Feature constraint-free blank two-dimensional contour matching adaptive positioning method and system

Abstract

The invention discloses a feature-constraint-free blank two-dimensional contour matching adaptive positioning method and system. The method comprises the steps that point cloud data preprocessing and feature surface extraction are conducted on a blank and a design model respectively; performing allowance calculation on the preprocessed data, performing coarse and fine registration on the blank measurement point cloud and the design model point cloud, and performing working allowance optimization to realize active allocation of the allowance to obtain the optimal pose of the blank measurement point cloud and the design model point cloud; the two-dimensional contour of a blank is matched and adaptively positioned, and a cross section at a certain height in the Z direction is selected as a registration object of a machine tool coordinate system and a workpiece coordinate system, so that translation offset and rotation offset required by positioning are obtained. According to the method, self-adaptive positioning of the complex blank is achieved, the pose of any blank clamped on the workbench can be accurately determined, the limitation of a traditional method on blank positioning characteristics and rotary or symmetrical parts is broken through, and the method has wide applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of manufacturing engineering and automation, and particularly relates to an adaptive positioning method and system for two-dimensional contour matching of a blank without feature constraints. Background Art

[0002] The blank of an aviation integral structural part is large in size, complex in process features and mostly of thin-wall structure. Before NC machining, the blank of an aviation integral structural part is generally positioned by manually scribing and aligning. A scriber is used to mark the geometric contour of the part on the blank, and the reference is repeatedly adjusted by experience to ensure sufficient allowance for each part and as uniform as possible allowance distribution. Finally, NC machining is carried out according to the reference obtained from scribing. The problems in the machining and positioning process of the blank of an aviation integral structural part are summarized as follows: 1) The reliability of machining and positioning is low, and the scrap rate of blank parts is high. On the one hand, the production tolerance of the blank of an integral structural part obtained by casting or forging is large. Even for blanks of the same batch, their actual shapes are different. It is difficult to directly utilize the self-positioning reference features of the blank, and even some parts do not have geometric features that can be used as a reference. On the other hand, manual scribing can only verify whether the local allowance is sufficient. Once a local material shortage situation in the blank manufacturing process is not detected in advance and the allowance distribution at some positions is insufficient, it will lead to part scrapping.

[0003] 2) The traditional scribing and aligning positioning method is inefficient and time-consuming. There is a certain allowance on each surface to be machined of the blank of an integral structural part, and the design reference cannot be directly utilized. For workpieces with complex process features, in order to ensure sufficient machining allowance for each feature of the blank, it is necessary to manually repeatedly adjust the positioning reference. Moreover, since the size of the integral structural part is large, the entire positioning process will consume a large amount of auxiliary time, seriously affecting the production efficiency.

[0004] 3) The existing machining and positioning methods cannot fully meet the requirements of adaptive positioning. The online positioning method completely based on the machine tool probe is difficult to obtain all the data of the workpiece surface. Therefore, the types of workpieces faced are generally single, and due to the slow measurement speed, the positioning of large workpieces takes a long time. The existing positioning methods based on image processing or three-dimensional scanning mainly focus on the overall matching of blank data, and the combination of allowance optimization and in-machine aligning and positioning links is not tight.

[0005] In the existing published patent, an adaptive positioning method for femtosecond laser micro-hole machining of complex curved surfaces forms a coordinate transformation matrix by using a sensor to measure the positions of several feature points on the complex curved surface and uses it as the machine tool compensation amount for the adaptive positioning method of femtosecond laser micro-hole machining. It uses feature points for positioning and requires that the machined object has easily selectable feature points for analysis. It is not applicable to complex parts without obvious features, while the two-dimensional contour matching adaptive positioning method proposed in this paper has low requirements for the features of the positioned parts.

[0006] There is a publicly disclosed patent, an adaptive positioning method for machining a kind of rotary-like part. By positioning the rotary workpiece, cooperating with the rotation of the main shaft, selecting the key point coordinates and the offset angle, and combining with an algorithm to calculate the specific position coordinates of each machining hole. The research object of this method is relatively single, mainly aiming at rotary-like parts, while the method proposed in this paper has no specific requirements on whether the part is a rotary-like part.

[0007] There is a publicly disclosed patent, a free-form surface positioning method based on the KNN-ICP algorithm. By using the KNN-ICP algorithm as the judgment basis for whether the three-dimensional point cloud is registered, and then using the quaternion method to calculate the translation offset and rotation offset between the ideal model point cloud and the actual model point cloud, and finally obtaining the positioning coordinates. This method directly performs three-dimensional registration on the free-form surface, and the algorithm has a large amount of calculation, while the method proposed in this paper converts the three-dimensional contour registration problem into a two-dimensional contour registration problem, which is easier to implement.

