A method for placing filaments using a three-axis CNC filament-placer and the filament-placer itself.

Abstract

The present application relates to the technical field of fiber placement control, in particular to a fiber placement method of a three-axis numerical control fiber placement head and the fiber placement head. The method simulates and analyzes the process parameters and the motion state near the endpoints of two paths through a basic G code file, determines the influence of the endpoints on the process parameters and the interference on the motion state, and obtains the adjustment parameters at the endpoints; the adjustment parameters are iteratively updated based on the adjustment parameters, and the obtained optimized G code is used for actual fiber placement. The present application simulates and analyzes the process parameters and the motion state near the endpoints between the fiber placement paths, adjusts the related speed of the paths in the G code file to determine the best fiber placement process parameters, realizes the precise optimization of the fiber placement parameters and the stable adaptation of the paths, and improves the fiber placement quality and efficiency.

Description

Technical Field

[0001] This invention relates to the field of filament placement control technology, specifically to a filament placement method and a filament placement head with a three-axis CNC filament placement head. Background Technology

[0002] Automatic yarn placement is an automated molding technology for resin-based composite materials. Multiple prepreg yarn bundles are bundled into a tape using a yarn placement head, and then heated and pressurized according to a programmed control system to lay the bundled prepreg tape layer by layer to form the final product. It is primarily used in the manufacture of composite material structural components. The yarn placement head is the core component of automated yarn placement technology. It combines the independent conveying of different prepreg yarns from winding technology with the compaction and cutting functions of automated tape placement technology. It can adjust the placement angle in real time according to the workpiece surface, meeting the placement requirements of complex workpieces such as three-dimensional curved surfaces. Compared to conventional three-axis equipment, it has greater adaptability and can realize the placement process for three-dimensional curved surfaces.

[0003] A three-axis CNC wire placement head is a type of wire placement head with three rotating axes. Through the angular movement of these three axes, the placement head can be adjusted in real-time along the normal direction according to the workpiece, enabling the placement process of three-dimensional curved surfaces. However, with conventional wire placement heads, when the wire placement path changes at angled positions, even a small rotation of the head results in a large vertical or horizontal movement of the machine tool. This alters the wire placement process parameters along the path, leading to a momentary acceleration of each axis. This momentary acceleration caused by changes in wire placement head parameters results in drastic fluctuations in wire bundle tension, potentially causing wire breakage. It also affects the placement position of the wire placement head, thus reducing wire placement accuracy. Summary of the Invention

[0004] To address the technical problems in the prior art, the present invention aims to provide a three-axis CNC filament placement method and a filament placement head, the specific technical solution of which is as follows:

[0005] This invention provides a method for placing filaments using a three-axis CNC filament-laying head, the method comprising:

[0006] Simulation experiments were conducted based on the wire placement path and process parameters obtained from the basic G-code file of the wire placement component.

[0007] The adjustment range of parameters at the endpoints between the two fiber placement paths and the degree of abrupt change in local process parameters at the endpoints are analyzed to obtain the adjustment range index for each endpoint.

[0008] Based on the deviation of the process parameter fitting path from each wire placement path near the endpoint, the parameter path error index of each wire placement path at the endpoint is determined; by combining the parameter path error index and adjustment range index of the two wire placement paths at each endpoint, the adjustment parameters of each endpoint are obtained.

[0009] The process parameters at the endpoints are adjusted based on the adjustment parameters, and the G-code file is updated. This process is iterated until the stopping condition is met to obtain an optimized G-code file. Wire placement is then performed based on the optimized G-code file and the corresponding process parameters.

[0010] Furthermore, the method for obtaining the adjustment range indicator includes:

[0011] For any two connecting fiber placement paths, the adjustment mutation value of the endpoint is obtained based on the degree of difference between the two fiber placement paths at that endpoint in terms of the magnitude and degree of variation of the process parameters.

[0012] Each fiber placement path connected to the endpoint is taken as the analysis path in turn. Based on the distribution of abrupt changes in process parameters on the analysis path from the endpoint and the local amplitude changes, the degree of abnormal parameter change at the endpoint is obtained.

[0013] By combining the abnormal parameter changes and adjustment mutation values ​​of the two wire laying paths connected at this endpoint, the adjustment range index of this endpoint is obtained.

[0014] Furthermore, the method for obtaining the adjusted mutation value includes:

[0015] Calculate the difference between the two fiber placement paths at the endpoint for each process parameter, and use it as the instantaneous adjustment value for each process parameter;

[0016] Fit each process parameter on each fiber placement path connected at the endpoint to obtain the parameter variation curve of each process parameter; use the slope difference of the parameter variation curves of each process parameter of the two fiber placement paths at the endpoint as the instantaneous change value of each process parameter.

