape height deviation change cen (x, y) is used as input to the fuzzy logic inference engine, and the next layer of trajectory adjustment and the corresponding (x, y) position process parameter adjustment are inferred according to the fuzzy rules;

[0021] The adjustment amount of the (x, y) position trajectory of the next layer is:

[0022] H infer (x,y)=H set (x,y)+e n (x,y)

[0023] Among them, H set (x, y) is the preset height of the next layer of trajectory at (x, y), H infer (x,y) is the inference height of the next layer trajectory at (x,y);

[0024] The process parameter is the walking speed, the walking speed V of the next layer corresponding to the (x, y) position infer (x,y) is:

[0025] V infer (x,y)=V set (x,y)+ΔV(x,y)

[0026] Among them, V set (x, y) is the preset stacking speed of the next sedimentary layer at (x, y); ΔV(x, y) is the walking speed adjustment obtained by logical reasoning.

[0027] Fuzzification is the process of converting the actual collected precise physical quantity into a fuzzy quantity that can be recognized by the fuzzy inference engine, in which the conversion mapping function between the precise physical quantity and the fuzzy quantity is called the membership function. The value range of the precise physical quantity is the basic domain. According to process tests, the range of the commonly used walking speed change TS can be set to [0.4, 0.8] m / min, the range of the interlayer weld height deviation e is [-3, 3] mm, and the range of the height deviation change ce is [-2, 2] mm. That is, the domains of the input and output quantities are TS∈[-0.2, 0.4], e∈[-3, 3], and ce∈[-2, 2] respectively.

[0028] In the fuzzy control method, the judgment criterion deviation of the forming height deviation e at each position on the surface of the deposition layer is divided into 7 levels: negative large, negative medium, negative small, zero, positive small, positive medium, positive large, and the corresponding fuzzy subsets are set as NB, NM, NS, ZO, PS, PM, and PB; the shape height error change ce is divided into 5 levels: negative large, negative small, zero, positive small, and positive large, and the corresponding fuzzy subsets are set as NB, NS, ZO, PS, and PB; the output walking speed change TS is also divided into 5 levels: negative large, negative small, zero, positive small, and positive large, and the corresponding fuzzy subsets are set as NB, NS, ZO, PS, and PB. The specific value range of each fuzzy subset is based on the above basic domain, and the mapping between the precise physical quantity and the fuzzy quantity is realized through the membership function, so as to ensure that the fuzzy control method can accurately adjust the walking speed under different deviation levels to optimize the forming quality of the deposition layer.

[0029] Furthermore, in the fuzzy control method, the fuzzy rules are designed based on experience and prior forming adjustment experience, and the basic principle is to control the step size of the fuzzy adjustment based on the deviation and the deviation change. Specifically, when the deviation and the deviation change deviate from the zero point in the same direction, a large adjustment step size in the opposite direction is selected to reduce the error as quickly as possible; when the deviation and the deviation change deviate from the zero point in different directions, the step size needs to be reduced for fine adjustment.

[0030] Furthermore, in step 2, the surface morphology information of the deposited layer is obtained, and it is necessary to complete the calibration of the measurement coordinate system and the forming coordinate system in advance before forming, determine the transformation matrix between the two, and then convert the measurement point cloud in the measurement coordinate system to the printing coordinate system according to the calibration parameters for joint analysis and calculation with the pre-planned lower layer forming trajectory.

[0031] Beneficial effects of the present invention:

[0032] 1. The present invention detects and analyzes the deposition morphology of each layer in real time, and timely adjusts process parameters such as the travel speed, thereby effectively reducing inter-layer and cumulative deviations and significantly improving the geometric accuracy and dimensional consistency of parts.

[0033] 2. The present invention utilizes fuzzy control methods, combined with multi-layer morphology deviation changes, to fine-tune the forming process, significantly improve surface flatness, reduce surface defects, and enhance overall surface quality.

[0034] 3. The present invention adopts an online adaptive control method of arc additive manufacturing morphology based on fuzzy control to control the morphology of parts, and verifies by comparing with parts that have not undergone morphology control, proving that this method can significantly improve the morphology quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention.

[0036] Figure 2 It is a schematic diagram of the device structure of the present invention.

[0037] Figure 3 It is a single straight-wall feature under interlayer control, where (a) is the macroscopic morphology and (b) is the layer-by-layer height distribution curve.