[0008] To sum up, the traditional positioning process of aerospace structural parts has low reliability, low efficiency and high cost. Therefore, before numerical control machining, ensuring the accurate positioning of the blank of the overall structural part has become an urgent problem to be solved. Summary of the Invention

[0009] The purpose of the present invention is to provide an adaptive positioning method and system for two-dimensional contour matching of a blank without feature constraints to solve the above problems.

[0010] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an adaptive positioning method for two-dimensional contour matching of a blank without feature constraints, including: Performing point cloud data preprocessing and feature surface extraction on the blank and the design model respectively; Calculating the machining allowance for the preprocessed data, performing rough and fine registration on the measured point cloud of the blank and the point cloud of the design model, and realizing the active distribution of the machining allowance through machining allowance optimization to obtain the optimal pose of the measured point cloud of the blank and the point cloud of the design model; Utilizing the adaptive positioning of the two-dimensional contour of the blank, by selecting a section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, obtaining the translation offset and rotation offset required for positioning.

[0011] Optionally, the performing point cloud data preprocessing and feature surface extraction on the blank and the design model respectively includes: Adopting a voxel downsampling algorithm and a farthest point downsampling algorithm to streamline the point cloud data, reducing redundant data. After obtaining the streamlined point cloud, calculating and redirecting the normal vector of the point cloud. The method for calculating the normal vector of the point cloud adopts the method for calculating the normal vector of the local surface fitting of the point cloud, using the local kThe normal vector of the neighborhood plane estimates the normal vector of this point and is solved using the principal component analysis method; k The neighborhood refers to any point in the point cloud p i , in space, the points at a distance from point p i nearest k points; for the obtained rough blank measurement point cloud and the designed model point cloud data, the random sample consensus algorithm based on octree cells is used to perform an initial segmentation of the point cloud feature surface, and then the region growing algorithm is used to perform a re-segmentation of the point cloud feature surface.

[0012] Optionally, the calculation of the allowance for the preprocessed data, the rough and fine registration of the rough blank measurement point cloud and the designed model point cloud, and the active allocation of the allowance through the optimization of the machining allowance to obtain the optimal pose of the rough blank measurement point cloud and the designed model point cloud include: After completing the preprocessing and feature surface extraction of the rough blank measurement point cloud and the designed model point cloud, the data such as the normal line information obtained from the preprocessing is used for the allowance calculation, and the corresponding relationship is established for the extracted feature surfaces. The nominal minimum machining allowance is specified for the specific machining surface, and the allowance for the rough blank machining surface is optimized; the SAC-IA rough registration based on the FPFH feature is used to solve the FPFH features of each point of the rough blank measurement point cloud and the designed model point cloud, and the random sample consensus principle is applied to match the feature points to initially align the two point clouds; at the same time, an improved ICP fine registration algorithm is proposed, and the truncated least squares method is introduced to improve the ICP algorithm; the corresponding relationship of the feature surfaces is established for the rough blank measurement point cloud and the designed model point cloud after the feature surface extraction, the allowance constraint is set, the mathematical models of the allowance are established for the plane and other machining surfaces respectively, and the allowance optimization is performed through the constraint variance minimization matching algorithm.

[0013] Optionally, the adaptive positioning using the rough blank two-dimensional contour matching, by selecting the cross-section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, to obtain the translation offset and rotation offset required for positioning, includes: First, according to the relative position relationship between the design coordinate system and the workpiece / programming coordinate system used in the numerical control program, the rough blank measurement point cloud and the part model point cloud are transformed from the design coordinate system to the workpiece / programming coordinate system; then a plane is generated at the specified height or the rough blank point cloud is used, and the rough blank point cloud is intercepted at the specified height to generate the pre-measurement points; then the machine tool runs the measurement macro program to obtain the actual measurement point set of the rough blank part , the actual measurement point set is located in the machine tool coordinate system, and by intercepting the rough blank measurement point cloud that has been transformed to the workpiece / programming coordinate system at the height , the rough blank two-dimensional contour point set is obtained ; at this time, the adaptive positioning problem of the rough blank after the allowance optimization is transformed into the actual measurement point set and the two-dimensional contour point set Regarding the registration problem, the least - squares problem is solved through a two - dimensional ICP algorithm; finally, the rotation matrix and translation vector from the machine tool coordinate system to the workpiece / programming coordinate system are obtained.