[0017] By combining the instantaneous change value and instantaneous adjustment value of each process parameter, the instantaneous change index of each process parameter at this endpoint is obtained; by combining the instantaneous change index of all process parameters at this endpoint, the adjustment change value of this endpoint is obtained.

[0018] Furthermore, the method for obtaining the abnormal variation degree of the parameters includes:

[0019] For any process parameter, on the parameter variation curve fitted by the process parameter on the analysis path, the absolute values ​​of the slopes are arranged in descending order to obtain a sequence. The positions of the data points corresponding to the first preset number of absolute values ​​of the slopes in the sequence are taken on the analysis path as abrupt change points. The abrupt change point closest to this endpoint is taken as the neighboring outlier.

[0020] On the parameter change curve of the analysis path, the nearest extreme value before and after each mutation point is obtained, and the difference between the extreme values ​​is taken as the local volatility of each mutation point; the maximum local volatility of the mutation point other than the neighboring anomaly points is taken as the maximum variation index.

[0021] Obtain the range of the parameter change curves between the endpoint and the neighboring outliers as the neighboring volatility of the endpoint; normalize the difference between the neighboring volatility of the endpoint and the maximum change index to obtain the outlier significance index of the endpoint.

[0022] By combining the length of the analysis path between the endpoint and the neighboring anomaly points and the anomaly significance index, the anomaly change index of the endpoint and the analysis path for this process parameter can be obtained.

[0023] By combining all the abnormal change indicators of process parameters, the degree of abnormal change of parameters at this endpoint of the analysis path is obtained.

[0024] Furthermore, the method for obtaining the parameter path error index includes:

[0025] For any fiber placement path, the fiber placement points are predicted and fitted based on the process parameters on the fiber placement path to obtain the process fitting path; the minimum distance from each fiber placement point on the process fitting path to the fiber placement path is taken as the deviation value of each fiber placement point.

[0026] For any endpoint of the filament placement path, the endpoint error anomaly degree is obtained based on the degree of difference in the distribution of deviation values ​​between the endpoint's neighboring filament placement points and other filament placement points.

[0027] By combining the deviation values ​​of all filament placement points and the endpoint error anomaly of that endpoint, the parametric path error index of the filament placement path at that endpoint is obtained.

[0028] Furthermore, the method for obtaining the endpoint error anomaly degree includes:

[0029] The filament-laying points located within a preset neighborhood of this endpoint are taken as neighboring filament-laying points;

[0030] The average deviation of all adjacent fiber placement points is taken as the proximity error; the average deviation of all other fiber placement points on the fiber placement path besides the adjacent fiber placement points is taken as the normal error.

[0031] The difference between the neighboring error degree and the normal error degree is normalized to obtain the endpoint error anomaly degree of that endpoint.

[0032] Furthermore, the method for obtaining the adjustment parameters includes:

[0033] For any endpoint, the sum of the parametric path error indices of the two connected wire-laying paths at that endpoint is used as the path anomaly index for that endpoint.

[0034] By combining the path anomaly index and adjustment magnitude index of the endpoint, the adjustment parameters of the endpoint are obtained.

[0035] Furthermore, the method for obtaining the optimized G-code file includes:

[0036] For any endpoint, obtain the velocity of that endpoint in the previous iteration experiment; multiply the value of the endpoint's adjustment parameter by the velocity, and use it as the optimized velocity of that endpoint.

[0037] The process parameters are updated based on the optimized speed of all endpoints, the updated G-code file is regenerated, and iterative simulation experiments are conducted using the updated G-code file until the stopping condition is met. The last updated G-code file is then used as the optimized G-code file.

[0038] Furthermore, the stopping conditions include:

[0039] The iteration stops when the mean of the adjustment parameters of all endpoints in a single simulation experiment is less than the preset adjustment threshold.

[0040] The present invention also provides a three-axis CNC filament placement head, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the filament placement method of the three-axis CNC filament placement head described above.

[0041] The present invention has the following beneficial effects:

[0042] This invention utilizes a basic G-code file for simulation, identifying potential problems before actual fiber placement. This avoids material waste or equipment damage caused by unreasonable parameters during direct operation, providing a reliable data foundation for subsequent optimization. Considering that anomalies are most pronounced when the fiber placement path changes in the G-code file, the process parameters and motion states at and near the endpoints of the two paths are analyzed. This determines the impact of the endpoints on process parameters and their interference with motion states. The dual impact of parameter fluctuation intensity and fiber placement position offset caused by parameter-path mismatch is comprehensively quantified, ensuring that parameter adjustments reduce both abrupt changes and path deviations, improving adjustment accuracy. Through iterative optimization, parameter defects are gradually eliminated, continuously improving the adaptability of process parameters and paths. The resulting optimized G-code avoids abrupt changes in parameters and speeds, eliminating the risk of instantaneous acceleration and ensuring a smooth actual fiber placement process. This invention simulates and analyzes the process parameters and motion states near the endpoints between fiber placement paths, adjusting the relevant speeds of the paths in the G-code file to determine the optimal fiber placement process parameters. This achieves precise optimization of fiber placement parameters and stable path adaptation, improving fiber placement quality and efficiency. Attached Figure Description

