Method for optimizing measurement path of three-coordinate measuring machine based on digital twinning and related equipment

Abstract

The application relates to the technical field of measurement path planning, and provides a three-coordinate measuring machine measurement path optimization method based on digital twinning and related equipment. The method comprises the following steps: establishing a digital twinning model of a measured part and a three-coordinate measuring machine; in the digital twinning model, the measurement region is classified according to the measurement accuracy of the measured part, and a plurality of region types are obtained; an initial measurement viewpoint set is obtained, and an effective measurement viewpoint set is screened from the initial measurement viewpoint set according to the region types; based on the effective measurement viewpoint set, an ant colony algorithm is used to solve an initial measurement path of the three-coordinate measuring machine by taking path distance, measurement accuracy standard rate and motion energy consumption as optimization targets; and the initial measurement path is optimized according to actual motion parameters of a measuring head of the three-coordinate measuring machine, so that an optimized measurement path is obtained. The application can shorten the viewpoint screening period, improve the viewpoint screening efficiency, and further improve the path planning efficiency, so as to meet the real-time measurement requirement.

Description

Technical Field

[0001] This application relates to the field of measurement path planning technology, and in particular to a method and related equipment for optimizing the measurement path of a coordinate measuring machine based on digital twins. Background Technology

[0002] Measurement path planning involves planning an ordered set of measurement viewpoints based on measurement task requirements, the geometric model of the part being measured, and sensor parameters to obtain complete and accurate surface measurement data. During the measurement process, the measurement viewpoint directly determines the measurement position and orientation of the 3D scanner, and the accuracy of the obtained 3D data is significantly affected by the measurement viewpoint.

[0003] However, existing measurement viewpoint set selection schemes based on ray tracing algorithms, when faced with complex parts, adopt an indiscriminate traversal mode, performing the same error calculation on all initial measurement viewpoints. This results in a large number of redundant viewpoints consuming excessive computing resources, prolonging the viewpoint selection cycle, and leading to low viewpoint selection efficiency. Consequently, path planning efficiency is low, making it difficult to meet real-time measurement requirements. Summary of the Invention

[0004] In view of the above, it is necessary to propose a measurement path optimization method and related equipment for a coordinate measuring machine based on digital twins, which can shorten the viewpoint selection cycle, improve the viewpoint selection efficiency, and thus improve the path planning efficiency, thereby meeting the real-time measurement requirements.

[0005] The first aspect of this application provides a method for optimizing the measurement path of a coordinate measuring machine based on digital twins, the method comprising:

[0006] Establish a digital twin model of the part to be measured and the coordinate measuring machine;

[0007] In the digital twin model, the measurement area is classified according to the measurement accuracy of the measured part to obtain multiple area types;

[0008] Obtain an initial set of measurement viewpoints, and filter out a valid set of measurement viewpoints from the initial set of measurement viewpoints according to the region type;

[0009] Based on the effective set of measurement viewpoints, the initial measurement path of the coordinate measuring machine is obtained by using the ant colony algorithm with path distance, measurement accuracy achievement rate and motion energy consumption as optimization objectives.

[0010] The initial measurement path is optimized based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path.

[0011] In an optional embodiment, the step of filtering the initial set of measurement viewpoints according to the region type to obtain a valid set of measurement viewpoints includes:

[0012] The sampling ratio is determined based on the region type.

[0013] According to the sampling ratio, a candidate measurement viewpoint set is obtained by sampling from the initial measurement viewpoint set of the corresponding region.

[0014] Calculate the error between the measurement data under the candidate measurement viewpoint set and the measurement data obtained from simulation;

[0015] Determine whether the error is greater than a preset error threshold;

[0016] If the error is greater than the preset error threshold, the measurement viewpoints in the candidate measurement viewpoint set are removed to obtain the effective measurement viewpoint set.

[0017] In an optional embodiment, the initial measurement path of the coordinate measuring machine is obtained by using an ant colony algorithm based on the effective measurement viewpoint set, with path distance, measurement accuracy achievement rate, and motion energy consumption as optimization objectives. This includes:

[0018] Initialize the parameters of the ant colony algorithm;

[0019] In the digital twin model, each viewpoint in the effective measurement viewpoint set is used as a node of the ant colony algorithm to establish an initial pheromone matrix and a heuristic information matrix;

[0020] Construct a complete measurement path based on pheromone concentration and heuristic information;

[0021] For each constructed measurement path, the target values ​​are calculated with path distance, measurement accuracy compliance rate, and motion energy consumption as optimization objectives.

[0022] Based on the target value of each measurement path, update the pheromone concentration of each edge on the measurement path. The measurement path with the better target value will obtain a larger pheromone increment.

[0023] When the iteration meets the stopping requirement, the current optimal path is output as the initial measurement path.

[0024] In an optional embodiment, optimizing the initial measurement path based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path includes:

[0025] For each adjacent measuring point in the initial measurement path, the angle conversion time and the pause time are dynamically calculated based on the actual motion parameters of the probe using a pre-established correlation model between the probe angle conversion time and the conversion amplitude, and between the pause time and the change amplitude of the motion direction.

[0026] A time-cost adjacency matrix is ​​generated based on the angle conversion time and the pause time.

[0027] Based on the time-cost adjacency matrix, the initial measurement path is optimized using the ant colony algorithm to obtain an optimized measurement path.