[0014] Optionally, the least - squares objective function is as follows:

[0015] The offsets in the X, Y, and Z directions are obtained , , and the rotation angle C around the Z - axis direction. By setting them in the workpiece coordinate system of the machine tool, the adaptive positioning after the allowance optimization can be finally realized.

[0016] Optionally, after the rough - blank NC machining, the machining results after the adaptive positioning of the rough - blank are inspected: Two groups of measuring points on both sides of the wall are obtained respectively by on - machine measurement with a machine tool probe and , the measuring points on the Ⅰ surface The equation of the Ⅰ surface is obtained by the least - squares fitting method of the plane , then for each point q i on the Ⅱ surface, its distance to the Ⅰ surface is calculated d i , d i The average value of is the measured value of the distance between the two surfaces; the least - squares fitting of the plane is solved by the SVD matrix decomposition method; finally, the thickness value d has the following calculation formula:

[0017] The flatness error is obtained by the least - squares method. Through on - machine measurement, the set of coordinates of the points on the plane to be evaluated of the workpiece is obtained. Using the point set, the least - squares plane equation of these N points is fitted by the least - squares method , and this plane passes through the point . For each point P in the point set P, its directed distance to the least - squares plane is calculated d i ; when the angle between the vector p xi v i d and the angle n is less than 180°, d xi > 0, indicating that the point v is on the side where the normal vector n points. When the angle is greater than 180°, d xiLess than 0 indicates that the point v is on the opposite side of the n normal vector; the distance between the measurement points with the largest distance on both sides of the least-squares plane is used as the flatness error, that is, the directed distance d xi The difference between the maximum value and the minimum value:

[0018] Select two cross-sections in the hole that are as far apart as possible, and the centers of the cross-sections are respectively , ,The normal vector of the reference plane obtained by least-squares fitting is n, and the center of the bottom surface O 1, Establish the minimum circumscribed cylinder with n as the axis direction, O The distance from 2 to the axis of the cylinder is the perpendicularity error, O 1 O The included angle between 2 and n is θ, and the final perpendicularity error is:

[0019] In a second aspect, the present invention provides an adaptive positioning system for blank two-dimensional contour matching without feature constraints, including: A data preprocessing module for preprocessing point cloud data and extracting feature surfaces for the blank and the design model respectively; An optimal pose acquisition module for calculating the allowance for the preprocessed data, performing rough and fine registration on the blank measurement point cloud and the design model point cloud, and actively allocating the allowance through machining allowance optimization to obtain the optimal pose of the blank measurement point cloud and the design model point cloud; A positioning output module for using adaptive positioning of the blank two-dimensional contour, and obtaining the translation offset and rotation offset required for positioning by selecting a cross-section at a certain height in the Z direction as the registration object between the machine tool coordinate system and the workpiece coordinate system.

[0020] Optionally, the adaptive positioning using the blank two-dimensional contour, by selecting a cross-section at a certain height in the Z direction as the registration object between the machine tool coordinate system and the workpiece coordinate system, to obtain the translation offset and rotation offset required for positioning, includes: First, according to the relative position relationship between the design coordinate system and the workpiece / programming coordinate system used in the numerical control program, transform the blank measurement point cloud and the part model point cloud from the design coordinate system to the workpiece / programming coordinate system; then generate a plane at the specified height or use the blank point cloud to intercept the blank point cloud at the specified height to generate pre-measurement points; then run the measurement macro program on the machine tool to obtain the measured point set of the blank part contour ,The measured point set is located in the machine tool coordinate system, and by intercepting the blank measurement point cloud that has been transformed to the workpiece / programming coordinate system at the height Obtain the blank two-dimensional contour point set ; At this time, the adaptive positioning problem of the blank after allowance optimization is transformed into the measured point set and the two-dimensional contour point set registration problem, and the least squares problem is solved by the two-dimensional ICP algorithm; finally, the rotation matrix and translation vector from the machine tool coordinate system to the workpiece / programming coordinate system are obtained; The least squares objective function is as follows:

[0021] The offsets in the X, Y, and Z directions are obtained 、 、 and the rotation angle C around the Z-axis direction. Setting it in the workpiece coordinate system of the machine tool can finally realize the adaptive positioning after allowance optimization.

[0022] In a third aspect, the present invention provides a computer device, 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, the steps of the adaptive positioning method for blank two-dimensional contour matching without feature constraints are implemented.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the adaptive positioning method for blank two-dimensional contour matching without feature constraints are implemented.