[0038] Figure 4 It is a single straight-wall feature without interlayer control, where (a) is the macroscopic morphology and (b) is the layer-by-layer height distribution curve.

[0039] In the figure: 1-substrate; 2-welding gun; 3-line structured light sensor; 4-deposition layer. DETAILED DESCRIPTION

[0040] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0041] See also Figure 1 and Figure 2 As shown, the arc additive manufacturing morphology online adaptive control method of the present invention comprises the following steps:

[0042] An online adaptive control method for morphology applied in arc additive manufacturing can be implemented through the following steps:

[0043] Step 1: Import the 3D model of the part to be formed into the trajectory planning software to obtain the forming NC code, and then import it into the NC system.

[0044] The trajectory planning software slices the 3D model in layers to obtain the corresponding contours of each slice layer of the 3D model of the metal part, then fills the trajectory of each layer contour to obtain the scanning path of each layer, and plans it in sequence.

[0045] The slice layer thickness and forming process parameters are determined by basic process experiments or experience. The basic process parameters include key factors such as forming voltage, current, and travel speed.

[0046] Step 2: Conduct online measurement of the actual forming morphology of the part during the forming process.

[0047] The motion platform drives the line structured light sensor to scan the upper surface of the part to obtain a three-dimensional point cloud of the part surface in the measurement coordinate system.

[0048] The conversion matrix from the measurement coordinate system to the forming coordinate system is determined through a calibration experiment before forming. After the measurement is completed, the point cloud of the part surface is converted to the forming coordinate system and saved.

[0049] Step 3: Compare the actual formed morphology with the expected formed morphology and analyze the differences.

[0050] Firstly, the original point cloud data of line laser is preprocessed to obtain the smooth point cloud of sedimentary layer. The preprocessing steps include segmentation and denoising.

[0051] Discretize the next layer of space trajectory to be formed into a set of equally spaced discrete points P, P = P ij ∈R 3 |i=1,2,…M;j=1,2,…N, where M is the number of trajectories and N is the number of discrete points on a trajectory.

[0052] During the arc additive manufacturing process, the height of the track corresponding to the weld will fluctuate slightly and frequently. If the adjacent points are too close, the parameters will change too frequently, affecting the stability of the forming process and the welding quality. If the adjacent points are too far away, the parameter adjustment effect will not meet expectations. Considering all factors, the track discrete point spacing is set to 1mm.

[0053] The deviation calculation takes the discrete trajectory position point as the center and the molten pool width as the diameter. The height of the morphology point cloud in this area is taken as the actual height of the trajectory position, and the height deviation of the corresponding discrete trajectory point is obtained by subtracting it from the expected height.

[0054] Step 4: Adjust the trajectory and forming parameters of the next layer based on fuzzy reasoning.

[0055] A Mamdani type two-dimensional fuzzy logic controller is established with height deviation and height deviation change as input and walking speed change as output.

[0056] The judgment criterion deviation of the forming height deviation e at each position on the surface of the deposition layer is divided into 7 levels: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and its corresponding fuzzy subset is set as {NB, NM, NS, ZO, PS, PM, PB}, and the corresponding value is {-3mm, -2mm, -1mm, 0mm, 1mm, 2mm, 3mm}. Similarly, the shape height error change ce is divided into 5 levels {negative large, negative small, zero, positive small, positive large}, and the corresponding fuzzy subset is set as {NB, NS, ZO, PS, PB}, and the corresponding value is {-2mm, -1mm, 0mm, 1mm, 2mm}. The output walking speed change is divided into 5 levels {negative large, negative small, zero, positive small, positive large}, and the corresponding fuzzy subsets are set as {NB, NS, ZO, PS, PB}, and the corresponding values ​​are {0.4m / min, 0.5m / min, 0.6m / min, 0.7m / min, 0.8m / min}.

[0057] The membership function selects the triangle function near the equilibrium point and the Gaussian function at the edge. The membership value of the intersection of two adjacent membership functions is set to 0.5. The centroid method is used in the defuzzification process.

[0058] After the membership function is determined, the fuzzy control rule table can be designed according to the data rule manual, expert experience and prior experimental results. The basic idea is to control the step size of the fuzzy adjustment according to the deviation and the deviation change. For example, when the deviation and the deviation change deviate from the zero point in the same direction, it is necessary to select a large adjustment step size in the opposite direction to reduce the error as soon as possible; when the deviation and the deviation change deviate from the zero point in different directions, it is necessary to reduce the step size for fine adjustment.