[0028] In an optional embodiment, the correlation model between the probe angle conversion time and conversion amplitude, and between the pause time and the change in motion direction amplitude, is established in the following manner:

[0029] By experimentally collecting the actual conversion time of the probe under different angle conversion amplitudes, a functional relationship between angle conversion time t1 and angle conversion amplitude Δθ is established: t1=a×Δθ+b, where a and b are coefficients obtained by fitting experimental data;

[0030] Analyze the correlation between the magnitude of the change in motion direction and the pause time, and establish a functional relationship between the pause time t2 and the magnitude of the change in motion direction Δα: t2=c×Δα2+d×Δα+e, where c, d, and e are fitting coefficients.

[0031] In an optional embodiment, establishing a digital twin model of the part under test and the coordinate measuring machine includes:

[0032] Geometric tolerance and material property data are obtained from the three-dimensional CAD model of the part under test;

[0033] Obtain the structural parameters, kinematic parameters, and sensor performance parameters of the coordinate measuring machine;

[0034] Based on the 3D CAD model, the geometric tolerances, the material property data, the structural parameters, the kinematic parameters, and the sensor performance parameters, an integrated digital twin model is constructed on the digital twin platform.

[0035] In an optional embodiment, the coordinate measuring machine measurement path optimization method based on digital twin further includes:

[0036] Acquire the status data of the coordinate measuring machine during the actual measurement process;

[0037] The parameters of the digital twin model are corrected based on the state data and the simulation data of the digital twin model;

[0038] The measurement path is iteratively optimized based on the revised digital twin model;

[0039] After the iterative optimization is completed, the target measurement path is output.

[0040] In an optional embodiment, the coordinate measuring machine measurement path optimization method based on digital twin further includes:

[0041] The coordinate measuring machine is controlled to measure the part under test based on the target measurement path.

[0042] A second aspect of this application provides a coordinate measuring machine (CMM) comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement all or part of the steps of the digital twin-based CMM measurement path optimization method.

[0043] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements all or part of the steps of the digital twin-based coordinate measuring machine measurement path optimization method.

[0044] This application establishes a digital twin model of the measured part and the coordinate measuring machine (CMM), simulating the real measurement process in a virtual environment. This avoids equipment wear and time waste during actual debugging and reduces the trial-and-error cost of measurement path planning. By classifying the measurement area in the digital twin model according to the measurement accuracy of the measured part, multiple area types are obtained. Based on these area types, an effective set of measurement viewpoints is determined, breaking the traditional indiscriminate screening model and achieving differentiated screening based on area importance. This significantly improves the efficiency of viewpoint screening while ensuring measurement accuracy. Using the ant colony algorithm based on the effective set of measurement viewpoints, the initial measurement path of the CMM is obtained with path distance, measurement accuracy achievement rate, and motion energy consumption as optimization objectives. This initial measurement path balances distance, accuracy, and energy consumption, meeting various requirements during the measurement process. Finally, the initial measurement path is optimized based on the actual motion parameters of the CMM probe, resulting in an optimized measurement path that better reflects the actual movement of the probe. This changes the traditional fixed-time calculation model, further reducing time loss in actual measurement and ensuring path planning efficiency, thus meeting the requirements of real-time measurement. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1A flowchart illustrating a measurement path optimization method for a coordinate measuring machine based on digital twin, provided for embodiments of this application;

[0047] Figure 2 A functional block diagram of a coordinate measuring machine measurement path optimization device based on digital twin provided in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the structure of a coordinate measuring machine provided in an embodiment of this application. Detailed Implementation

[0049] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing an embodiment in one alternative implementation and is not intended to be limiting of the application.

[0051] Measurement path planning, as a core component of coordinate measuring machine (CMM) technology, must accurately match the measurement task requirements (such as the inspection standards for dimensional and geometric tolerances), adapt to the geometric model characteristics of the measured part (such as surface curvature, structural complexity, and distribution of hidden areas), and also consider the performance parameters of the sensor (such as field of view, depth of field, measurement accuracy curve, and working distance). Its core objective is to plan an ordered set of measurement viewpoints to ensure that the CMM can completely cover the surface to be measured through a reasonable combination of positions and orientations, and that the acquired data meets accuracy requirements. In this process, the role of the measurement viewpoint is crucial. It directly defines the spatial coordinates and orientation parameters of the CMM (such as probe tilt angle and sensor orientation). The position of the CMM determines whether it can effectively capture the key features of the part, while the orientation affects core measurement conditions such as light projection angle and data acquisition resolution. Ultimately, the accuracy of the obtained 3D data is highly correlated with the rationality of the measurement viewpoint. An unreasonable viewpoint may lead to data loss, blurred edges, excessive errors, or even prevent the complete measurement of complex surfaces.

[0052] However, existing measurement viewpoint selection schemes based on ray tracing algorithms suffer from significant efficiency bottlenecks when dealing with complex parts, making them unsuitable for real-time measurement scenarios. These schemes employ an indiscriminate traversal approach when selecting the initial measurement viewpoint set, performing the same error calculation process on all initial viewpoints: simulating the measurement process using ray tracing, comparing the actual measurement data with the simulation data at each viewpoint, and determining the viewpoint's validity based on an error threshold. However, complex parts (such as automotive engine blocks and precision aerospace components) often have a large number of initial measurement viewpoints, and the measurement requirements for different areas vary significantly. Some critical areas (such as sealing surfaces and assembly holes) require high-density viewpoints to ensure accuracy, while simple, non-critical planar areas do not require excessive redundant viewpoints. Existing schemes allocate equal computational resources to redundant viewpoints in all areas, resulting in excessive processor resources and time being consumed by ineffective computation in many non-critical areas. This not only prolongs the viewpoint selection cycle but may also cause measurement interruptions due to computational delays, making them unsuitable for applications with high timeliness requirements, such as real-time production line inspection and rapid quality verification.