[0024] Compared with the prior art, the present invention has the following technical effects: The present invention realizes the adaptive positioning of complex blanks, can accurately determine the pose of any blank clamped on the workbench, breaks through the limitations of traditional methods on blank positioning features, rotary or symmetric parts, and has wide applicability.

[0025] 1) Innovatively transform the positioning problem of structural parts into a two-dimensional matching problem between the two-dimensional contour points of the blank in the workpiece coordinate system and the measured contour points in the machine tool coordinate system. This method only needs to process two-dimensional contour registration, reduces the amount of point cloud data required for positioning, and thus greatly shortens the positioning time.

[0026] 2) Adopt the two-dimensional ICP matching algorithm. By setting Z in the three-dimensional algorithm to the same real number for calculation, the conversion from the machine tool coordinate system to the programming coordinate system is realized. This algorithm has been experimentally verified by joint parts and curved surface parts, has the characteristics of fast calculation speed and good registration effect, and can effectively improve the positioning accuracy and efficiency. Description of the Drawings

[0027] Figure 1 It is a flow chart for feature surface segmentation and extraction.

[0028] Figure 2 It is the schematic diagram of adaptive positioning.

[0029] Figure 3 It is the diagram of the two-dimensional contour points and the actually measured contour points of the joint piece.

[0030] Figure 4 It is the matching diagram of the two-dimensional contour points and the actually measured contour points of the joint piece.

[0031] Figure 5 It is the matching diagram of the two-dimensional contour points and the actually measured contour points of the curved surface piece.

[0032] Figure 6 It is the measurement diagram of the rib thickness.

[0033] Figure 7 It is the perpendicularity error diagram. Specific implementation manners

[0034] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0035] The present invention provides a method for adaptive positioning of a blank of a complex structural part, including the following steps: Step 1: Perform point cloud data preprocessing and feature surface extraction on the blank model and the design model respectively.

[0036] The voxel downsampling algorithm and the farthest point downsampling algorithm are used to streamline the point cloud data, reduce redundant data. After obtaining the streamlined point cloud, calculate and redirect the normal vector of the point cloud. The method for calculating the normal vector of the point cloud adopts the calculation method of the normal vector of the local surface fitting of the point cloud. The algorithm assumes that the surface of the point cloud is smooth everywhere, and the normal vector of a certain point can be approximately estimated by the normal vector of the local k-neighborhood plane of the point, and the principal component analysis method is used to solve it. The k-neighborhood refers to, for any point in the point cloud , in space, the set of the k points closest to the point . The quality of the k-neighborhood affects the calculation effect of the normal vector. And for each point in the point cloud, its k-neighborhood needs to be found. For the point cloud data without topological relationship between points, all points except need to be queried each time to determine the closest k points. In this way, the time complexity of each brute-force search is , N is the number of points in the point cloud, and this efficiency is unacceptable. Therefore, a KD tree is constructed to manage the point cloud data. The KD tree is a k-dimensional binary tree, where each node is k-dimensional data. Through the KD tree, the unordered point cloud can be arranged in an orderly manner. The time complexity of constructing the KD tree is , and the time complexity of each retrieval is reduced from to , the specific construction method of the KD tree is not introduced in this article. Instead, the function KDTreeFlann() in the third-party point cloud processing library Open3d is directly called to perform k-nearest neighbor queries. For the obtained rough measurement point cloud of the blank and the point cloud data of the design model, the random sample consensus algorithm based on octree cells is used to initially segment the feature surfaces of the point cloud, and then the region growing algorithm is used to further segment the feature surfaces of the point cloud. Its flowchart is as shown in Figure 1 .

[0037] Step 2: Coarse and fine registration of the rough measurement point cloud of the blank and the point cloud of the design model. Through the optimization of the machining allowance, the active allocation of the allowance is realized, and the optimal poses of the rough measurement point cloud of the blank and the point cloud of the design model are obtained.

[0038] After completing the preprocessing and feature surface extraction of the rough measurement point cloud of the blank and the point cloud of the design model, the data such as the normal information obtained from the preprocessing is used for the allowance calculation, and the corresponding relationships are established for the extracted feature surfaces. The nominal minimum machining allowance is specified for specific machining surfaces, and the machining allowance of the rough blank surfaces is optimized. SAC-IA coarse registration based on FPFH features. The FPFH features of each point of the rough measurement point cloud of the blank and the point cloud of the design model are solved, and the random sample consensus principle is applied to match the feature points to preliminarily align the two point clouds. An improved ICP fine registration algorithm is proposed. The truncated least squares method is introduced to improve the traditional ICP algorithm. It is verified that the improved ICP fine registration algorithm further aligns the two point clouds on the basis of the coarse registration, and is superior to the traditional ICP algorithm in terms of convergence speed and registration accuracy. The registration accuracy of the joint part point cloud is improved by 52.42%. A method for optimizing the machining allowance by minimizing the constrained variance matching is proposed. The corresponding relationships of the feature surfaces are established for the rough measurement point cloud of the blank and the point cloud of the design model after the feature surface extraction, the allowance constraints are set, the mathematical models of the allowance are established for the plane and other machining surfaces respectively, and the allowance is optimized through the constrained variance minimization matching algorithm.