[0043] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating a filament placement method using a three-axis CNC filament placement head, as provided in one embodiment of the present invention;

[0045] Figure 2 A flowchart illustrating a method for obtaining an adjustment range index according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of a three-axis CNC filament placement head provided in one embodiment of the present invention. Detailed Implementation

[0047] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a three-axis CNC filament placement head and its filament placement head according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] The following description, in conjunction with the accompanying drawings, details a three-axis CNC filament placement method and a specific scheme for the filament placement head provided by the present invention.

[0050] Please see Figure 1 The diagram illustrates a flowchart of a three-axis CNC filament placement method according to an embodiment of the present invention, which includes the following steps:

[0051] S1: Simulation experiments are conducted based on the wire placement path and process parameters obtained from the basic G-code file of the wire placement component.

[0052] In a specific embodiment of the present invention, for a component requiring wire placement, path analysis is performed on the component to generate a wire placement path. The obtained wire placement path is stored in a path file, such as G-code. The wire placement machine reads this path file to perform wire placement on the component. Each path in the wire placement path contains relevant motion parameters, such as speed and position. For example, for wire placement on a curved surface component, path analysis first determines a series of wire placement trajectories as the wire placement path. These trajectories are written as G-code instructions as a basic G-code file, which the wire placement machine can read for wire placement. The wire placement path includes the coordinates and speed of each position. G-code is an instruction in a CNC program, also known as G-instruction. Using G-code, rapid positioning, reverse circular interpolation, forward circular interpolation, midpoint circular interpolation, radius programming, and jump machining can be achieved.

[0053] Based on the input of the fiber placement path and position parameters into the spatiotemporal graph neural network (ST-GNN), the spatiotemporal graph network can transform the fiber placement path into a graph structure and combine it with temporal convolution to handle time series problems, because the fiber placement path has a sequential relationship, which conforms to the characteristics of time processing. The input of this spatiotemporal neural network is motion parameters, i.e., G-code files, and the output is the fiber placement head's process parameters, such as the A-axis rotation angle of the fiber placement head, etc., obtaining the process parameters for each position on each fiber placement path. For example, if the input G-code instruction is a speed of 50 mm / s at position X=100, the output process parameters for the fiber placement head are a rotation of 30° on the A-axis, a rotation of 15° on the B-axis, and a rotation of 10° on the C-axis.

[0054] When a conventional filament placement head is positioned at an angle, a small rotation of the head results in a significant vertical or horizontal movement of the machine tool, making it more prone to momentary acceleration of the machine tool axes. In this embodiment of the invention, the ABC axes of the filament placement head move smoothly, preventing momentary acceleration of the machine tool axes during position changes. Both axes A and B utilize dual servo motor drives, providing stronger torque and eliminating gear and rack backlash. (See also...) Figure 3 The diagram shows a structural schematic of a three-axis CNC filament placement head provided in an embodiment of the present invention.

[0055] It should be noted that the G-code generation for path analysis and the training of spatiotemporal graph neural networks are techniques well known to those skilled in the art, and will not be elaborated upon here.

[0056] S2: Analyze the parameter adjustment range at the endpoints between the two fiber placement paths, as well as the degree of abrupt change in local process parameters at the endpoints, to obtain the adjustment range index for each endpoint.

[0057] The endpoint where two filament placement paths switch is where parameters are most prone to sudden changes. If the process parameters of the filament placement head differ significantly at the same endpoint under different paths, it indicates that the path change requires a significant adjustment to the process parameters. This causes a sudden change in the instantaneous tension of the filament bundle at the filament placement head. The change in tension makes the filament bundle taut, which increases the gap between adjacent filament bundles during the filament placement process, making it easy to cause defects, such as forming voids, reducing material properties, or insufficient tension leading to insufficient adhesion between the filament bundle and the component surface, reducing interlayer bonding strength.

[0058] To avoid defects, it is necessary to ensure a smooth and continuous transition of process parameters during path changes, and to control and reduce instantaneous speed changes caused by these parameter variations. If the parameters remain optimal during path changes, the process parameters at the path endpoints will be high enough to achieve a normal fiber placement process, with a low probability of sudden acceleration.

