CN120326412A - Auxiliary positioning method and system for multi-process switching of precision molds - Google Patents
Auxiliary positioning method and system for multi-process switching of precision moldsInfo
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- CN120326412A CN120326412A CN202510584004.7A CN202510584004A CN120326412A CN 120326412 A CN120326412 A CN 120326412A CN 202510584004 A CN202510584004 A CN 202510584004A CN 120326412 A CN120326412 A CN 120326412A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q3/00—Devices holding, supporting, or positioning work or tools, of a kind normally removable from the machine
- B23Q3/18—Devices holding, supporting, or positioning work or tools, of a kind normally removable from the machine for positioning only
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Abstract
本申请提供了精密模具多工序切换中的辅助定位方法及系统，涉及精密加工技术领域，通过采集当前工序下模具的工艺基准信息并构建三维模具网格模型；以定位参考数据为基准，利用三维模具网格模型进行网格比对和偏移计算，获取实时偏移信息；以消除实时偏移信息为目标，对模具进行一次位置调整，得到一次修正模具；根据下一相邻工序的历史定位误差，对一次修正模具进行二次位置调整，执行下一相邻工序的模具加工。本申请解决了现有技术由于缺乏实时位姿偏移感知与历史误差补偿机制，导致模具定位误差累积，加工精度下降的技术问题，显著提高了模具多工序切换定位精度，增强多工序切换的稳定性和可靠性，提升模具加工质量。
The present application provides an auxiliary positioning method and system in the multi-process switching of precision molds, which relates to the field of precision machining technology. The method and system collect the process reference information of the mold in the current process and construct a three-dimensional mold mesh model; use the positioning reference data as a reference, use the three-dimensional mold mesh model to perform mesh comparison and offset calculation, and obtain real-time offset information; with the goal of eliminating the real-time offset information, adjust the position of the mold once to obtain a corrected mold; according to the historical positioning error of the next adjacent process, adjust the position of the corrected mold twice, and execute the mold processing of the next adjacent process. The present application solves the technical problem that the prior art lacks real-time posture offset perception and historical error compensation mechanism, resulting in the accumulation of mold positioning errors and the reduction of machining accuracy. It significantly improves the positioning accuracy of mold multi-process switching, enhances the stability and reliability of multi-process switching, and improves the mold processing quality.
Description
Technical Field
The application relates to the technical field of precision machining, in particular to an auxiliary positioning method and system in multi-station switching of a precision die.
Background
In the field of precision manufacturing, mold processing typically involves continuous switching of multiple processes, such as roughing, finishing, surface treatment, assembly debugging, and the like. In order to ensure that the die maintains uniform machining reference and clamping precision among different working procedures, a multi-working-procedure switching and positioning technology of the die becomes a key link for guaranteeing machining quality and efficiency.
At present, the positioning method for multi-procedure switching of the precise die is mostly dependent on modes such as a fixed reference block, a mechanical positioning pin or a fixture, and the die is realigned by manual or semi-automatic means. The method has the obvious problems of limited positioning precision, low operation efficiency, incapability of timely detecting and compensating errors and the like, although the structure is simple and the cost is low. Particularly, in the process of switching the die, the die position is easy to slightly deviate due to factors such as temperature change, clamping stress, micro deformation of the die, and the like, and the conventional method is difficult to timely sense errors, so that the errors are gradually amplified in the subsequent process, and the overall machining precision is influenced.
Disclosure of Invention
The application provides an auxiliary positioning method and an auxiliary positioning system for a precise die in multi-process switching, which solve the technical problems of accumulated die positioning errors and reduced machining precision caused by lack of a real-time pose deviation sensing and historical error compensating mechanism for the die in the multi-process switching process in the prior art, and achieve the technical effects of remarkably improving the multi-process switching positioning precision of the die, enhancing the stability and reliability of the multi-process switching and improving the machining quality of the die.
In view of the above problems, on the one hand, the application provides an auxiliary positioning method in multi-process switching of a precision die, which comprises the steps of collecting process reference information of a die in a current process and establishing positioning reference data when the die enters a process switching stage, utilizing multi-source sensing equipment to collect information of the die to construct a three-dimensional die grid model, utilizing the three-dimensional die grid model to conduct grid comparison and offset calculation based on the positioning reference data to obtain real-time offset information, taking the real-time offset information as a target to eliminate, controlling an auxiliary positioning device to conduct primary position adjustment on the die to obtain a primary corrected die, conducting secondary position adjustment on the primary corrected die through a multi-axis adjusting device according to the historical positioning error of a next adjacent process to finish die position correction, and executing die processing of the next adjacent process.
On the other hand, the application also provides an auxiliary positioning system in the multi-process switching of the precise die, which comprises a positioning reference construction module, a die information acquisition module, an offset calculation module, a primary correction module and a secondary correction module, wherein the positioning reference construction module is used for acquiring process reference information of the die in the current process and establishing positioning reference data when the die is processed, the die information acquisition module is used for acquiring information of the die by using multi-source sensing equipment to construct a three-dimensional die grid model when the die enters the process switching stage, the offset calculation module is used for carrying out grid comparison and offset calculation by using the three-dimensional die grid model by taking the positioning reference data as a reference, acquiring real-time offset information, the primary correction module is used for controlling an auxiliary positioning device to carry out primary position adjustment on the die to obtain a primary correction die, and the secondary correction module is used for carrying out secondary position adjustment on the primary correction die by using a multi-axis adjusting device according to the historical positioning error of the next adjacent process to finish die position correction, and the die processing of the next adjacent process is carried out.
The one or more technical schemes provided by the application have at least the following beneficial effects:
And when the die is processed, process reference information of the die under the current working procedure is acquired, positioning reference data is established, an accurate reference standard is provided for positioning in the subsequent working procedure switching, and the consistency and continuity of data among multiple working procedures are ensured. When the die enters a process switching stage, information acquisition is carried out on the die by utilizing multi-source sensing equipment, and a three-dimensional die grid model is constructed so as to truly reflect the actual position and morphology of the die after switching, and a detailed data basis is provided for subsequent comparison and calculation. And taking the positioning reference data as a benchmark, performing grid comparison and offset calculation by using the three-dimensional mould grid model, realizing the quantitative analysis of the difference between the actual state of the mould and the theoretical benchmark, acquiring accurate real-time offset information, and providing decision basis for subsequent positioning correction. And according to the historical positioning error of the next adjacent process, carrying out secondary position adjustment on the primary correction die through a multi-axis adjusting device to finish die position correction, improving the stability of integral positioning and feedforward compensation capability, ensuring the consistency of the starting point of the next process, and then executing die processing of the next adjacent process.