[0039] Step 3: Use the adaptive positioning method for rough blank two-dimensional contour matching. By selecting the section at a certain height in the Z direction as the registration object between the machine tool coordinate system and the workpiece coordinate system, the translation offset and rotation offset required for positioning are obtained.

[0040] First, according to the design coordinate system and the workpiece / programming coordinate system used in the generation of the numerical control program 's relative position relationship, the rough measurement point cloud of the blank and the point cloud of the part model are transformed from the design coordinate system to the workpiece / programming coordinate system .

[0041] Then, use SolidWorks or other CAD software to generate a plane at the specified height or use the rough blank point cloud, and the rough blank point cloud at the specified height Intercept and generate predicted measurement points.

[0042] Then run the measurement macro program on the machine tool to obtain the actual measurement point set of the rough part contour , the actual measurement point set is located in the machine tool coordinate system, and by intercepting the rough measurement point cloud that has been transformed into the workpiece / programming coordinate system at a certain height , a two-dimensional contour point set of the rough can be obtained . At this time, the adaptive positioning problem of the rough after allowance optimization is transformed into the registration problem between the actual measurement point set and the two-dimensional contour point set . Solve the least squares problem through the two-dimensional ICP algorithm. The least squares objective function is as follows; finally, solve for the rotation matrix and translation vector from the machine tool coordinate system to the workpiece / programming coordinate system, and use the following formula to obtain the offsets in the X, Y, and Z directions , , and the rotation angle C around the Z axis direction, and setting it in the workpiece coordinate system of the machine tool can finally achieve the adaptive positioning after allowance optimization.

[0043]

[0044] Experimental verification was carried out to verify the effect of the adaptive positioning method based on rough two-dimensional contour matching.

[0045] First, extract the two-dimensional contour of the joint part as Figure 3 (a) shows, a total of 3822 points, located in the workpiece coordinate system. Then discretize to obtain 30 predicted measurement points, rotate the predicted measurement points 30° clockwise around the Z axis, translate 2 mm in the negative X direction, and translate 2 mm in the negative Y direction, as the actual measurement points of the joint part contour in the machine tool coordinate system. If the algorithm principle is correct, the two-dimensional contour points will successfully match with the actual measurement points of the contour, and the offset set in advance for the predicted measurement points will be inversely calculated.

[0046] After solving through the two-dimensional ICP algorithm, the relative position change between the two is as Figure 4 shown. It can be seen that the two-dimensional contour of the joint part matches with the actual measurement points of the contour, and the rotation matrix is obtained, and the rotation around Z axis is 30.000°, the translation vector is obtained, and the offsets in the X and Y directions are inversely calculated to be 1.999 and 2.000, which are basically consistent with the initially set offsets.

[0047]

[0048] Verification was carried out on the curved surface part without available positioning features, and the rotation matrix , inversely calculate the rotation angle of 26.000° around the Z axis and the translation vector , and the matching effect is as Figure 5 shown.

[0049]

[0050] Step 4: Study the calculation method of partial feature dimensions and geometric tolerances of the workpiece after CNC machining of the blank, and verify the machining results after adaptive positioning.

[0051] After the preliminary preparation and adaptive positioning of the aviation blank, CNC machining is started. The machine tool probe is used to measure the feature dimensions and geometric tolerance parameters after some machining processes on the machine, which can timely verify the adaptive positioning effect. According to the different feature shapes to which the dimensions belong, the corresponding dimension solving calculation methods are also different. According to the dimension calculation method, the measurement of common dimensions of the workpiece can be divided into the dimension measurement based on plane elements and the dimension measurement based on circular hole elements. The dimension measurement based on plane elements is outlined.

[0052] Many features in aviation complex parts are composed of planes, such as thin walls, vertical ribs, webs, etc. The calculation methods of dimensions such as wall height, wall thickness, and rib thickness based on these features are the same. First, the reference plane is fitted, then the point-plane distance is solved, and finally the dimension value is comprehensively calculated.