[0059] Meanwhile, considering the changes in process parameters at the endpoint, during the actual wire placement process, the process parameters of the wire placement head may be adjusted in advance when the wire is placed near the endpoint, or the adjustment of parameters may be delayed to ensure the continuity of the path change. This ensures that the process parameters of different paths at the endpoint are similar, avoiding instantaneous speed changes caused by large parameter changes. However, if the change is more drastic than normal, it may also lead to a local instantaneous acceleration at the endpoint.

[0060] Therefore, by analyzing the adjustments and abrupt changes in local process parameters at the endpoints and along the path, the required adjustment range index for each endpoint is analyzed. Preferably, in this embodiment of the invention, the method for obtaining the adjustment range index is described in [reference needed]. Figure 2 The diagram illustrates a method for obtaining an adjustment range index according to an embodiment of the present invention, which includes the following steps:

[0061] S201: For any two endpoints connecting the fiber placement paths, obtain the adjustment mutation value of the endpoint based on the degree of difference between the two fiber placement paths at that endpoint in terms of the magnitude and degree of variation of the process parameters.

[0062] First, we analyze the instantaneous changes at the endpoints. When the deviations in parameter magnitude and degree of change are both high, it indicates that the abrupt change at the breakpoint is more drastic, the continuity is worse, and the necessity for adjustment and optimization is higher.

[0063] In this embodiment of the invention, the difference between each process parameter of the two wire laying paths at the endpoint is calculated as the instantaneous adjustment value of each process parameter, reflecting the adjustment range of the parameter value when the path changes.

[0064] Furthermore, each process parameter on each fiber-laying path connected at the endpoint is fitted to obtain a parameter variation curve for each process parameter, and the variation trend of the parameters is continuously analyzed. Then, the difference in slope between the parameter variation curves of each process parameter on the two fiber-laying paths at the endpoint is taken as the instantaneous change value of each process parameter. The greater the difference in the rate of change, the higher the difference in adjustment, and the higher the possibility of fiber-laying changes. In this embodiment of the invention, polynomial fitting can be used to obtain the parameter variation curve. Curve fitting is a technique well-known to those skilled in the art and will not be elaborated or limited here.

[0065] By combining the instantaneous sudden change value and instantaneous adjustment value of each process parameter, the instantaneous change index of each process parameter at the endpoint is obtained. The larger the instantaneous sudden change value and instantaneous adjustment value, the greater the parameter adjustment range and the more violent the change. Both the instantaneous sudden change value and the instantaneous adjustment value are positively correlated with the instantaneous change index. In one embodiment of the present invention, the product of the instantaneous sudden change value and the instantaneous adjustment value is used as the instantaneous change index.

[0066] In another embodiment of the present invention, the parameter value and slope of each process parameter at the endpoint of each wire laying path can be grouped into a binary tuple. The difference between the two binary tuples of the two wire laying paths at the endpoint can be calculated using the L2 norm to obtain the instantaneous change index of each process parameter at the endpoint. The difference between the parameter value and the rate of change can be comprehensively analyzed, which will not be elaborated here.

[0067] Finally, by combining the instantaneous change indices of all process parameters at this endpoint, the adjustment mutation value of this endpoint is obtained, and the sum of the instantaneous change indices of all process parameters is taken as the adjustment mutation value of this endpoint.

[0068] S202: Sequentially take each wire laying path connected to the endpoint as the analysis path, and obtain the degree of abnormal parameter change at the endpoint based on the distribution of process parameter mutations on the analysis path from the endpoint and the local amplitude changes.

[0069] Considering the possibility of premature or delayed changes before the endpoint, by comparing the degree of parameter change at the endpoint with that at other locations along the path to see if it is too abrupt, we can reflect the possibility of instantaneous acceleration. The greater the abnormality of the change, the higher the adjustment requirement at the endpoint needs to be.

[0070] In this embodiment of the invention, for any process parameter, on the parameter change curve fitted by that process parameter on the analysis path, the absolute values ​​of the slopes are arranged in descending order to obtain a sequence. The positions of the data points corresponding to the first preset number of absolute values ​​of the slope in the sequence are taken on the analysis path as abrupt change points. Since the adjustment of the process parameters of the wire placement head will cause normal vibration of the wire placement head, which will further affect the parameters, the abrupt change point with the maximum slope may not be the point used to solve the parameter change caused by the change of the wire placement path during the wire placement process. Therefore, by taking multiple position points with higher slope values ​​for analysis, in this embodiment of the invention, the preset number can be set to 5. The implementer can adjust it according to the specific implementation scenario, and there is no limitation here.

[0071] The mutation point closest to the endpoint is taken as the neighboring anomaly point, and the part from the neighboring anomaly point to the endpoint is the change that is most likely to affect the path switching.