In summary, the application realizes high-precision auxiliary positioning of the precision die in the multi-working-procedure switching process by fusing a dual mechanism of real-time sensing and historical data driving. According to the scheme, the real-time offset calculation of the die pose is realized by using the process reference data and the three-dimensional grid model, and the positioning consistency and the compensation precision in the process of die procedure connection are remarkably improved by using a double-stage position adjustment strategy of current offset correction and historical error compensation, so that the problem of error accumulation caused by insufficient perceptibility of a traditional method is effectively avoided, the adaptability to micro deformation and displacement under complex working conditions is enhanced, and the technical effects of remarkably improving the multi-procedure switching positioning precision of the die, enhancing the stability and reliability of multi-procedure switching and improving the processing quality of the die are finally achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of an auxiliary positioning method in multi-process switching of a precision die according to an embodiment of the application.
Fig. 2 is a schematic flow chart of constructing a three-dimensional mold grid model in the auxiliary positioning method in the multi-process switching of the precision mold according to the embodiment of the application.
Fig. 3 is a schematic structural diagram of an auxiliary positioning system in multi-process switching of a precision die according to an embodiment of the present application.
Reference numerals illustrate the positioning reference construction module 10, the die information acquisition module 20, the offset calculation module 30, the primary correction module 40 and the secondary correction module 50.
Detailed Description
By providing the auxiliary positioning method and the auxiliary positioning system in the multi-process switching of the precise die, the technical problems that in the prior art, due to the lack of a real-time pose deviation sensing and historical error compensating mechanism for the die in the multi-process switching process, the die positioning error is accumulated and the processing precision is reduced are solved, and the technical effects of remarkably improving the multi-process switching positioning precision of the die, enhancing the stability and the reliability of the multi-process switching and improving the processing quality of the die are achieved.
In a first embodiment, as shown in fig. 1, an embodiment of the present application provides an auxiliary positioning method in precision mold multi-process switching, where the method includes:
And step 100, collecting process reference information of the lower die in the current working procedure and establishing positioning reference data during die processing.
Specifically, the process reference information refers to geometric elements and parameters according to which the mold determines the machining or clamping position in the current process, and generally includes reference points, reference surfaces, reference lines, and process parameters such as machining and clamping. In the initial processing stage of the die, key reference characteristics of the die in the current working procedure, including geometric information such as a reference surface (such as a bottom surface or a clamping surface), a reference line (such as a symmetrical center line), a reference point (such as a positioning hole center) and the like, are acquired in real time through a sensor system arranged on a numerical control device or a detection table. And simultaneously, combining machining parameters (such as tool path, rotating speed and feeding) and clamping parameters (such as clamping force and clamping position) to build a machining model. On the basis, a local coordinate system of the die is constructed by the collected basic geometric information, and the gesture of the die is finely adjusted according to the technological parameters, so that positioning reference data with high-precision description capability is generated, accurate capturing and standardized expression of the die at the initial processing position and gesture are realized, a high-quality basic basis is provided for comparing and correcting the gesture in the subsequent multi-step switching process, and the accuracy of subsequent positioning compensation is remarkably improved. For example, in the first milling process, an XY plane reference is established by taking the centers of four positioning holes as reference points, and then the positioning edges of the fixture are used as the Z reference surfaces. The machining parameters are 300mm/min of feeding, the spindle rotating speed is 2000rpm, the clamping force is 1200N, and finally a positioning reference coordinate system containing 6-dimensional pose description is established.
And step 200, when the mould enters a process switching stage, information acquisition is carried out on the mould by utilizing multi-source sensing equipment, and a three-dimensional mould grid model is constructed.
Specifically, the die is information-collected using multi-source sensing devices deployed around a plant measurement area or process platform before the current process is completed and ready to enter the next process. Multisource sensing devices include image sensors, laser scanners, structured light devices, and the like. The image sensor captures the texture and characteristic pictures of the surface of the mould from a plurality of angles, and the laser scanner acquires point cloud data through light beam scanning to cover the whole appearance of the mould. And carrying out contour extraction and characteristic point positioning on the image data, fusing the image data with the point cloud data, and fitting to form a complete three-dimensional mold surface model. Then, grid dimensions are set according to the process precision requirements, grid division is carried out on the three-dimensional surface, and a three-dimensional grid model with continuous structure is generated for subsequent comparison and analysis. Through multi-source perception and high-precision three-dimensional modeling, the real state of the die during process switching can be comprehensively restored, a digital basis is provided for subsequent pose offset analysis and dynamic correction, and the comprehensiveness and accuracy of error identification are ensured.
And step 300, carrying out grid comparison and offset calculation by using the three-dimensional mould grid model by taking the positioning reference data as a benchmark, and obtaining real-time offset information.
Further, the real-time offset information includes a coordinate position offset and an attitude angle deviation.
Specifically, the positioning reference data established in the step S100 is used as a target template, the three-dimensional mold grid model obtained in the step S200 is used as a current state, and the two are subjected to point-to-point comparison through an ICP (iterative closest point) algorithm. The comparison process calculates the spatial offset of each grid point and generalizes to real-time offset information including coordinate position offset and attitude angle offset. The coordinate position offset includes a displacement change in a three-dimensional space (X, Y, Z directions) of the mold, the attitude angle offset includes a change in rotational attitude (Pitch angle, yaw angle, roll angle) of the mold, and the six-degree-of-freedom offset parameters can be integrally expressed as three position offsets (Δx, Δy, Δz) and three attitude angle offsets (Δpitch, Δyaw, Δroll), wherein Δx is a displacement offset in an X-axis direction, Δy is a displacement offset in a Y-axis direction, Δz is a displacement offset in a Z-axis direction, Δpitch is a Pitch angle offset, Δyaw is a Yaw angle offset, and Δroll is a Roll angle offset. The method realizes high-precision detection and parameterization expression of the current pose error of the die, provides a clear quantization target for subsequent automatic position adjustment, and greatly improves the error response speed and compensation precision in the process of procedure switching.