[0053] To address the technical problems of long viewpoint selection cycles, low viewpoint selection efficiency, and inability to meet real-time requirements, this application provides a method and related equipment for optimizing the measurement path of a coordinate measuring machine based on digital twins.

[0054] Figure 1 This is a flowchart illustrating a method for optimizing the measurement path of a coordinate measuring machine based on digital twins, as provided in an embodiment of this application. The method specifically includes the following steps.

[0055] S11. Establish a digital twin model of the part to be measured and the coordinate measuring machine.

[0056] The part being measured refers to a physical component whose dimensions, geometric properties such as form and position tolerances need to be checked by a coordinate measuring machine to verify whether it meets the design requirements, such as automobile engine blocks, gearbox housings, turbine blades, gears, and bearing housings.

[0057] A coordinate measuring machine (CMM) is a high-precision measuring device that uses a moving probe to accurately acquire the three-dimensional coordinates of points on the surface of a part, thereby calculating the part's dimensions and geometric tolerances. A CMM consists of a mechanical structure (such as x / y / z three-axis guideways), a probe system (such as the PH10T rotatable tilting head + optical sensor), a control system (driving the movement of each axis), and data processing software.

[0058] When constructing an integrated digital twin model of the part under test and the coordinate measuring machine (CMM), the measurement datum of the virtual part under test (e.g., the bottom surface) needs to be aligned with the worktable datum of the virtual CMM (e.g., the marble table surface of the CMM), consistent with the clamping and positioning of the part during actual measurement. Measurement points to be measured (e.g., bolt hole centers, surface vertices) should be marked on the virtual part under test. The probe of the virtual CMM must be able to move accurately to these measurement points, and the probe posture (e.g., A-axis / B-axis swing angle) must meet the visibility requirements of actual measurement; for example, the probe cannot be facing away from the measurement points. During virtual measurement, after the virtual probe contacts or scans the measurement points of the virtual part, it needs to output virtual measurement data consistent with the actual measurement and compare it with the design values ​​of the virtual part to verify the measurement accuracy. Virtual measurement data can include coordinate values, dimensional deviations, etc.

[0059] In an optional embodiment, establishing a digital twin model of the part under test and the coordinate measuring machine includes:

[0060] Geometric tolerance and material property data are obtained from the three-dimensional CAD model of the part under test;

[0061] Obtain the structural parameters, kinematic parameters, and sensor performance parameters of the coordinate measuring machine;

[0062] Based on the 3D CAD model, the geometric tolerances, the material property data, the structural parameters, the kinematic parameters, and the sensor performance parameters, an integrated digital twin model is constructed on the digital twin platform.

[0063] A 3D CAD model is a three-dimensional digital model of the part being measured, which can fully present the part's geometry, dimensions, and other information. Geometric tolerances are used to limit the allowable range of errors in the geometric features (such as shape, orientation, and position) of a part, such as roundness tolerance, parallelism tolerance, and positional tolerance. Material property data are data used to describe the physical and chemical properties of the materials used in the part being measured, such as density, coefficient of thermal expansion, modulus of elasticity, and hardness.

[0064] Structural parameters are parameters related to the structure of the coordinate measuring machine (CMM), such as the travel range of each axis, guide rail accuracy, probe type and size, etc. Kinematic parameters describe the motion characteristics of each axis of the CMM, such as maximum speed, acceleration, positioning accuracy, and repeatability. Sensor performance parameters include field of view, depth of field, measurement resolution, and measurement error range.

[0065] Geometric tolerances and material properties are extracted from the 3D CAD model file of the part being tested. For example, the roundness tolerance of the bearing hole in the gearbox housing is 0.006 mm, and the positional tolerance is 0.01 mm; the material density of the part is 7.2 g / cm³, and the coefficient of thermal expansion is 11 × 10⁻⁻⁻⁶. 6 / ℃, elastic modulus 1.8×10 5 MPa.

[0066] Obtain structural parameters, kinematic parameters, and sensor performance parameters from the coordinate measuring machine's technical manual, such as x-axis travel 1000mm, y-axis 800mm, z-axis 600mm, probe model PH10T; maximum x / y / z-axis speed 600mm / s, acceleration 1200mm / s², positioning accuracy ±0.0025mm, repeatability ±0.001mm; optical sensor field of view 150×150mm, depth of field 30mm, measurement resolution 0.0001mm, measurement error range ±0.0015mm.

[0067] Import the 3D CAD model of the part to be measured into a digital twin platform, such as Unity, Unreal Engine, or TwinBuilder, and associate the geometric tolerances and material properties of the part with the model, such as marking the roundness tolerance at the bearing hole. Then, import the 3D model of the coordinate measuring machine (CMM) and input the structural and kinematic parameters of the CMM into the digital twin platform to build the kinematic model of the CMM (setting the motion range, speed, acceleration, etc. of each axis). Finally, integrate the optical sensor model and configure the sensor performance parameters into the model to simulate the sensor's measurement function. Through the platform's collaborative functions, associate the part model and the CMM model to build an integrated digital twin model.