[0072] On the parameter change curve of the analysis path, the nearest extreme value before and after each mutation point is obtained, and the difference between the extreme values ​​is taken as the local fluctuation of each mutation point. For each mutation point, which reflects the existence of normal adjustment changes, the extreme values ​​of the parameters on both sides of the mutation point reflect the local range of the mutation. For example, for the rotation angle, the extreme value on the left side of the mutation point is 18°, the extreme value on the right side is 22°, and the local fluctuation is 4°.

[0073] In the analysis path, the maximum local fluctuation of the mutation point other than the adjacent anomaly point is used as the maximum change index to reflect the maximum range of fluctuation under normal mutation conditions.

[0074] Further, the range of the parameter variation curves between the endpoint and nearby outliers is obtained as the neighborhood volatility of the endpoint, reflecting the degree of parameter variation at the nearby endpoint. The difference between the neighborhood volatility of the endpoint and the maximum variation index is normalized to obtain the anomaly significance index of the endpoint. The larger the anomaly significance index, the more significant the parameter variation near the endpoint. It should be noted that normalization is a technique well known to those skilled in the art, and the choice of normalization can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0075] By combining the length of the analysis path between the endpoint and the nearby anomaly points and the anomaly significance index, the anomaly change index of the endpoint and the analysis path for this process parameter is obtained. Considering the distance between the nearby anomaly points and the endpoint, although the process parameter changes are large, if there is a sufficient distance from the endpoint, the actual change amplitude per unit time is small and no obvious instantaneous acceleration or deceleration phenomenon will occur.

[0076] Therefore, in this embodiment of the invention, the value of the analysis path length between the endpoint and the neighboring anomaly points, after negative correlation mapping, is multiplied by the anomaly significance index to obtain the anomaly change index. A higher anomaly change index indicates that the parameter change per unit distance near the endpoint is more drastic than in other areas, and the anomaly is more likely to occur at the endpoint. It should be noted that negative correlation mapping is a technique well-known to those skilled in the art; for example, negative exponentiation or inverse proportional forms may be used, and will not be limited or elaborated upon here.

[0077] Finally, by combining the abnormal change indicators of all process parameters, the degree of abnormal change of parameters at the analysis path at that endpoint is obtained. This comprehensive assessment of the possible anomalies of all process parameters evaluates the impact of adjustments to the single-sided fiber placement path at the endpoint. In this embodiment of the invention, the sum of all abnormal change indicators of process parameters is used as the degree of abnormal change of parameters at the analysis path at that endpoint.

[0078] S203: By combining the abnormal change rate of parameters and the adjustment mutation value of the two wire laying paths connected at this endpoint, the adjustment range index of this endpoint is obtained.

[0079] By analyzing both wire-laying paths connected at the endpoints, the abnormal variation of parameters on both sides of the wire-laying paths before and after the endpoints was obtained, reflecting the parameter changes near the endpoints. The greater the abnormal variation on both sides, and the greater the abrupt change value of the endpoint position adjustment, the higher the adjustment of the wire-laying head's process parameters is required, and the higher the need for correction of subsequent movements.

[0080] In this embodiment of the invention, the sum of the abnormal variation of parameters of the two wire laying paths connected at the endpoint is used as a local anomaly index to reflect the overall anomaly possibility at the endpoint. The product of the local anomaly index and the adjustment mutation value is normalized to obtain the adjustment magnitude index. The larger the adjustment magnitude index, the more the endpoint needs to be optimized and adjusted.

[0081] S3: Based on the deviation of the process parameter fitting path from each wire placement path near the endpoint, determine the parameter path error index of each wire placement path at the endpoint; combine the parameter path error index and adjustment range index of the two wire placement paths at each endpoint to obtain the adjustment parameters for each endpoint.

[0082] The determination of the fiber placement process parameters near specific endpoint positions depends not only on the process parameters before and after that position, but also on the specific motion state of the fiber placement head. Relying solely on process parameters can lead to significant errors. For example, if the fiber placement path deviates significantly at that endpoint, and a high fiber placement speed is present, inertia may prevent timely deviance. This can result in the process parameters before and after the endpoint changing slightly or exhibiting the same trend, but at this point, the process parameters are in an abnormal state, thus affecting the fiber placement quality and efficiency of the fiber placement head.

[0083] The parameters generated in theory may seem to match the path, but in actual execution, deviations may occur due to mechanical characteristics, parameter mutations, and other issues. By back-fitting the path through process parameters, we can verify whether the operation command can truly make the filament placement head follow the target route.