And step 400, controlling the auxiliary positioning device to perform primary position adjustment on the die to obtain a primary correction die by taking the real-time offset information as a target.
In particular, the auxiliary positioning device refers to a mechanical or electric control structure for precisely adjusting the position of the mold, such as a pneumatic positioning pin, a six-degree-of-freedom flexible positioning platform, a servo control mechanism and the like. After receiving the offset information output in step S300, calculating an adjustment path according to the quality and structural characteristics of the mold, and driving the auxiliary positioning device to perform position correction. The auxiliary positioning device performs small displacement or posture adjustment on the die according to the set path, and ensures that the die enters an error tolerance zone (for example within +/-0.05 mm and +/-0.1 degrees). After the adjustment is finished, the current mold state is marked as a one-time correction mold and is used for subsequent finer historical error correction, so that the subsequent fine adjustment complexity is reduced, and the mold switching efficiency and the stability of the correction effect are improved.
And S500, performing secondary position adjustment on the primary correction die through a multi-axis adjusting device according to the historical positioning error of the next adjacent process, finishing die position correction, and executing die processing of the next adjacent process.
Specifically, the historical positioning error is based on high-frequency offset information obtained by statistics in positioning adjustment records of the same or similar molds in similar procedures in the past. The multi-axis adjusting device is a positioning platform or a robot with a plurality of degrees of freedom adjusting capability, and can realize high-precision space pose adjustment.
In order to further improve the positioning accuracy, the historical positioning error data of the similar molds stored on the industrial Internet platform in the adjacent working procedure are consulted after one-time correction, and an error prediction model is built by combining the geometric deviation of the current mold. According to the offset trend output by the model, small-range high-precision fine adjustment (such as +/-0.01 mm) is performed through a multi-axis adjusting device, and finally the posture of the die is close to a theoretical optimal state. After the adjustment is completed, the die is locked at a new position, and the processing flow of the next procedure is entered. The step introduces a historical data feedback and high-precision multi-axis adjustment mechanism, so that the positioning strategy has self-adaptability and prospective, error accumulation is effectively prevented, and the continuity and the machining precision of the process connection are improved.
Further, step S100 includes:
And S110, collecting a datum point, a datum plane, a datum line and a process parameter of a lower die of the current working procedure as the process datum information, wherein the process parameter comprises a processing parameter and a clamping parameter.
And step S120, establishing a reference coordinate system and a target attitude angle of the die based on the reference points, the reference surfaces and the reference lines.
And step 130, fine tuning the base coordinate system and the target attitude angle according to the processing parameters and the clamping parameters to obtain the positioning reference data.
Specifically, in the initial working procedure of the die, geometric reference elements such as a reference point, a reference surface, a reference line and the like of the die in the working procedure, and process parameters such as processing parameters, clamping parameters and the like are collected by accessing a machine tool control system, a sensor interface or a process design system (such as CAM software). The machining parameters comprise parameters required by numerical control machining such as feeding speed, spindle rotating speed, cutting path and the like, and the clamping parameters comprise clamping force, clamping mode, clamping position and other technological conditions for fixing the die. For example, the center of the positioning hole, the clamping plane and the like are identified by using a visual sensor and a measuring head, the processing speed, the tool path and the like in the G code are read by a controller, and the clamping force and the contact point position are recorded in real time by a clamp monitoring system. Together, these information constitute the complete process reference information for the process.
A logical coordinate system is determined in the mold model based on the acquired reference geometric elements. For example, a reference point is set as a coordinate origin, a reference plane defines a Z-axis direction, a reference line defines an X-axis direction, a Y-axis is automatically deduced, and the right-hand construction of a coordinate system is completed. Meanwhile, by comparing the design gesture of the model, an ideal target gesture angle (such as 0-degree yaw, 0-degree roll and 3-degree pitch) required by the current working procedure is generated, discrete geometric features are converted into a standardized coordinate system and gesture definition, the quantifiable expression of the state of the die is realized, and stable reference is provided for offset detection and automatic correction.
There may be a slight shift in the original reference coordinate system and attitude angle due to machining errors, clamping deformations, or mold loading deviations. Therefore, simulation calculation is carried out by combining machining parameters (such as a main shaft thrust direction, a cutter contact area) and clamping parameters (a force application direction of a clamping point and clamp rigidity), a fine adjustment vector is generated and acts on an original reference coordinate system to dynamically correct the reference coordinate system and a target attitude angle, positioning reference data is obtained, an adaptive correction mechanism combining an ideal attitude and an actual working condition is realized, the fitting degree and usability of the reference data are improved, and therefore accuracy and robustness of die follow-up position recognition and correction actions are enhanced.
Further, as shown in fig. 2, step S200 includes:
And S210, after the processing of the current process is finished, acquiring a mold image set by utilizing an image sensor to acquire an image of the mold according to a preset acquisition angle, and carrying out omnibearing scanning on the mold by utilizing a laser scanner to obtain a mold point cloud set.
And S220, extracting contour lines according to the mold image set, and fusing and constructing a three-dimensional mold contour.
And S230, performing point cloud fitting on the mold point cloud set by utilizing the three-dimensional mold contour to construct a three-dimensional mold model.
And step S240, carrying out grid division on the three-dimensional mould model according to a preset grid size to obtain the three-dimensional mould grid model.
Specifically, after the current process is finished, an image acquisition and scanning process is started. The method comprises the steps of firstly, carrying out image acquisition by an image sensor around a die according to a preset angle (for example, every 30 degrees of rotation) under the dispatching of a control system to generate a multi-view image set, and then, carrying out high-precision laser ranging on the whole surface of the die by a laser scanner to obtain an original point cloud set of the die, thereby providing a foundation for the construction of a follow-up three-dimensional model.
And (5) invoking an image processing algorithm (such as Canny edge detection, sobel operator and depth edge recognition model) to analyze the model image set and extract contour feature lines. The contour lines are converted into three-dimensional coordinates (such as stereoscopic reconstruction, beam triangulation and the like) through camera calibration data, so that a preliminary three-dimensional contour of the die is formed. These contours can be referenced with the point cloud data as structural boundaries in a subsequent fusion process.