[0068] In the above optional implementations, by acquiring the 3D CAD model, geometric tolerances, and material property data of the part being measured, an accurate geometric and physical basis for the digital twin model is provided. This ensures that the model can truly reflect the shape, dimensional accuracy requirements, and physical characteristics of the part, laying the foundation for the accuracy of subsequent virtual measurement and path planning. By acquiring the structural parameters, kinematic parameters, and sensor performance parameters of the coordinate measuring machine (CMM), key technical parameters of the CMM are comprehensively collected, providing data support for constructing a CMM model and sensor model that conforms to the characteristics of the actual equipment, ensuring the consistency between the virtual measurement process and the actual measurement process. By constructing a virtual environment that simulates a real measurement scenario, a large number of simulation experiments and path planning can be carried out in this environment, avoiding the trial-and-error costs of actual equipment debugging, and providing a unified operating platform for the implementation of subsequent steps.

[0069] S12, in the digital twin model, the measurement area is classified according to the measurement accuracy of the measured part to obtain multiple area types.

[0070] In actual measurements, the accuracy requirements for different areas of the measured part will vary. For example, in an automobile engine cylinder block, the dimensional tolerance of the combustion chamber surface may require ±0.005mm, the roundness tolerance of the inner wall of the bolt hole may require ±0.003mm, while the tolerance of the outer wall plane of the cylinder block may only require ±0.02mm. Multiple measurement accuracy ranges can be preset, each corresponding to a measurement area type.

[0071] In the digital twin model, the measurement accuracy of all measurement areas of the measured part can be matched with multiple measurement accuracy ranges to determine the type of each measurement area.

[0072] In existing technologies, all measurement areas are either processed according to high-precision requirements or low-precision requirements. However, processing according to high-precision requirements leads to excessive consumption of computational resources for viewpoint selection and path planning in low-precision areas, while processing according to low-precision requirements results in substandard measurement accuracy in critical areas. Classifying the measurement areas of the measured part according to measurement accuracy allows the digital twin model to simulate the priority requirements of real-world measurements, providing clear target guidance for subsequent viewpoint selection and path planning.

[0073] S13, obtain an initial set of measurement viewpoints, and filter out a valid set of measurement viewpoints from the initial set of measurement viewpoints according to the region type.

[0074] In the digital twin model of the part under test, the surface of the part under test is first divided into multiple initial regions based on the measurable area of ​​the part under test using a region growing algorithm. Then, combined with the field of view and depth of field of the optical sensor, each initial region is further subdivided into smaller final partitions. For each final partition, the moving position of the coordinate measuring machine is determined according to the coordinates of the partition center point and the working distance of the sensor. The A-axis / B-axis swing angle of the probe PH10T is calculated according to the partition normal vector using a preset formula. Finally, the moving position of the coordinate measuring machine and the A-axis / B-axis swing angle uniquely determine a single initial measurement viewpoint. After summing up the initial measurement viewpoints corresponding to all final partitions, an initial measurement viewpoint set is formed.

[0075] Since the initial measurement viewpoint set is generated in batches based on partitioning rules, there may be overlap in the measurement range of viewpoints in adjacent partitions, or the measurement position corresponding to a viewpoint may be blocked by the component structure, preventing the sensor from actually collecting data. This results in redundant viewpoints in the initial measurement viewpoint set. Redundant viewpoints increase the computational load of subsequent path planning and may also lead to invalid movements during the measurement process, reducing overall measurement efficiency. Therefore, it is necessary to remove redundant viewpoints with excessive errors or that do not meet the measurement requirements from the large number of initially generated measurement viewpoints. After removing redundant viewpoints, a valid viewpoint set with no redundancy in the measurement range and meeting the data accuracy standards can be selected, laying the foundation for subsequent planning of efficient and high-precision measurement paths.

[0076] In an optional embodiment, the step of filtering the initial set of measurement viewpoints according to the region type to obtain a valid set of measurement viewpoints includes:

[0077] The sampling ratio is determined based on the region type.

[0078] According to the sampling ratio, a candidate measurement viewpoint set is obtained by sampling from the initial measurement viewpoint set of the corresponding region.

[0079] Calculate the error between the measurement data under the candidate measurement viewpoint set and the measurement data obtained from simulation;

[0080] Determine whether the error is greater than a preset error threshold;

[0081] If the error is greater than the preset error threshold, the measurement viewpoints in the candidate measurement viewpoint set are removed to obtain the effective measurement viewpoint set.

[0082] The sampling ratio refers to the proportion of initial measurement viewpoints selected for error calculation in a region, determined according to the region type. Different region types correspond to different sampling ratios. For example, for the high-priority combustion chamber region, to ensure measurement accuracy, the sampling ratio is set to 100%, meaning all 200 initial measurement viewpoints are selected, resulting in a candidate measurement viewpoint set of 200 viewpoints; for the medium-priority cylinder block connection region, the sampling ratio is set to 50%, meaning 150 initial measurement viewpoints are randomly selected at a 50% ratio, resulting in a candidate measurement viewpoint set of 75 viewpoints; for the low-priority cylinder block outer wall region, the sampling ratio is set to 30%, meaning 100 initial measurement viewpoints are randomly selected at a 30% ratio, resulting in a candidate measurement viewpoint set of 30 viewpoints.