[0053] Taking the calculation of rib thickness as an example, as Figure 6 shown, two groups of measuring points on both sides of the wall are obtained respectively by in-machine measurement with the probe and . The measuring points on the I surface obtain the equation of the I surface through the least squares fitting method of the plane. Then, the distance q i from each point d i on the II surface to the I surface is calculated. d i The average value of

[0054] is the measured value of the distance between the two surfaces. The least squares fitting of the plane is solved by the SVD matrix decomposition method. The distance from any point d 0 in space to the plane is shown in Equation (2).

[0055]

[0056] Finally, the thickness value d is obtained as shown in Equation (3).

[0057]

[0058] For the calculation of the geometric tolerance of a workpiece, the in-machine measurement data calculation methods for flatness and perpendicularity are studied here. The evaluation methods for flatness error include the three-far-point method, the minimum zone evaluation method, the maximum straightness evaluation method, and the least squares method. The least squares method is adopted in this paper. By in-machine measurement, the points on the plane of the workpiece to be evaluated are obtained The set of coordinates, and the least squares plane equation of these N points is fitted using the least squares method . This plane must pass through the point . For each point P in the point set P i , the directed distance d xi from it to the least squares plane is calculated

[0059]

[0060] When the angle between the vector p i v and the included angle n is less than 180°, d xi is greater than 0, indicating that the point v is on the side where the normal vector n points. When the included angle is greater than 180°, d xi is less than 0, indicating that the point v is on the opposite side of the normal vector n. The distance between the measurement points with the largest distance on both sides of the least squares plane is used as the flatness error, that is, the difference between the maximum value and the minimum value of the directed distance d xi :

[0061] Perpendicularity represents the variation of elements such as lines, planes, and axes in the vertical direction relative to the datum. Here, the perpendicularity of the axis is discussed and defined as the diameter of the smallest cylinder that completely encloses the axis and is perpendicular to the datum, as shown in Figure 7 .

[0062] Two sections as far apart as possible are selected inside the hole, and the centers of the sections are , respectively. The normal vector of the reference plane obtained by least squares fitting is n. A minimum circumscribed cylinder is established with the center of the bottom surface O 1 and the direction of n as the axis direction. The distance from O 2 to the axis of this cylinder is the perpendicularity error O 1 O The included angle θ between 2 and n is:

[0063] The perpendicularity error is:

[0064] In another embodiment of the present invention, there is provided Adaptive positioning system for two-dimensional contour matching of blank without feature constraint , capable of implementing the above-mentioned Adaptive positioning for two-dimensional contour matching of blank without feature constraint method. Specifically, the system includes: A data preprocessing module, which is used to perform point cloud data preprocessing and feature surface extraction on the blank and the design model respectively; An optimal pose acquisition module, which is used to calculate the allowance for the preprocessed data, perform rough and fine registration on the measured point cloud of the blank and the point cloud of the design model, and realize the active allocation of the allowance through machining allowance optimization, so as to obtain the optimal pose of the measured point cloud of the blank and the point cloud of the design model; A positioning output module, which is used to utilize the adaptive positioning of the two-dimensional contour of the blank, and obtain the translation offset and rotation offset required for positioning by selecting a section at a certain height in the Z direction as the registration object between the machine tool coordinate system and the workpiece coordinate system.

[0065] In the embodiments of the present invention, the division of modules is illustrative, and it is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present invention, each functional module may be integrated in a processor, may also exist physically alone, or two or more modules may be integrated in one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0066] In another embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of the adaptive positioning method for the two-dimensional profile matching of a featureless blank.

[0067] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the adaptive positioning method for the two-dimensional profile matching of a featureless blank in the above embodiments.

[0068] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0069] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0070] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. An adaptive positioning method for rough blank two-dimensional contour matching without feature constraints, characterized in that including: Preprocessing the point cloud data and extracting the feature surfaces for the blank and the design model respectively; Calculating the machining allowance for the preprocessed data, performing rough and fine registration on the measured point cloud of the blank and the point cloud of the design model, actively allocating the machining allowance through machining allowance optimization, and obtaining the optimal poses of the measured point cloud of the blank and the point cloud of the design model; Using the adaptive positioning of the two-dimensional contour matching of the blank, selecting a section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, and obtaining the translational offset and rotational offset required for positioning.