And taking the obtained three-dimensional contour as structural constraint, performing surface fitting and topology reconstruction on the obtained point cloud, converting the mold point cloud into a continuous curved surface model, constructing a three-dimensional mold model, realizing the conversion of the mold surface from discrete points to a continuous curved surface, ensuring good visualization effect and subsequent analysis applicability of the model, and improving the expression accuracy of the mold model.
And splitting the three-dimensional curved surface by using a grid generation algorithm (such as Delaunay triangulation, octree segmentation and the like) according to the preset grid size parameters to generate a three-dimensional mould grid model with equal density or self-adaptive density. The preset grid size is determined according to the actual machining state. The model has the characteristics of light structure and high precision, and is convenient for subsequent execution of offset analysis, gesture comparison or simulation verification. Through gridding treatment, the three-dimensional mould model obtains a standardized structural form, and can efficiently participate in calculation processes such as subsequent comparison, error analysis, posture adjustment and the like, thereby remarkably improving the operation efficiency and analysis precision of the system.
Further, step S220 includes:
and S221, training the convolutional neural network to be converged by adopting a sample die image set and a sample die contour set of the similar die to obtain a contour line extractor.
And step S222, respectively extracting contour lines of a plurality of die images in the die image set by using the contour line extractor to obtain a plurality of die contours.
And S223, carrying out feature registration and smooth restoration according to the plurality of mold contours based on the preset acquisition angles, and constructing a three-dimensional mold contour.
Specifically, the sample mold image set is multi-angle image data of a known type of mold collected in advance. The sample die contour set is a die edge line set which is manually marked or automatically extracted and is used as a training label. And using a supervised learning method, taking images of a large number of similar molds and corresponding contour labels thereof as training data, and training a convolutional neural network model to learn edge structural features in the images so as to obtain a contour line extractor. The training process is exemplified by firstly collecting and sorting a sample mold image set of the similar mold and a corresponding contour line labeling image to construct a training sample pair. Each sample comprises a mold image from a plurality of preset acquisition angles and a corresponding contour line annotation graph thereof, and the annotation process draws pixel-level contour edges through image annotation software and uniformly converts the contour edges into a binarization mask image. In order to enhance the generalization capability of the model, data enhancement operations including rotation, flipping, brightness disturbance, blurring processing and affine transformation are performed on the image samples, and the number of samples and visual diversity are expanded. Then, a convolutional neural network model taking U-Net as a main structure is constructed, input is an RGB (red, green and blue) mould image, and output is a contour probability map. The downsampling part in the network adopts ResNet-18 as a feature extraction skeleton, and the upsampling part uses a multi-layer deconvolution module for resolution recovery. In the training process, a weighted combination of a two-class cross entropy loss (BCE), a Dice loss and an edge structure loss is adopted as a total loss function, so that the recognition accuracy of the model to the contour edge is enhanced. Model training adopts an Adam optimizer, the initial learning rate is set to be 0.001, the number of samples selected by one training is 8, the training rounds are 100, the training set and the verification set are divided according to the proportion of 8:2, and model performance monitoring is carried out through indexes such as the cross-over ratio, the F1 score, the edge offset error and the like. After the performance index of the model on the verification set tends to be converged and stable, saving the model weight file which is finally trained as a contour line extractor for batch contour extraction tasks of subsequent die images.
And inputting the die image set acquired by the image sensor into a contour line extractor, respectively performing edge detection, filtering and contour positioning operation on each image, and outputting a contour map matched with the image in size. The contour map sets corresponding to all the images form a plurality of mold contours, so that preparation is made for subsequent three-dimensional modeling.
And carrying out three-dimensional reconstruction projection on the contour lines with different angles by utilizing a preset acquisition angle during image shooting. And then, spatial alignment is carried out on the outlines of all the visual angles by adopting an ICP (iterative closest point) registration algorithm, characteristic point matching and other methods. And (3) performing curvature filtering and smooth interpolation on edge lines with defects, burrs and break points to realize contour restoration, and finally combining the edge lines into a continuous and complete three-dimensional contour frame, so as to realize unified expression of the multi-angle image contour of the die in a three-dimensional space, generate a three-dimensional contour frame with continuous structure and controllable precision, and provide high-quality geometric guiding information for subsequent point cloud fitting.
Further, step S230 includes:
and S231, carrying out random fitting on the mold point clouds for one time by utilizing the three-dimensional mold outline to obtain a first point cloud fitting result, and calculating the ratio of the number of point clouds falling into the first point cloud fitting result to the total number of point clouds in the mold point clouds to obtain a first fitting precision.
And S232, carrying out secondary random fitting on the mold point cloud set by utilizing the three-dimensional mold outline again to obtain a second point cloud fitting result and second fitting precision.
And S233, performing iterative fitting until the preset fitting times are reached, outputting a point cloud fitting result with the maximum fitting precision to be set as an optimal point cloud fitting result, and constructing a three-dimensional mold model according to the optimal point cloud fitting result.
Specifically, a random sample consensus algorithm (RANSAC) is used to perform a preliminary point cloud fit using the already constructed three-dimensional mold contour as a reference model prior to fitting the mold point cloud set. The method comprises the specific operation that a plurality of points in a mould point cloud are randomly selected as initial fitting seed points, and a preliminary plane or curved surface fitting model is generated by utilizing the seed points. And then, calculating the distance between each point in all the die point clouds and the fitting model, and judging whether the distances accord with a preset tolerance threshold value or not. The eligible points are considered to be points that fall within the fit result, and the number of these points is recorded. And calculating the ratio of the number of the point clouds falling into the fitting result to the total number of the point clouds of the die point clouds, and obtaining the accuracy of the first fitting. The higher the ratio, the better the fitting effect, and the higher the accuracy.
After the first fitting, in order to further improve the fitting precision, the point cloud is secondarily fitted by utilizing the three-dimensional die contour. At this point, the same random sample consensus algorithm (RANSAC) as the first time is used, but the initially selected seed points and tolerance values are adjusted to further refine the fit result. The new fitting model will generate a new point cloud fitting result and recalculate the fitting accuracy. Likewise, the criterion for calculating the fitting accuracy is the ratio of the number of point clouds falling into the fitting result to the total number of point clouds. If the accuracy of the second fitting result is higher than that of the first fitting result, the point cloud fitting result generated at this time is the second fitting result.