[0084] Therefore, by analyzing the motion state, it can be reflected that there is motion error at the endpoint of each wire laying path, and the need for adjustment of the operating state at the endpoint can be measured. Preferably, in this embodiment of the invention, the focus is on the deviation of adjacent endpoints. The method for obtaining the parameter path error index includes:

[0085] For any fiber placement path, the fiber placement points are predicted and fitted based on the process parameters along the path to obtain a fitted process path. In one specific embodiment of the invention, interpolation, such as linear interpolation, can be used to sample the path, discretizing it and representing it as an ordered sequence of data points, where each data point is a fiber placement point. The next fiber placement point position is predicted sequentially from the first fiber placement point position based on the process parameters of the fiber placement head, thus obtaining a fitted path.

[0086] As an example, the position of the next fiber placement point in three-dimensional coordinates is: The current location of the fiber placement point is... , The displacement distance along the X-axis can be obtained by adjusting process parameters such as the rotation radius and rotation angle of the wire placement head. Similarly, It is expressed as the displacement distance in the Y-axis direction. This is expressed as the displacement distance along the Z-axis. After determining all the wire placement points according to the wire placement path sequence, since the path curve of each wire placement path is marked in the G-code file (e.g., straight line or arc), the process fitting path can be obtained by connecting all the predicted wire placement points through the fitted curve.

[0087] Furthermore, the minimum distance from each fiber placement point in the process fitting path to that path is used as the deviation value for each fiber placement point. By calculating the degree of deviation between the predicted fiber placement point position and the actual fiber placement path, the accuracy of the fiber placement process parameters is reflected. The larger the deviation value, the more likely the process parameters are to become abnormal. The fiber placement points in the subsequent analysis represent the fiber placement points on the process fitting path. It should be noted that the calculation of the minimum distance from the location point to the path is a technique well-known to those skilled in the art, such as using Euclidean distance, and will not be elaborated here.

[0088] Furthermore, for any endpoint of the fiber placement path, the endpoint error anomaly degree is obtained based on the degree of difference in the distribution of deviation values ​​between the endpoint's neighboring fiber placement points and other fiber placement points. By comparing the difference between the neighboring error degree and other normal error parts, the higher the neighboring error, the more significant the anomaly at the endpoint may be.

[0089] In this embodiment of the invention, the fiber-laying points located within a preset neighborhood range of the endpoint are considered as neighboring fiber-laying points. The preset neighborhood range can be set to a range of 10 data points, and the implementer can adjust the range setting as needed. In other embodiments of the invention, the path length from the corresponding neighboring abnormal point obtained in step S2 to the endpoint can also be used as the preset neighborhood range to focus on deviations on path segments with high fluctuations; this is not a limitation.

[0090] Furthermore, the average deviation of all adjacent fiber placement points is used as the proximity error, reflecting the degree of deviation error at the endpoint. The average deviation of all other fiber placement points besides the adjacent fiber placement points on the fiber placement path is used as the normal error, reflecting the deviation error under normal prediction comparison.

[0091] The difference between the adjacent error degree and the normal error degree is normalized to obtain the endpoint error anomaly degree. The smaller the difference, that is, the lower the endpoint error anomaly degree, the higher the similarity between the errors before and after. In this case, the wire placement process parameters in the neighborhood of the endpoint can operate stably without producing large positional deviations. Conversely, if the error in the neighborhood of the endpoint increases significantly, it indicates that the accuracy of the process parameters is lower.

[0092] Finally, by combining the deviation values ​​of all fiber placement points and the endpoint error anomaly degree of that endpoint, the parametric path error index of the fiber placement path at that endpoint is obtained. Combined with overall error deviation analysis, a high overall error indicates low accuracy of the process parameters and a tendency for anomalies. In this embodiment of the invention, the sum of the mean deviation values ​​of all fiber placement points and the endpoint error anomaly degree of that endpoint is normalized and used as the parametric path error index of the fiber placement path at that endpoint.

[0093] By considering the influence of comprehensive process parameters and their interference with the motion state, the degree of optimization required for each endpoint is determined. In this embodiment of the invention, for any endpoint, the sum of the parameter path error indices of the two connected wire-laying paths at that endpoint is used as the path anomaly index of that endpoint. Taking into account the motion interference of the two paths linked at the endpoint, and combining the path anomaly index and the adjustment range index of that endpoint, the adjustment parameter of that endpoint is obtained. In this embodiment of the invention, the product of the path anomaly index and the adjustment range index of that endpoint is used as the adjustment parameter of that endpoint. The larger the adjustment parameter, the more likely the motion state near the endpoint is to be abnormal, and the higher the adjustment range is required.

[0094] S4: Adjust the process parameters at the endpoints based on the adjustment parameters and update the G-code file, and iterate until the stopping condition is met to obtain the optimized G-code file; perform wire placement based on the optimized G-code file and the corresponding process parameters.