2. The adaptive positioning method for blank two-dimensional contour matching without feature constraints according to claim 1, wherein The preprocessing of the point cloud data and the extraction of the feature surfaces for the blank and the design model respectively include: The voxel downsampling algorithm and the farthest point downsampling algorithm are used to streamline the point cloud data, reduce redundant data. After obtaining the streamlined point cloud, the normal vector of the point cloud is calculated and redirected. The method for calculating the normal vector of the point cloud adopts the method of calculating the normal vector of the local surface of the point cloud, and the normal vector of a certain point is estimated by the normal vector of the local k neighborhood plane of the point, and the principal component analysis method is used to solve it; k The neighborhood refers to any point in the point cloud p i , and in space, the points p i closest to k form a set; for the obtained rough measurement point cloud and the designed model point cloud data, the random sample consensus algorithm based on octree cells is used to initially segment the feature surface of the point cloud, and then the region growing algorithm is used to re-segment the feature surface of the point cloud.

3. The adaptive positioning method for matching the two-dimensional contour of a blank without feature constraints according to claim 1, characterized in that The calculation of the machining allowance for the preprocessed data, the rough and fine registration of the measured point cloud of the blank and the point cloud of the design model, and the active allocation of the machining allowance through machining allowance optimization to obtain the optimal poses of the measured point cloud of the blank and the point cloud of the design model include: After completing the preprocessing of the point cloud data and the extraction of the feature surfaces for the measured point cloud of the blank and the point cloud of the design model, using the data such as the normal information obtained from the preprocessing for the machining allowance calculation, establishing corresponding relationships for the extracted feature surfaces, specifying the nominal minimum machining allowance for specific machining surfaces, and optimizing the machining allowance for the blank machining surfaces; performing rough registration based on the SAC-IA of the FPFH feature, solving the FPFH features of each point of the measured point cloud of the blank and the point cloud of the design model, applying the principle of random sample consensus to match the feature points, and initially aligning the two point clouds; at the same time, proposing an improved ICP fine registration algorithm, introducing the truncated least squares method to improve the ICP algorithm; establishing a corresponding relationship between the feature surfaces for the measured point cloud of the blank and the point cloud of the design model after the extraction of the feature surfaces, setting machining allowance constraints, establishing mathematical models for the machining allowance for the plane and other machining surfaces respectively, and optimizing the machining allowance through the constraint variance minimization matching algorithm.

4. The adaptive positioning method for blank two-dimensional contour matching without feature constraints according to claim 1, characterized in that, The use of the adaptive positioning of the two-dimensional contour matching of the blank, selecting a section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, and obtaining the translational offset and rotational offset required for positioning includes: First, based on the design coordinate system and the numerical control program, generate the relative position relationship between the workpiece / programming coordinate system used, and transform the blank measurement point cloud and the part model point cloud from the design coordinate system to the workpiece / programming coordinate system; then generate a plane at a specified height or use the blank point cloud to intercept the blank point cloud at the specified height to generate pre-measurement points; then run the measurement macro program on the machine tool to obtain the actual measurement point set of the blank part contour. , the actual measurement point set is located in the machine tool coordinate system, and by intercepting the blank measurement point cloud that has been transformed to the workpiece / programming coordinate system at height , obtain the two-dimensional contour point set of the blank. ; at this time, the adaptive positioning problem of the blank after allowance optimization is transformed into the registration problem between the actual measurement point set and the two-dimensional contour point set , and solve the least squares problem through the two-dimensional ICP algorithm; finally, solve for the rotation matrix and translation vector from the machine tool coordinate system to the workpiece / programming coordinate system.

5. The adaptive positioning method for blank two-dimensional contour matching without feature constraints according to claim 1, characterized in that The least squares objective function is as follows: In the formula, R is the rotation matrix for the coordinate transformation from the machine tool coordinate system to the workpiece coordinate system; t is the translation vector for the coordinate transformation from the machine tool coordinate system to the workpiece coordinate system; p mi is the i-th point on the actually measured blank contour; p ci is p mi the closest point corresponding to it on the blank two-dimensional contour point set; Obtain the offsets in the X, Y, and Z directions , , and the rotation angle C about the Z-axis direction. Setting them in the workpiece coordinate system of the machine tool can finally achieve the adaptive positioning after the allowance optimization.