And performing iterative fitting according to the steps until the preset maximum fitting times are reached, and calculating fitting precision for each point cloud fitting result. And when the iteration times reach a preset limit, selecting a fitting result with the highest fitting precision as an optimal point cloud fitting result. And finally, constructing a three-dimensional mold model according to the optimal point cloud fitting result, ensuring that the geometric shape of the model is matched with the point cloud data of the actual mold to the greatest extent, and providing a high-quality model foundation for subsequent grid division, positioning analysis and other operations.
Further, the generating method of the preset mesh size in step S240 includes:
and step S240-1, monitoring and acquiring a processing parameter sequence and a clamping parameter sequence in the processing process of the current procedure.
And S240-2, carrying out deviation analysis on the processing parameter sequence and the clamping parameter sequence by taking the standard processing parameter and the standard clamping parameter of the current working procedure as references to obtain a processing parameter deviation amplitude sequence and a clamping parameter deviation amplitude sequence.
And S240-3, carrying out mean value calculation and fluctuation analysis on the processing parameter deviation amplitude sequence, and outputting a processing parameter deviation amplitude mean value and a processing parameter fluctuation coefficient, wherein the fluctuation coefficient is the ratio of a standard deviation to the mean value.
And S240-4, carrying out mean value calculation and fluctuation analysis on the clamping parameter deviation amplitude sequence, and outputting a clamping parameter deviation amplitude mean value and a clamping parameter fluctuation coefficient.
And S240-5, obtaining the current machining deviation scale according to the machining parameter deviation amplitude average value, the machining parameter fluctuation coefficient, the clamping parameter deviation amplitude average value and the clamping parameter fluctuation coefficient through weighted calculation.
And S240-6, calculating the ratio of the preset machining deviation scale of the current working procedure to the current machining deviation scale, and multiplying the ratio by the initial grid size to obtain the preset grid size.
Specifically, various processing parameters and clamping parameters are continuously monitored and collected in the current working procedure processing process. These parameters include cutting speed, feed speed, tool position, workpiece position, clamping force, etc. These parameter sequences are recorded in real time by sensors, control systems or data acquisition devices to provide the necessary raw data for subsequent bias analysis.
After the processing parameter sequence and the clamping parameter sequence of the current working procedure are obtained, the standard processing parameter and the standard clamping parameter of the current working procedure are used as references, and deviation analysis is carried out on the actual processing parameter sequence and the actual clamping parameter sequence. Specifically, the deviation amplitude of each of the machining parameters and the clamping parameters from the standard value is calculated, and a machining parameter deviation amplitude sequence and a clamping parameter deviation amplitude sequence are generated. These deviation amplitude data can be used to reflect the instability or source of error in the process.
Then, the average value calculation and fluctuation analysis are carried out on the processing parameter deviation amplitude sequence, and the average value of the processing parameter deviation, namely the average degree of deviation, is firstly obtained. Then, calculating standard deviation of the deviation sequence to obtain a fluctuation coefficient, wherein a fluctuation coefficient formula is that the fluctuation coefficient=standard deviation/mean value, and the fluctuation coefficient is used for describing fluctuation amplitude of the processing parameters, namely the change degree of the parameters in the actual processing process. The smaller the average value of the deviation amplitude of the processing parameter is, the lower the fluctuation coefficient is, which means that the processing process is more stable and the error is smaller.
Similarly, the clamping parameter deviation amplitude sequence is subjected to mean value calculation and fluctuation analysis. And (5) calculating the mean value of the clamping parameter deviation, and calculating the fluctuation coefficient of the mean value. The deviation analysis of the clamping parameters can reveal possible errors and unstable factors in the clamping process, and the smaller the average value of the deviation amplitude is, the smaller the fluctuation coefficient is, so that the higher the stability of the clamping process is.
And carrying out weighted calculation according to the machining parameter deviation amplitude average value, the machining parameter fluctuation coefficient, the clamping parameter deviation amplitude average value and the clamping parameter fluctuation coefficient to obtain the machining deviation scale of the current working procedure. The processing deviation scale integrates the influence of all deviation amplitude and fluctuation coefficient, and can accurately reflect the overall situation of actual processing errors in the working procedure. Generally, the smaller the deviation, the smaller the fluctuation, and the smaller the processing deviation scale.
And finally, calculating the ratio of the preset machining deviation scale to the actual machining deviation scale of the current working procedure. This ratio reflects the deviation between the accuracy of the current process and the preset standard. Multiplying the ratio by the initial mesh size to obtain the preset mesh size. If the deviation is large, the grid size needs to be reduced, so that the grid division precision is improved, and the higher machining precision is ensured. Conversely, if the deviation is small, the grid size may be increased appropriately to optimize the computational efficiency. The initial grid size is a grid division size set according to the mould size and the initial precision requirement and is used as a reference for adjusting the preset grid size.
Through the steps, the preset grid size can be dynamically adjusted according to the actual deviation condition of the machining parameters and the clamping parameters, so that the required precision and stability can be achieved through die positioning and error correction in the machining process.
Further, step S500 includes:
And S510, performing grid comparison and offset calculation by using the three-dimensional mould grid model to obtain the geometric deviation of the mould in the current working procedure, and expanding the geometric deviation according to a preset deviation tolerance interval to obtain a geometric deviation interval.
And S520, carrying out similar mold processing information retrieval based on the industrial Internet by taking the geometric deviation interval and the next adjacent procedure as retrieval constraints, and determining a sample historical positioning error set.
And step S530, analyzing and determining a historical positioning error according to the sample historical positioning error set, and performing secondary position adjustment on the primary correction die by using a multi-axis adjusting device with the aim of eliminating the historical positioning error.
Specifically, the preset deviation tolerance interval is an allowable deviation range set according to process requirements and experience, and is used for expanding geometric deviation to form a geometric deviation interval. And (5) performing grid comparison and offset calculation by using the currently generated three-dimensional mold grid model. By comparing the actual geometry of the mold in the current process with a preset geometry (typically a standard model or a reference model), the geometric deviation of the mold, including dimensional deviation, angular deviation, etc., is calculated. And expanding the geometric deviation according to a preset deviation tolerance interval to form a geometric deviation interval. This expansion is to cover the possible error range, ensuring that even if the deviation fluctuates, the corresponding compensation can still be tolerated and performed. In this way, it is ensured that the mould can still be maintained within a suitable tolerance range when the mould is subjected to secondary adjustment, avoiding other problems caused by excessive correction.