[0095] The speed of wire placement is adjusted based on the adjustment parameters. By reducing the wire placement speed near the endpoints, sufficient time is provided for parameter adjustment, allowing the wire placement head enough time to adjust the parameters and avoid the influence of inertia. A new G-code file can be regenerated by updating the adjusted motion parameters. It should be noted that since the speed at each endpoint of the path is determined, the motion state at each position on the path can be determined. If necessary, the path can be segmented to better describe the motion state at different positions on the same path.

[0096] While optimization reduces speed, the new parameters may cause deviations in other positions, requiring continuous fine-tuning until overall stability is achieved. Subsequent iterations and simulations based on the updated G-code file can determine the optimal wire placement state, yielding an optimized G-code file for CNC machining and improving actual wire placement accuracy.

[0097] In this embodiment of the invention, the method for obtaining the optimized G-code file includes: for any endpoint, obtaining the speed of that endpoint in the previous iteration experiment; if it is the first adjustment, then it is the end speed of the wire-laying path in the corresponding G-code file. The product of the negatively correlated value of the adjustment parameter of that endpoint and the speed is used as the optimized speed of that endpoint. The larger the adjustment parameter, the greater the degree of deceleration required, and the smaller the speed needs to be.

[0098] In another embodiment of the present invention, the included angle between the two wire laying paths connected at the endpoint can be negatively correlated and normalized as the turning adjustment degree, so as to consider the deceleration situation more comprehensively from the turning aspect. When the included angle is larger, it means that the turning is more abrupt and the deceleration requirement is higher. The product of the negatively correlated value of the adjustment parameter, the turning adjustment degree and the speed is used as the optimized speed of the endpoint.

[0099] Based on the optimized speed of all endpoints, the process parameters are updated, the updated G-code file is regenerated, and iterative simulation experiments are conducted using the updated G-code file. In this embodiment of the invention, the updated G-instruction file is re-input into the ST-GNN network, and the corresponding process parameters of the wire placement head are output for re-simulation experiments.

[0100] The process continues until the stopping condition is met. The last updated G-code file is then used as the optimized G-code file, and wire placement can be performed based on the optimized G-code file and the corresponding process parameters. In this embodiment of the invention, the stopping condition includes: when the average value of the adjustment parameters of all endpoints in a single simulation experiment is less than a preset adjustment threshold, it indicates that the current wire placement state has reached its optimal state, and the iteration stops. The preset adjustment threshold is set to 0.1, and the specific value can be adjusted by the implementer according to the specific implementation scenario, without limitation.

[0101] In summary, this invention uses a basic G-code file for simulation to identify potential problems before actual fiber placement, avoiding material waste or equipment damage caused by unreasonable parameters during direct operation, and providing a reliable data foundation for subsequent optimization. Considering that anomalies are most evident in the G-code file during fiber placement path changes, the process parameters and motion states at and near the endpoints of the two paths are analyzed to determine the impact of the endpoints on process parameters and their interference with motion states. The dual impact of parameter fluctuation intensity and fiber placement position offset caused by parameter-path mismatch is comprehensively quantified to ensure that parameter adjustments both reduce abrupt changes and path deviations, improving adjustment accuracy. Through iterative optimization, parameter defects are gradually eliminated, continuously improving the adaptability of process parameters and paths. The resulting optimized G-code avoids abrupt changes in parameters and speeds, eliminates the risk of instantaneous acceleration, and ensures a smooth actual fiber placement process. This invention simulates and analyzes the process parameters and motion states near the endpoints between fiber placement paths, adjusts the relevant speeds of the paths in the G-code file to determine the optimal fiber placement process parameters, achieving precise optimization of fiber placement parameters and stable path adaptation, improving fiber placement quality and efficiency.

[0102] The present invention also provides a three-axis CNC filament placement head, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the filament placement method of the three-axis CNC filament placement head described above.

[0103] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method of laying a fiber by a three-axis numerical control fiber laying head, characterized by, The method comprises: Based on the laying member, a simulation experiment is performed on the laying path and process parameters in the basic G code file; The adjustment amplitude index of each end point is obtained by analyzing the parameter adjustment amplitude between the two laying paths and the mutation degree of the local process parameters at the end point; The parameter path error index of each laying path at the end point is determined based on the deviation of the process parameter fitting path from each laying path near the end point, and the adjustment parameters of each end point are obtained by combining the parameter path error index and the adjustment amplitude index of the two laying paths at each end point; The process parameters at the end point are adjusted based on the adjustment parameters, and the G code file is updated iteratively until the stop condition is met, to obtain an optimized G code file; and the laying is performed according to the optimized G code file and the corresponding process parameters.