[0059]

[0060] like Figure 2 As shown, Figure 4 As shown, the hardware system of the present invention is composed of an optical structure sensor Gocator 2430 and a MIG welding system.

[0061] Figure 3 The layered slicing and path trajectory of a 160 mm long and 20-layer straight-walled feature structure formed layer by layer using two reciprocating deposition methods are shown. During the forming process, all forming process parameters except the walking speed remain the same. The process parameters of the forming process are set as follows:

[0062]

[0063] The parts are deposited and formed by the arc additive manufacturing morphology online adaptive control method based on interlayer fuzzy control proposed in the present invention, such as Figure 3 (a) shows that during the forming process, except for the travel speed, other process parameters remain the same. Starting from the third layer, the travel speed is determined by the fuzzy controller based on the actual measured surface morphology. The process parameter control resolution of the system is set to 1 mm. During the forming process, the height distribution curve of each layer is shown in Figure 3 (b) The results show that the feature part is well formed as a whole, and the height distribution of each layer is uniform with small fluctuations. The growth trend of the height difference between the two ends and the middle area is effectively blocked during the forming process and tends to be stable, ensuring the consistency and stability of the forming quality.

[0064] In contrast, when the method of the present invention is not used for forming, the formed straight wall characteristic structure is as follows Figure 4 (a) As shown. After each layer is formed, the surface morphology is measured online, and the characteristic part height distribution curve is obtained as shown in Figure 4(b). The results show that even with the reciprocating stacking strategy, as the forming process proceeds, starting from the third layer, the height difference between the two ends of the straight-wall feature and the middle end gradually increases. At the 20th layer, the height difference between the two ends and the middle area of ​​the weld is close to 10.2 mm. This "collapse" phenomenon at the arc end leads to poor gas protection effect during the forming process, which in turn affects the arc morphology and forming stability, resulting in a decrease in forming quality. At the same time, the effective forming area is reduced. When the height difference further increases to a certain extent, it may even cause the molten pool to flow, resulting in the inability to continue the forming process.

[0065] Through the above comparative experiments, it can be seen that the online adaptive control method of arc additive manufacturing morphology based on interlayer fuzzy control of the present invention can significantly improve the surface morphology stability and forming accuracy during the forming process, effectively suppress the accumulation of forming deviations, and improve the forming quality and reliability.

Claims

1. An online adaptive control method for arc additive manufacturing morphology, characterized in that: Here are the steps: Step 1: Slice the 3D model of the part to be formed in layers, plan the forming path, and obtain the spatial trajectory and process parameters of the part deposition forming; import the planned spatial trajectory and process parameters into the CNC system and the arc forming system, and the CNC system drives the arc forming system to perform additive forming layer by layer on the substrate according to the planned forming path; Step 2: During the arc additive manufacturing process, a topography sensor is used to obtain the surface topography information of the deposited layer online, and the result is transmitted to the computer control system; Step 3: The computer control system analyzes the actual morphology information of the deposited layer and obtains the morphology deviation by comparing it with the expected morphology information; Step 4: According to the morphology difference results, dynamically plan and update the subsequent forming trajectory and forming process parameters to suppress the surface morphology difference and improve the forming quality and accuracy.

2. The method for online adaptive control of arc additive manufacturing morphology according to claim 1, characterized in that: The arc additive manufacturing morphology online adaptive control method is implemented based on an arc additive manufacturing forming morphology online detection and adaptive control system, including a numerical control system, a motion platform, a computer control system, an arc additive forming system and a morphology detection system.

3. The method for online adaptive control of arc additive manufacturing morphology according to claim 2, characterized in that: The numerical control system drives the arc forming system to form relative motion according to a preset forming trajectory. During the motion, the wire feeding mechanism of the arc forming system melts the metal wire and deposits it on the surface of the forming substrate, thereby obtaining a complete metal part of a desired shape.

4. The method for online adaptive control of arc additive manufacturing morphology according to claim 1, characterized in that: The topography sensor is a structured light sensor, which is used to monitor the topography of the sediment layer online and feed back the topography results in the form of a three-dimensional point cloud to the computer control system in real time.