[0083] For each candidate measurement viewpoint in the candidate measurement viewpoint set, the coordinate measuring machine is controlled to perform actual measurements on the corresponding part of the measured part under that viewpoint to obtain actual measurement data; at the same time, the measurement process under that candidate measurement viewpoint is simulated in the digital twin model to obtain simulated measurement data; the difference between the actual measurement data and the simulated data corresponding to each candidate measurement viewpoint is calculated to obtain the error of the candidate measurement viewpoint, and the error reflects the accuracy of the measurement of the candidate measurement viewpoint.

[0084] The preset error threshold is an upper limit value for error set according to the measurement accuracy requirements. Different error thresholds can be set for different measurement areas, or the same error threshold can be set. The error is compared with the preset error threshold. When the error is greater than the preset error threshold, it indicates that the measurement accuracy of the candidate measurement viewpoint is not up to standard, and the candidate measurement viewpoint needs to be eliminated. When the error is less than or equal to the preset error threshold, it indicates that the measurement accuracy of the candidate measurement viewpoint is up to standard, and the candidate measurement viewpoint needs to be retained. Candidate measurement viewpoints with errors greater than the corresponding error thresholds in the set of candidate measurement viewpoints are eliminated to obtain the effective set of measurement viewpoints.

[0085] The above-described optional implementation methods address the varying accuracy requirements of different regions. By determining the sampling ratio based on region type and sampling according to that ratio to obtain a candidate measurement viewpoint set, differentiated sampling for regions with different accuracy requirements is achieved. This ensures measurement accuracy in high-priority regions while reducing computational load in low-priority regions, thus improving viewpoint selection efficiency. Selecting representative viewpoints from a large number of initial viewpoints for subsequent error analysis avoids error calculation for all initial viewpoints, reducing workload and increasing selection speed. By calculating the error between candidate viewpoint measurement data and simulation data, the measurement accuracy of each candidate viewpoint can be accurately determined, providing an objective basis for subsequently eliminating viewpoints with substandard accuracy. Eliminating viewpoints with substandard measurement accuracy ensures the quality of the effective measurement viewpoint set, laying the foundation for planning high-precision measurement paths.

[0086] S14. Based on the effective measurement viewpoint set, the initial measurement path of the coordinate measuring machine is obtained by using the ant colony algorithm with path distance, measurement accuracy compliance rate and motion energy consumption as optimization objectives.

[0087] Ant colony optimization is a biomimetic optimization algorithm that simulates the path-finding process of ant colonies. Ants leave pheromones along their paths, and other ants tend to follow paths with higher pheromone concentrations, eventually leading the colony to the shortest path.

[0088] After filtering out a set of effective measurement viewpoints that guarantee measurement accuracy from a large initial set of viewpoints, these effective viewpoints can be used as nodes in the ant colony algorithm. These nodes serve as stations on the measurement path, thus finding a measurement route that passes through all stations. When searching for a measurement route, three objectives need to be considered simultaneously: minimizing the path distance, maximizing the measurement accuracy achievement rate, and minimizing the possibility of energy depletion. Finally, through iterative calculations using the ant colony algorithm, the route with the best overall performance is obtained and used as the initial measurement path.

[0089] In an optional embodiment, the initial measurement path of the coordinate measuring machine is obtained by using an ant colony algorithm based on the effective measurement viewpoint set, with path distance, measurement accuracy achievement rate, and motion energy consumption as optimization objectives. This includes:

[0090] Initialize the parameters of the ant colony algorithm;

[0091] In the digital twin model, each viewpoint in the effective measurement viewpoint set is used as a node of the ant colony algorithm to establish an initial pheromone matrix and a heuristic information matrix;

[0092] Construct a complete measurement path based on pheromone concentration and heuristic information;

[0093] For each constructed measurement path, the target values ​​are calculated with path distance, measurement accuracy compliance rate, and motion energy consumption as optimization objectives.

[0094] Based on the target value of each measurement path, update the pheromone concentration of each edge on the measurement path. The measurement path with the better target value will obtain a larger pheromone increment.

[0095] When the iteration meets the stopping requirement, the current optimal path is output as the initial measurement path.

[0096] First, the parameters of the ant colony algorithm are initialized. These parameters can include: number of ants, pheromone heuristic factor, expected heuristic factor, pheromone evaporation coefficient, and maximum number of iterations. Specifically, the number of ants is the number of virtual ants participating in the path search (e.g., 100), the pheromone heuristic factor influences the ants' dependence on pheromones (e.g., 1.2), the expected heuristic factor influences the ants' dependence on heuristic information such as path distance (e.g., 0.8), the pheromone evaporation coefficient controls the rate of pheromone decay (e.g., 0.5), and the maximum number of iterations is the maximum number of loops the algorithm can terminate (e.g., 1000).

[0097] The initial pheromone matrix represents the initial pheromone concentration between nodes in the ant colony algorithm. Initially, the pheromone concentration on each side is the same, for example, all set to 0.1. Assuming the number of effective measurement viewpoints is N, then the number of nodes is N, and the initial pheromone matrix is ​​N*N. The heuristic information matrix is ​​a matrix constructed based on the path distance between nodes, the measurement accuracy achievement rate, and the motion energy consumption. It reflects the attraction of ants moving from one node to another; for example, the closer the distance, the higher the measurement accuracy achievement rate, and the lower the energy consumption, the larger the heuristic information value. The path distance between any two nodes can be calculated, the measurement accuracy achievement probability can be determined based on the type of region where the node is located and historical measurement data, and the motion energy consumption can be calculated based on the motion parameters of the coordinate measuring machine. The path distance, measurement accuracy achievement rate, and motion energy consumption are then fused according to preset weights to obtain the heuristic information value, thereby constructing the heuristic information matrix.