And taking the obtained geometric deviation interval as a retrieval constraint, and combining the processing requirements of the next adjacent process, retrieving the processing information of the similar die by using an industrial Internet platform, and inquiring the processing data of the similar die under the similar process. From these retrieved history data, a sample history positioning error set is screened. This error set contains positioning error information experienced by other molds during the same or similar procedure. By analyzing these historical positioning errors, reference data can be provided for secondary adjustments of the current mold.
And carrying out statistical analysis according to the data in the sample historical positioning error set to determine the historical positioning error. On the basis of this, a correction scheme for eliminating the history positioning error is set, and the secondary position adjustment is performed. The multi-axis adjusting device is used for reversely adjusting (i.e. compensating in advance) the die, and the forthcoming error is eliminated by presetting the compensation quantity before the working procedure, so that the accumulation of the error is reduced, and the processing precision of the next working procedure is effectively improved.
Further, step S530 includes:
Step S531, a sample historical positioning error set is obtained, wherein the sample historical positioning error comprises X-axis offset data, Y-axis offset data, Z-axis offset data, pitch angle deviation, yaw angle deviation and roll angle deviation.
Step S532, randomly selecting a first sample historical positioning error, if the ratio of any deviation value in the first sample historical positioning error to other similar data in the sample historical positioning error set is smaller than a preset deviation threshold value and is larger than a preset proportion, setting the first sample historical positioning error as a high-frequency historical positioning error, and sequentially screening and obtaining a high-frequency historical positioning error set.
And step S533, carrying out mean value calculation on the high-frequency historical positioning error set to obtain the historical positioning error.
Specifically, positioning data of the same type of mold in the same or similar procedure is searched, a sample historical positioning error set is obtained, and each sample historical positioning error data comprises X-axis offset data, Y-axis offset data, Z-axis offset data, pitch angle deviation, yaw angle deviation and roll angle deviation. The X-axis offset data is the position offset of the die in the X-axis direction, the Y-axis offset data is the position offset of the die in the Y-axis direction, the Z-axis offset data is the position offset of the die in the Z-axis direction, the pitch angle deviation is the inclination angle deviation of the die around the X-axis direction, the yaw angle deviation is the rotation angle deviation of the die around the Z-axis direction, and the roll angle deviation is the rotation angle deviation of the die around the Y-axis direction.
Randomly selecting a first sample historical positioning error from the sample historical positioning error set. Then, the deviation value of the first sample historical positioning error is compared with the deviation amplitude in other similar data. If the duty cycle of the deviation magnitude in either direction of the deviation value of the sample is greater than the preset ratio (e.g., 80% of the deviation values are all less than the set threshold value) than the preset deviation threshold value, then the error sample is considered to be a high frequency historical positioning error, i.e., the error sample is a common positioning error. In the specific operation process, the deviation value of the first X-axis deviation data of the first sample historical positioning error and other X-axis deviation data in the sample historical positioning error set can be calculated first, the data duty ratio smaller than a preset deviation threshold value is counted, if the data duty ratio is larger than the preset proportion, the duty ratios of the Y-axis deviation data, the Z-axis deviation data, the pitch angle deviation, the yaw angle deviation and the roll angle deviation are calculated continuously in sequence, and if the data duty ratio is larger than the preset proportion, the first sample historical positioning error is set to be a high-frequency historical positioning error. The above screening process is repeated to screen a series of high frequency historical positional error sets from the sample historical positional error sets, which will be the reference data for the correction target.
And carrying out mean value calculation on the screened high-frequency historical positioning error set to obtain a representative historical positioning error value, wherein the historical positioning error value can reflect the average level of positioning errors in the processing process of the similar die, provides an accurate target value for carrying out secondary position adjustment on the primary correction die through the multi-axis adjusting device, is beneficial to eliminating the historical positioning error more accurately and improves the accuracy of die processing.
In summary, the auxiliary positioning method in the multi-step switching of the precision die provided by the embodiment of the application has the following beneficial effects:
the embodiment of the application realizes the high-precision auxiliary positioning of the precision die in the multi-working-procedure switching process by fusing a dual mechanism of real-time sensing and historical data driving. The real-time offset calculation of the die pose is realized by utilizing the process reference data and the three-dimensional grid model, and the positioning consistency and the compensation precision in the process of die procedure connection are obviously improved by a double-stage position adjustment strategy of current offset correction and historical error compensation, so that the problem of error accumulation caused by insufficient perceptibility of the traditional method is effectively avoided, the adaptability to micro deformation and displacement under complex working conditions is enhanced, and the technical effects of obviously improving the multi-procedure switching positioning precision of the die, enhancing the stability and the reliability of multi-procedure switching and improving the processing quality of the die are finally achieved.
In a second embodiment, as shown in fig. 3, based on the same inventive concept as the previous embodiment, the embodiment of the present application provides an auxiliary positioning system in precision die multi-process switching, the system includes:
the positioning reference construction module 10 is used for collecting process reference information of the lower die in the current process and establishing positioning reference data during die processing.
And the die information acquisition module 20 is used for acquiring information of the die by using the multi-source sensing equipment when the die enters the process switching stage, and constructing a three-dimensional die grid model.
And the offset calculation module 30 is used for carrying out grid comparison and offset calculation by using the three-dimensional mold grid model by taking the positioning reference data as a benchmark to acquire real-time offset information.
Further, the real-time offset information includes a coordinate position offset and an attitude angle deviation.
And the primary correction module 40 is used for controlling the auxiliary positioning device to perform primary position adjustment on the die to obtain a primary correction die aiming at eliminating the real-time offset information.
And the secondary correction module 50 is used for performing secondary position adjustment on the primary correction die through a multi-axis adjusting device according to the historical positioning error of the next adjacent process, completing die position correction and executing die processing of the next adjacent process.
Further, the positioning reference construction module 10 according to the embodiment of the present application is further configured to perform the following steps:
Collecting a datum point, a datum plane, a datum line and a process parameter of a lower die of a current working procedure as process datum information, wherein the process parameter comprises a machining parameter and a clamping parameter, establishing a datum coordinate system and a target attitude angle of the die based on the datum point, the datum plane and the datum line, and fine-tuning the datum coordinate system and the target attitude angle according to the machining parameter and the clamping parameter to obtain positioning reference data.