5. The method for online adaptive control of arc additive manufacturing morphology according to claim 1, characterized in that: Assume that the part to be formed consists of n deposited layers. represents the expected height of the surface of the nth deposition layer at (x,y), The actual deposition height is measured: What n (x,y)=e n (x,y)-s n-1 (x,y) Among them, e n (x, y) is the height deviation of the topography at the current layer (x, y), e n-1 (x, y) is the height deviation of the topography at the position (x, y) of the previous layer of the current layer, ce n (x, y) is the height deviation change between the two layers.

6. The method for online adaptive control of arc additive manufacturing morphology according to claim 1, characterized in that: In step 3, according to the morphology difference results, the subsequent forming trajectory and forming process parameters are dynamically planned and updated, and the fuzzy control method is adopted; the fuzzy control method adopts a dual-input Mamdani type two-dimensional fuzzy logic controller, which has a fuzzy generator and a fuzzy eliminator; the morphology height deviation e at the current layer (x, y) position is calculated. n (x,y) and shape height deviation change ce n (x, y) is used as input to the fuzzy logic inference engine, and the next layer of trajectory adjustment and the corresponding (x, y) position process parameter adjustment are inferred according to the fuzzy rules; The adjustment amount of the (x, y) position trajectory of the next layer is: H infer (x,y)=H set (x,y)+e n (x,y) Among them, H set (x, y) is the preset height of the next layer of trajectory at (x, y), H infer (x,y) is the inference height of the next layer trajectory at (x,y); The process parameter is the walking speed, the walking speed V of the next layer corresponding to the (x, y) position infer (x,y) is: V infer (x,y)=V set (x,y)+ΔV(x,y) Among them, V set (x, y) is the preset stacking speed of the next sedimentary layer at (x, y); ΔV(x, y) is the walking speed adjustment obtained by logical reasoning.

7. The method for online adaptive control of arc additive manufacturing morphology according to claim 6, characterized in that: In the fuzzy control method, the judgment criterion deviation of the deviation e of the forming height at each position on the surface of the deposition layer is divided into 7 levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and the corresponding fuzzy subsets are set as NB, NM, NS, ZO, PS, PM, and PB; the range of the deviation e of the forming height is [-3, 3] mm; the shape height error change ce is divided into 5 levels: negative large, negative small, zero, positive small, and positive large, and the corresponding fuzzy subsets are set as NB, NS, ZO, PS, and PB; the shape height error change ce range is [-2, 2] mm; the output walking speed change TS is divided into 5 levels: negative large, negative small, zero, positive small, and positive large, and the corresponding fuzzy subsets are set as NB, NS, ZO, PS, and PB; the range of the walking speed change TS is [0.4, 0.8] m / min.

8. The method for online adaptive control of arc additive manufacturing morphology according to claim 7, characterized in that: The judgment criterion deviation of the forming height deviation e at each position on the surface of the deposition layer is divided into 7 levels: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the corresponding values ​​are {-3mm, -2mm, -1mm, 0mm, 1mm, 2mm, 3mm}; the morphology height error change ce is divided into 5 levels {negative large, negative small, zero, positive small, positive large}, and the corresponding values ​​are {-2mm, -1mm, 0mm, 1mm, 2mm}; The output walking speed change is divided into 5 levels {negative large, negative small, zero, positive small, positive large}, and the corresponding values ​​are {0.4m / min, 0.5m / min, 0.6m / min, 0.7m / min, 0.8m / min}.

9. The method for online adaptive control of arc additive manufacturing morphology according to claim 6, characterized in that: In the fuzzy control method, the fuzzy rules are designed based on experience and prior forming adjustment experience. The basic principle is to control the step size of the fuzzy adjustment based on the deviation and the deviation change. Specifically, when the deviation and the deviation change deviate from the zero point in the same direction, a large adjustment step size in the opposite direction is selected to reduce the error as quickly as possible; when the deviation and the deviation change deviate from the zero point in different directions, the step size needs to be reduced for fine adjustment.

10. The method for online adaptive control of arc additive manufacturing morphology according to claim 1, characterized in that: In step 2, the surface morphology information of the deposited layer is obtained. It is necessary to complete the calibration of the measurement coordinate system and the forming coordinate system in advance before forming, determine the transformation matrix between the two, and then convert the measurement point cloud in the measurement coordinate system to the printing coordinate system according to the calibration parameters for joint analysis and calculation with the pre-planned lower layer forming trajectory.

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