2. The method of claim 1, wherein, The adjustment amplitude index is obtained by: For any two end points between the connected laying paths, the adjustment mutation value of the end point is obtained according to the difference between the process parameter size and the change degree of the two laying paths at the end point; The parameter abnormal change degree of the analysis path at the end point is obtained according to the distribution and local amplitude change of the process parameter mutation on the analysis path. The adjustment amplitude index of the end point is obtained by combining the parameter abnormal change degree and the adjustment mutation value of the two connected laying paths at the end point.

3. The method of claim 2, wherein, The adjustment mutation value is obtained by: The difference between each process parameter at the end point of the two laying paths is calculated as the instantaneous adjustment value of each process parameter; The parameter change curve of each process parameter is obtained by fitting each process parameter on each laying path connected to the end point; and the slope difference of the parameter change curve of each process parameter at the end point of the two laying paths is taken as the instantaneous mutation value of each process parameter. The instantaneous change index of each process parameter at the end point is obtained by combining the instantaneous mutation value and the instantaneous adjustment value of each process parameter; and the adjustment mutation value of the end point is obtained by combining the instantaneous change index of all process parameters at the end point.

4. The method of claim 2, wherein the method further comprises: The parameter abnormal change degree is obtained by: For any process parameter, the sequence is obtained by arranging the slope absolute values in descending order on the parameter change curve of the process parameter fitted on the analysis path; the positions of the data points corresponding to the first preset number of slope absolute values in the sequence on the analysis path are taken as the mutation points; and the nearest adjacent abnormal point is the nearest mutation point from the end point. On the parameter change curve of the analysis path, the nearest extreme value before and after each mutation point is obtained, and the extreme value difference is taken as the local fluctuation degree of each mutation point; and the maximum local fluctuation degree of the mutation points except the adjacent abnormal point is taken as the maximum change index. The range of the parameter change curve between the end point and the adjacent abnormal point is obtained as the adjacent fluctuation degree of the end point; and the difference between the adjacent fluctuation degree and the maximum change index of the end point is normalized to obtain the abnormal significant index of the end point. In combination with the length of the analysis path between the endpoint and the adjacent abnormal point and the abnormality significance indicator, the abnormal change indicator of the endpoint and the analysis path in the process parameter is obtained. In combination with the abnormal change indicators of all the process parameters, the parameter abnormal change degree of the analysis path at the endpoint is obtained.

5. The method of claim 1, wherein the method further comprises: The method for obtaining the parameter path error indicator comprises: For any one of the fiber laying paths, the fiber laying points are determined and fitted according to the process parameters on the fiber laying path, and a process fitting path is obtained; the minimum distance from each fiber laying point on the process fitting path to the fiber laying path is taken as the deviation value of each fiber laying point; For any one of the endpoints of the fiber laying path, the endpoint error abnormality degree of the endpoint is obtained according to the difference degree of the deviation value distribution between the adjacent fiber laying points and other fiber laying points of the endpoint; In combination with the deviation values of all the fiber laying points and the endpoint error abnormality degree of the endpoint, the parameter path error indicator of the fiber laying path at the endpoint is obtained.

6. The method of claim 5, wherein, The method for obtaining the endpoint error abnormality degree comprises: The fiber laying points within the preset neighborhood range of the endpoint are taken as the adjacent fiber laying points; The mean value of the deviation values of all the adjacent fiber laying points is taken as the adjacent error degree; the mean value of the deviation values of all the other fiber laying points except the adjacent fiber laying points on the fiber laying path is taken as the normal error degree; The difference value between the adjacent error degree and the normal error degree is normalized to obtain the endpoint error abnormality degree of the endpoint.

7. The method of claim 1, wherein the method further comprises: The method for obtaining the adjustment parameter comprises: For any one of the endpoints, the sum value of the parameter path error indicators of the two connected fiber laying paths at the endpoint is taken as the path abnormality indicator of the endpoint; In combination with the path abnormality indicator of the endpoint and the adjustment amplitude indicator, the adjustment parameter of the endpoint is obtained.

8. The method of claim 1, wherein the method further comprises: The method for obtaining the optimized G code file comprises: For any one of the endpoints, the speed of the endpoint in the previous iteration experiment is obtained; the product of the value of the adjustment parameter of the endpoint after negative correlation mapping and the speed is taken as the optimized speed of the endpoint; Based on the optimized speeds of all the endpoints, the process parameters are updated, the updated G code file is regenerated, and the iteration simulation experiment is performed with the updated G code file until the stop condition is met; the last updated G code file is taken as the optimized G code file.

9. The method of claim 1, wherein, The stop condition comprises: When the mean value of the adjustment parameters of all the endpoints in a single simulation experiment is less than a preset adjustment threshold, the iteration is stopped.

10. A tri-axial CNC fiber placement head comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, The processor executes the computer program to realize the steps of the fiber laying method of the three-axis numerical control fiber laying head according to any one of claims 1-9.

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