[0098] Each ant starts from a randomly selected starting node. When choosing the next node, it selects the node according to the pheromone concentration and heuristic information value between the nodes, using a roulette wheel method (the higher the pheromone concentration and heuristic information value, the higher the probability of being selected). This continues until all nodes are traversed, constructing a complete measurement path. The number of different measurement paths will be as many as the number of ants.

[0099] For each constructed measurement path, the target value is obtained by fusing the path distance, measurement accuracy compliance rate, and motion energy consumption according to preset weights. The target value is used to evaluate the quality of the measurement path; the smaller the target value, the better the measurement path, and the larger the target value, the worse the measurement path.

[0100] Identify the measurement path with the smallest target value among all measurement paths. Calculate the pheromone increment based on the target value corresponding to this measurement path; the smaller the target value, the larger the increment. Pheromone increment = Q / target value, where Q is a constant. New pheromone concentration = (1 - volatility coefficient) × old pheromone concentration + pheromone increment. Update the pheromone concentration of each edge on the measurement path to the new pheromone concentration.

[0101] The iteration stops when the following conditions are met: the number of iterations reaches the maximum number of iterations, and the change in the target value of the optimal path obtained through multiple consecutive iterations is less than a preset threshold. The algorithm then stops and outputs the current optimal path as the initial measurement path.

[0102] In the above optional implementations, establishing an initial pheromone matrix ensures fair search in the initial stage of the ant colony algorithm, with each measurement path having the possibility of being selected. Establishing a heuristic information matrix based on key indicators of actual measurement needs guides ants to search towards better paths, reducing blindness. Combining pheromone concentration and heuristic information values ​​in node selection utilizes information accumulated in previous searches (pheromone) while considering the merits of the paths themselves (heuristic information), effectively balancing global and local searches and increasing the probability of finding the globally optimal path. By fusing multiple objectives into a single comparable value, accurate evaluation of the comprehensive performance of different paths is achieved, providing an objective basis for subsequent pheromone updates and optimal path selection. Updating pheromone concentration based on path objective values ​​guides subsequent ants to search more areas near the optimal path, accelerating algorithm convergence while maintaining a certain degree of search diversity and avoiding getting trapped in local optima.

[0103] S15, the initial measurement path is optimized based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path.

[0104] Although an initial measurement path was obtained by optimizing path distance, measurement accuracy compliance rate, and motion energy consumption, the initial measurement path does not fully conform to the actual execution characteristics of the coordinate measuring machine. It ignores the time loss of the actual movement of the probe, resulting in the actual measurement efficiency being far lower than expected. Therefore, the optimization of path distance, measurement accuracy compliance rate, and motion energy consumption is not directly equivalent to the optimization of actual measurement time. Optimizing the initial measurement path by combining the actual motion parameters of the probe can yield a measurement path that is more in line with the actual measurement scenario and has higher execution efficiency.

[0105] The actual motion parameters of the probe may include, but are not limited to: the magnitude of the angle change between adjacent measuring points (angle change magnitude) and the magnitude of the change in motion direction (motion direction change magnitude). The actual motion parameters of the probe are determined by the position and orientation of adjacent measuring points in the initial measurement path.

[0106] In an optional embodiment, optimizing the initial measurement path based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path includes:

[0107] For each adjacent measuring point in the initial measurement path, the angle conversion time and the pause time are dynamically calculated based on the actual motion parameters of the probe using a pre-established correlation model between the probe angle conversion time and the conversion amplitude, and between the pause time and the change amplitude of the motion direction.

[0108] A time-cost adjacency matrix is ​​generated based on the angle conversion time and the pause time.

[0109] Based on the time-cost adjacency matrix, the initial measurement path is optimized using the ant colony algorithm to obtain an optimized measurement path.

[0110] Adjacent measurement points refer to two consecutive measurement viewpoints in the initial measurement path. For example, the first and second measurement viewpoints in a certain measurement path are adjacent measurement points, or the second and third measurement viewpoints are adjacent measurement points, etc.

[0111] The correlation model is a pre-established mathematical model used to describe the correspondence between probe angle conversion time and conversion amplitude, and pause time and change in motion direction amplitude. Specifically, the correlation model between probe angle conversion time and conversion amplitude, and pause time and change in motion direction amplitude is established as follows: The actual conversion time of the probe under different angle conversion amplitudes is collected experimentally, and a functional relationship is established between the angle conversion time t1 and the angle conversion amplitude Δθ: t1 = a × Δθ + b, where a and b are coefficients obtained by fitting experimental data, and the angle conversion amplitude Δθ is the size of the rotation angle when the probe changes its posture from one measuring point to another, for example, a 20° rotation along the A-axis and a 10° rotation along the B-axis. The total angle conversion amplitude is obtained through vector synthesis. The correlation between the change in motion direction amplitude and the pause time is analyzed, and a functional relationship is established between the pause time t2 and the change in motion direction amplitude Δα: t2 = c × Δα 2 +d×Δα+e, where c, d, and e are fitting coefficients, and Δα is the angle between the two motion directions when the probe moves between two adjacent measuring points, reflecting the degree of change in motion direction.