6. The adaptive positioning method for blank two-dimensional contour matching without feature constraints according to claim 1, wherein After numerically controlling the machining of the blank, inspecting the machining result after the adaptive positioning of the blank: Two sets of measuring points on both sides of the wall are obtained respectively by on-machine measurement with a machine tool probe and , the measuring points of surface Ⅰ The equation of surface Ⅰ is obtained by the least squares fitting method of the plane , then for each point on surface Ⅱ q i calculate its distance to surface Ⅰ d i , d i The average value is the measured value of the distance between the two surfaces; The least squares fitting of the plane is solved by the SVD matrix decomposition method; Finally, the thickness value d has the following calculation formula: where d is the finally obtained thickness value; N2 is the number of measurement points on the II surface; d i is the distance from each point q i on the II surface to the corresponding point on the I surface; The flatness error is obtained by the least squares method through on-machine measurement of the points on the plane of the workpiece to be evaluated. The set of coordinates of the points is used to fit the least squares plane equation of these N points by the least squares method. This plane passes through the point For each point P i in the point set P, the directed distance from it to the least squares plane is calculated. d xi ; When the angle between the vector p i v and the angle n is less than 180°, d xi is greater than 0, indicating that the point v is on the side where the normal vector n points. When the angle is greater than 180°, d xi is less than 0, indicating that the point v is on the opposite side of the normal vector n; The distance between the measurement points with the largest distance on both sides of the least squares plane is used as the flatness error, that is, the difference between the maximum and minimum values of the directed distance d xi : where flatness is the flatness error value; d xmax is the directed distance d xi the maximum value of; d xmin is the directed distance d xi the minimum value of; Select two cross-sections in the hole that are as far apart as possible, with the centers of the cross-sections being , respectively. The normal vector of the reference plane obtained by least squares fitting is n. Taking the center of the bottom surface O 1 and n as the axis direction, establish the minimum circumscribed cylinder. O The distance from O 1 O 2 to the axis of this cylinder is the perpendicularity error. The included angle between 1 O 2 and n is θ, and the final perpendicularity error is: where perpendicularity is the perpendicularity error value; θ is O 1 O the included angle between 2 and n.

7. An adaptive positioning system for rough blank two-dimensional contour matching without feature constraints, characterized in that including: A data preprocessing module for preprocessing the point cloud data and extracting the feature surfaces for the blank and the design model respectively; An optimal pose acquisition module for calculating the machining allowance for the preprocessed data, performing rough and fine registration on the measured point cloud of the blank and the point cloud of the design model, actively allocating the machining allowance through machining allowance optimization, and obtaining the optimal poses of the measured point cloud of the blank and the point cloud of the design model; A positioning output module for using the adaptive positioning of the two-dimensional contour matching of the blank, selecting a section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, and obtaining the translational offset and rotational offset required for positioning.

8. The adaptive positioning system for matching the two-dimensional contour of a blank without feature constraints according to claim 7, wherein The use of the adaptive positioning of the two-dimensional contour matching of the blank, selecting a section at a certain height in the Z direction as the registration object of the machine tool coordinate system and the workpiece coordinate system, and obtaining the translational offset and rotational offset required for positioning includes: First, according to the relative position relationship between the design coordinate system and the workpiece / programming coordinate system used in the numerical control program, the blank measurement point cloud and the part model point cloud are transformed from the design coordinate system to the workpiece / programming coordinate system; then, a plane is generated at the specified height or the blank point cloud is used to intercept the blank point cloud at the specified height to generate pre-measurement points; and then the measurement macro program is run on the machine tool to obtain the measured point set of the blank part contour. , the measured point set is located in the machine tool coordinate system, and by intercepting the blank measurement point cloud that has been transformed to the workpiece / programming coordinate system at the height , a blank two-dimensional contour point set is obtained. ; at this time, the adaptive positioning problem of the blank after the allowance optimization is transformed into the registration problem between the measured point set and the two-dimensional contour point set , and the least squares problem is solved by the two-dimensional ICP algorithm; finally, the rotation matrix and translation vector from the machine tool coordinate system to the workpiece / programming coordinate system are obtained. The least squares objective function is as follows: where, R is the rotation matrix for the coordinate transformation from the machine tool coordinate system to the workpiece coordinate system; t is the translation vector for the coordinate transformation from the machine tool coordinate system to the workpiece coordinate system; p mi is the i-th point on the actually measured blank contour; p ci is p mi the closest point corresponding to the point on the two-dimensional contour point set of the blank; Obtain the offsets in the X, Y, and Z directions , , and the rotation angle C about the Z-axis direction. Setting them in the workpiece coordinate system of the machine tool can finally achieve the adaptive positioning after the allowance optimization.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the adaptive positioning method for two-dimensional contour matching of a blank without feature constraints as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the steps of the adaptive positioning method for two-dimensional contour matching of a blank without feature constraints as described in any one of claims 1 to 7 are implemented.

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