Further, the die information acquisition module 20 of the embodiment of the present application is further configured to perform the following steps:
After the current procedure is finished, acquiring a mold image set by utilizing an image sensor according to a preset acquisition angle, carrying out omnibearing scanning on the mold by utilizing a laser scanner to obtain a mold point cloud set, extracting contour lines according to the mold image set, fusing and constructing a three-dimensional mold contour, carrying out point cloud fitting on the mold point cloud set by utilizing the three-dimensional mold contour to construct a three-dimensional mold model, and carrying out grid division on the three-dimensional mold model according to a preset grid size to obtain the three-dimensional mold grid model.
Further, the die information acquisition module 20 of the embodiment of the present application is further configured to perform the following steps:
The method comprises the steps of training a convolutional neural network to converge by adopting a sample die image set and a sample die contour set of a similar die to obtain a contour line extractor, respectively extracting contour lines of a plurality of die images in the die image set by utilizing the contour line extractor to obtain a plurality of die contours, and carrying out feature registration and smooth restoration according to the plurality of die contours based on the preset acquisition angle to construct a three-dimensional die contour.
Further, the die information acquisition module 20 of the embodiment of the present application is further configured to perform the following steps:
The method comprises the steps of utilizing the three-dimensional die contour to conduct primary random fitting on the die point cloud set to obtain a first point cloud fitting result, calculating the ratio of the number of point clouds falling into the first point cloud fitting result to the total number of point clouds in the die point cloud set to obtain first fitting precision, utilizing the three-dimensional die contour to conduct secondary random fitting on the die point cloud set to obtain a second point cloud fitting result and second fitting precision, conducting iterative fitting until the preset fitting times are reached, outputting the point cloud fitting result with the maximum fitting precision to be set as an optimal point cloud fitting result, and constructing a three-dimensional die model according to the optimal point cloud fitting result.
Further, the system according to the embodiment of the present application is further configured to perform the following steps:
In the processing process of the current working procedure, monitoring and obtaining a processing parameter sequence and a clamping parameter sequence, carrying out deviation analysis on the processing parameter sequence and the clamping parameter sequence by taking the standard processing parameter and the standard clamping parameter of the current working procedure as references to obtain a processing parameter deviation amplitude sequence and a clamping parameter deviation amplitude sequence, carrying out mean value calculation and fluctuation analysis on the processing parameter deviation amplitude sequence, outputting a processing parameter deviation amplitude mean value and a processing parameter fluctuation coefficient, wherein the fluctuation coefficient is the ratio of standard deviation to mean value, carrying out mean value calculation and fluctuation analysis on the clamping parameter deviation amplitude sequence, outputting a clamping parameter deviation amplitude mean value and a clamping parameter fluctuation coefficient, carrying out weighted calculation on the processing parameter deviation amplitude mean value, the processing parameter fluctuation coefficient, the clamping parameter deviation amplitude mean value and the clamping parameter fluctuation coefficient to obtain the current processing deviation scale, and calculating the ratio of the preset processing deviation scale of the current working procedure to the current processing deviation scale and multiplying the initial grid size to obtain the preset grid size.
Further, the secondary correction module 50 according to the embodiment of the present application is further configured to perform the following steps:
The method comprises the steps of carrying out grid comparison and offset calculation by using a three-dimensional mould grid model, obtaining geometrical deviation of a mould in a current process, expanding the geometrical deviation according to a preset deviation tolerance interval to obtain a geometrical deviation interval, carrying out similar mould processing information retrieval based on industrial Internet by taking the geometrical deviation interval and the next adjacent process as retrieval constraints, determining a sample historical positioning error set, analyzing and determining a historical positioning error according to the sample historical positioning error set, and carrying out secondary position adjustment on the primary correction mould by using a multi-axis adjusting device with the aim of eliminating the historical positioning error.
Further, the secondary correction module 50 according to the embodiment of the present application is further configured to perform the following steps:
The method comprises the steps of obtaining a sample historical positioning error set, randomly selecting a first sample historical positioning error, setting the first sample historical positioning error as a high-frequency historical positioning error if the ratio of the deviation amplitude of any one of the first sample historical positioning error to other similar data in the sample historical positioning error set is smaller than a preset deviation threshold value and is larger than a preset proportion, sequentially screening and obtaining the high-frequency historical positioning error set, and carrying out mean value calculation on the high-frequency historical positioning error set to obtain the historical positioning error.
Through the foregoing detailed description of the auxiliary positioning method in the multi-procedure switching of the precision die, those skilled in the art can clearly know the auxiliary positioning system in the multi-procedure switching of the precision die in the embodiment, and for the system disclosed in the second embodiment, the system has corresponding functional modules and beneficial effects as corresponding to the method disclosed in the first embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The auxiliary positioning method in the multi-step switching of the precision die is characterized by comprising the following steps of:
When the die is processed, collecting process reference information of the die in the current process, and establishing positioning reference data;
When the mould enters a procedure switching stage, information acquisition is carried out on the mould by utilizing multi-source sensing equipment, and a three-dimensional mould grid model is constructed;
Taking the positioning reference data as a benchmark, performing grid comparison and offset calculation by using the three-dimensional mould grid model, and obtaining real-time offset information;
Controlling an auxiliary positioning device to perform primary position adjustment on the die to obtain a primary correction die by taking the real-time offset information as a target;
And according to the historical positioning error of the next adjacent process, performing secondary position adjustment on the primary correction die through a multi-axis adjusting device to finish die position correction, and executing die processing of the next adjacent process.
2. The auxiliary positioning method for multi-step switching of a precision die according to claim 1, wherein collecting process reference information of a die under a current process and establishing positioning reference data comprises:
Collecting a datum point, a datum plane, a datum line and a process parameter of a lower die of a current working procedure as process datum information, wherein the process parameter comprises a processing parameter and a clamping parameter;
Establishing a reference coordinate system and a target attitude angle of the die based on the reference points, the reference surfaces and the reference lines;
And fine tuning the base coordinate system and the target attitude angle according to the processing parameters and the clamping parameters to obtain the positioning reference data.