[0112] In the initial measurement path, all adjacent measurement point pairs are extracted. Using a digital twin model, the angle conversion amplitude and movement direction change amplitude of the probe between each pair of adjacent measurement points are calculated. The angle conversion time t1 is calculated based on the angle conversion amplitude, and the pause time t2 is calculated based on the movement direction change amplitude. The probe movement time t3 between adjacent measurement points is calculated based on the distance between adjacent measurement points and the probe's movement speed. The total time cost for each pair of adjacent measurement points is then calculated (angle conversion time t1 + pause time t2 + movement time t3). A time cost adjacency matrix is ​​generated using the total time cost between adjacent measurement points as its elements. The element in the i-th row and j-th column of the time cost adjacency matrix represents the total time cost from the i-th measurement point to the j-th measurement point. If two measurement points are not adjacent, the total time cost can be set to a larger value. The time cost adjacency matrix reflects the time consumption from one measurement point to another, providing a time dimension reference for subsequent path optimization.

[0113] Using the time cost adjacency matrix as input to the ant colony algorithm, ants construct paths based on the time cost information in the matrix. After each path construction, the total time cost is calculated, and the pheromone is updated according to the total time cost (the lower the total time cost, the larger the pheromone increment). The algorithm iterates until the stopping requirement is met, and the optimized path is output as the optimized measurement path.

[0114] The aforementioned optional implementation method dynamically calculates the angle conversion amplitude and motion direction change amplitude of the probe between adjacent measuring points based on actual motion parameters, thereby obtaining the angle conversion time and pause time. This achieves precise time calculation, avoids errors caused by fixed-time calculation, and ensures the accuracy of the subsequent time cost adjacency matrix. By generating the time cost adjacency matrix, the time cost between measuring points is presented in a structured matrix form, providing clear and efficient data input for the ant colony algorithm to optimize the path, facilitating rapid path search and evaluation by the algorithm. Based on the time cost adjacency matrix, the ant colony algorithm optimizes the initial measurement path, finding the optimized measurement path with the lowest total time cost, reducing time consumption in actual measurement, improving the measurement efficiency of the coordinate measuring machine, and meeting the needs of rapid measurement of large batches of parts.

[0115] In an optional embodiment, the coordinate measuring machine measurement path optimization method based on digital twin further includes:

[0116] Acquire the status data of the coordinate measuring machine during the actual measurement process;

[0117] The parameters of the digital twin model are corrected based on the state data and the simulation data of the digital twin model;

[0118] The measurement path is iteratively optimized based on the revised digital twin model;

[0119] After the iterative optimization is completed, the target measurement path is output.

[0120] Status data refers to various real-time data generated by the coordinate measuring machine (CMM) during the actual measurement process. When the CMM measures the part being measured (gearbox housing) according to the optimized measurement path, status data is collected in real time through the CMM's data acquisition system. Status data can include position data, velocity data, acceleration data, probe angle data, and measured part dimensional error data for each axis of the CMM.

[0121] In the digital twin model, simulation measurements are performed according to the same optimized measurement path to obtain corresponding simulation data. The simulation data and the state data are of the same type.

[0122] The actual state data is compared with the simulation data one by one, the difference value is calculated, and the average difference value of all data is statistically analyzed. Based on the average difference value, the relevant parameters in the digital twin model are adjusted to make the simulation results of the model closer to the actual measurement results. For example, the positioning error compensation coefficient of the coordinate measuring machine in the digital twin model is adjusted according to the average difference value of the positioning error of the coordinate measuring machine, and the sensor error proportional coefficient in the digital twin model is adjusted according to the average difference value of the sensor measurement error.

[0123] Based on the revised digital twin model, the steps of region division, viewpoint selection, and path planning are re-executed to obtain a better measurement path. Then, the status data is obtained again through actual measurement, compared with the revised model, and this process is repeated to continuously improve the path quality.

[0124] After correction, simulation measurements are performed again in the model, and the difference between the new simulation data and the actual state data is calculated. The iteration stops when the average difference between the simulation data and the actual state data of the digital twin model stabilizes within the preset average difference threshold, and the current optimized measurement path is output as the target measurement path.

[0125] The above-mentioned optional implementation method, by acquiring the state data of the actual measurement process of the coordinate measuring machine and correcting the parameters of the digital twin model based on the state data and simulation data, provides an objective basis for evaluating the performance of the digital twin model and the measurement path. This avoids the problem of decoupling from reality caused by relying solely on virtual simulation, making the simulation results of the digital twin model closer to the actual measurement results and improving the reliability of the model. By iteratively optimizing the measurement path based on the corrected model and outputting the target measurement path after the iteration optimization, continuous improvement of the measurement path is achieved. Each iteration can obtain a better path based on a more accurate model, continuously improving measurement efficiency and accuracy. This ensures that the final output path is the optimal path after multiple actual verifications and model corrections, which can meet the high-precision and high-efficiency measurement requirements of complex parts and improve the actual measurement performance of the coordinate measuring machine.

[0126] In an optional embodiment, the coordinate measuring machine measurement path optimization method based on digital twin further includes:

[0127] The coordinate measuring machine is controlled to measure the part under test based on the target measurement path.