3. The method for assisting in the multi-station switching of a precision die according to claim 1, wherein the information acquisition is performed on the die by using a multi-source sensing device, and the three-dimensional die grid model is constructed, comprising:
after the current procedure is finished, acquiring a mold image set by utilizing an image sensor to acquire the mold image set according to a preset acquisition angle, and carrying out omnibearing scanning on the mold by utilizing a laser scanner to acquire a mold point cloud set;
Extracting contour lines according to the mold image set, and fusing to construct a three-dimensional mold contour;
performing point cloud fitting on the mold point cloud set by utilizing the three-dimensional mold contour to construct a three-dimensional mold model;
and carrying out grid division on the three-dimensional mould model according to a preset grid size to obtain the three-dimensional mould grid model.
4. The auxiliary positioning method in multi-process switching of a precision die according to claim 3, wherein the contour line extraction is performed according to the die image set, and the three-dimensional die contour is constructed in a fusion manner, comprising:
training a convolutional neural network to converge by adopting a sample mould image set and a sample mould contour set of the similar mould to obtain a contour line extractor;
Respectively extracting contour lines of a plurality of die images in the die image set by using the contour line extractor to obtain a plurality of die contours;
And carrying out feature registration and smooth restoration according to the plurality of mold contours based on the preset acquisition angles, and constructing a three-dimensional mold contour.
5. A method of assisted positioning in precision mold multi-tool switching according to claim 3 wherein performing a point cloud fit on the mold point cloud using the three-dimensional mold profile to construct a three-dimensional mold model comprises:
Performing one-time random fitting on the mold point cloud set by utilizing the three-dimensional mold contour to obtain a first point cloud fitting result, and calculating the ratio of the number of point clouds falling into the first point cloud fitting result to the total number of point clouds in the mold point cloud set to obtain a first fitting precision;
Performing secondary random fitting on the mold point cloud set by utilizing the three-dimensional mold contour again to obtain a second point cloud fitting result and second fitting precision;
And performing iterative fitting until the preset fitting times are reached, outputting a point cloud fitting result with the maximum fitting precision to be set as an optimal point cloud fitting result, and constructing a three-dimensional mold model according to the optimal point cloud fitting result.
6. The auxiliary positioning method in multi-step switching of precision molds according to claim 3, wherein said generating method of the preset mesh size comprises:
In the processing process of the current working procedure, monitoring and acquiring a processing parameter sequence and a clamping parameter sequence;
Performing deviation analysis on the processing parameter sequence and the clamping parameter sequence by taking standard processing parameters and standard clamping parameters of the current working procedure as references to obtain a processing parameter deviation amplitude sequence and a clamping parameter deviation amplitude sequence;
Carrying out mean value calculation and fluctuation analysis on the processing parameter deviation amplitude sequence, and outputting a processing parameter deviation amplitude mean value and a processing parameter fluctuation coefficient, wherein the fluctuation coefficient is the ratio of standard deviation to mean value;
carrying out mean value calculation and fluctuation analysis on the clamping parameter deviation amplitude sequence, and outputting a clamping parameter deviation amplitude mean value and a clamping parameter fluctuation coefficient;
The current processing deviation scale is obtained according to the processing parameter deviation amplitude average value, the processing parameter fluctuation coefficient, the clamping parameter deviation amplitude average value and the clamping parameter fluctuation coefficient through weighted calculation;
calculating the ratio of the preset machining deviation scale of the current working procedure to the current machining deviation scale, and multiplying the ratio by the initial grid size to obtain the preset grid size.
7. The method for assisting positioning in multi-stage switching of a precision die according to claim 1, wherein the real-time offset information includes a coordinate position offset and an attitude angle deviation.
8. The auxiliary positioning method for multi-stage switching of a precision mold according to claim 1, wherein the secondary position adjustment of the primary correction mold by a multi-axis adjusting device according to the historic positioning error of the next adjacent process comprises:
grid comparison and offset calculation are carried out by using the three-dimensional mould grid model, the geometric deviation of the mould in the current working procedure is obtained, and the geometric deviation is expanded according to a preset deviation tolerance interval, so that a geometric deviation interval is obtained;
Taking the geometric deviation interval and the next adjacent procedure as retrieval constraints, carrying out similar mold processing information retrieval based on the industrial Internet, and determining a sample historical positioning error set;
and analyzing and determining the historical positioning error according to the sample historical positioning error set, and performing secondary position adjustment on the primary correction die by using a multi-axis adjusting device with the aim of eliminating the historical positioning error.
9. The method of assisted positioning in precision die multi-shot switching of claim 8, wherein determining a historical positioning error from the sample historical positioning error set analysis comprises:
acquiring a sample historical positioning error set, wherein the sample historical positioning error comprises X-axis offset data, Y-axis offset data, Z-axis offset data, pitch angle deviation, yaw angle deviation and roll angle deviation;
Randomly selecting a first sample historical positioning error, and if the ratio of any deviation value in the first sample historical positioning error to other similar data in the sample historical positioning error set is smaller than a preset deviation threshold value and is larger than a preset proportion, setting the first sample historical positioning error as a high-frequency historical positioning error, and sequentially screening to obtain a high-frequency historical positioning error set;
And carrying out mean value calculation on the high-frequency historical positioning error set to obtain the historical positioning error.
10. An auxiliary positioning system in precision die multi-stage switching, characterized in that the system is used for executing the auxiliary positioning method in precision die multi-stage switching according to any one of claims 1 to 9, comprising:
The positioning reference construction module is used for collecting process reference information of the lower die in the current working procedure and establishing positioning reference data when the die is processed;
the mould information acquisition module is used for acquiring information of the mould by utilizing the multi-source sensing equipment when the mould enters a process switching stage, and constructing a three-dimensional mould grid model;
The offset calculation module is used for carrying out grid comparison and offset calculation by using the three-dimensional mould grid model by taking the positioning reference data as a benchmark to obtain real-time offset information;
the primary correction module is used for controlling the auxiliary positioning device to perform primary position adjustment on the die to obtain a primary correction die with the aim of eliminating the real-time offset information;
And the secondary correction module is used for carrying out secondary position adjustment on the primary correction die through the multi-axis adjusting device according to the historical positioning error of the next adjacent process, finishing die position correction and executing die processing of the next adjacent process.
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| CN120852090A (en) * | 2025-07-23 | 2025-10-28 | 墨点狗智能科技（东莞）有限公司 | Prefabricated component production assistance system for intelligent construction |
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