[0128] The target measurement path is the final measurement path obtained after multiple iterations and optimizations. It has advantages such as high measurement efficiency, high accuracy compliance rate, and conformity to the actual motion characteristics of the probe. The target measurement path contains all the key information required for the coordinate measuring machine (CMM) to perform the measurement. The CMM's built-in control software can be used to convert all the key information contained in the target measurement path into control commands that the CMM can recognize, and then send the control commands to the CMM's control system to enable the CMM to automatically measure according to the target measurement path. The control commands can carry the position coordinates of each measuring point, probe angle, and movement speed. After the CMM reaches each measuring point according to the target measurement path, it uses optical sensors to measure the corresponding parts of the measured part, acquiring the actual dimensional data of the part (such as length, diameter, roundness, and positional accuracy), and storing or outputting the measurement data to determine whether the part meets the design requirements.

[0129] The above-mentioned optional implementation method automates the measurement process by controlling the coordinate measuring machine to perform measurements based on the target measurement path, avoiding human errors caused by manual operation (such as deviations caused by manually adjusting the probe angle). At the same time, the optimization characteristics of the target measurement path can fully utilize the performance of the coordinate measuring machine and improve the accuracy and efficiency of the measurement.

[0130] The measurement path optimization method for coordinate measuring machines (CMMs) based on digital twins provided in this application establishes a digital twin model of the measured part and the CMM, simulating the real measurement process in a virtual environment. This avoids equipment wear and time waste during actual debugging and reduces the trial-and-error cost of measurement path planning. By classifying the measurement area in the digital twin model according to the measurement accuracy of the measured part, multiple area types are obtained. The effective set of measurement viewpoints is then determined based on the area type, breaking the traditional indiscriminate selection mode and achieving differentiated selection based on area importance. This significantly improves the viewpoint accuracy while ensuring measurement accuracy. The efficiency of the screening process is improved. Based on an effective set of measurement viewpoints, an ant colony algorithm is used to solve for the initial measurement path of the coordinate measuring machine (CMM) with path distance, measurement accuracy achievement rate, and motion energy consumption as optimization objectives. The initial measurement path can take into account distance, accuracy, and energy consumption, meeting various needs in the measurement process. Finally, the initial measurement path is optimized according to the actual motion parameters of the CMM probe, resulting in an optimized measurement path that better matches the actual motion of the probe. This changes the traditional fixed-time calculation mode, further reduces time loss in actual measurement, and ensures the efficiency of path planning, thereby meeting the real-time measurement requirements.

[0131] Example 2

[0132] Figure 2This is a functional block diagram of the coordinate measuring machine measurement path optimization device based on digital twin provided in the embodiments of this application.

[0133] In some embodiments, the digital twin-based coordinate measuring machine (CMM) measurement path optimization device 20 may include multiple functional modules composed of program code segments. The program code of each program segment in the digital twin-based CMM measurement path optimization device 20 may be stored in the memory of the CMM and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality of measurement path optimization for coordinate measuring machines based on digital twins.

[0134] In this embodiment, the coordinate measuring machine measurement path optimization device 20 based on digital twins can be divided into multiple functional modules according to its functions. These functional modules may include: a setup module 201, a classification module 202, a filtering module 203, a calculation module 204, an optimization module 205, and a measurement module 206. As used in this application, a module refers to a series of computer-readable instruction segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0135] The establishment module 201 is used to establish a digital twin model of the part under test and the coordinate measuring machine.

[0136] The classification module 202 is used to classify the measurement area in the digital twin model according to the measurement accuracy of the measured part to obtain multiple area types.

[0137] The filtering module 203 is used to obtain an initial set of measurement viewpoints and filter out an effective set of measurement viewpoints from the initial set of measurement viewpoints according to the region type.

[0138] The calculation module 204 is used to solve for the initial measurement path of the coordinate measuring machine based on the effective measurement viewpoint set and using the ant colony algorithm, with path distance, measurement accuracy achievement rate and motion energy consumption as optimization objectives.

[0139] The optimization module 205 is used to optimize the initial measurement path based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path.

[0140] The measurement module 206 is used to control the coordinate measuring machine to measure the part under test based on the target measurement path.

[0141] It should be understood that the various variations and specific embodiments of the coordinate measuring machine measurement path optimization method based on digital twins provided in the above embodiments are also applicable to the coordinate measuring machine measurement path optimization device based on digital twins in this embodiment. Through the detailed description of the coordinate measuring machine measurement path optimization method based on digital twins described above, those skilled in the art can clearly understand the implementation process of the coordinate measuring machine measurement path optimization device based on digital twins in this embodiment. For the sake of brevity, it will not be described in detail here.

[0142] Example 3

[0143] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the steps described in the embodiment of the measurement path optimization method for a coordinate measuring machine based on digital twins. Figure 1 S11-S15 are shown.

[0144] S11, Establish a digital twin model of the part under test and the coordinate measuring machine;

[0145] S12, In the digital twin model, the measurement area is classified according to the measurement accuracy of the measured part to obtain multiple area types;

[0146] S13, obtain an initial measurement viewpoint set, and filter out a valid measurement viewpoint set from the initial measurement viewpoint set according to the region type;

[0147] S14. Based on the effective measurement viewpoint set, the initial measurement path of the coordinate measuring machine is obtained by using the ant colony algorithm with path distance, measurement accuracy compliance rate and motion energy consumption as optimization objectives.

[0148] S15, the initial measurement path is optimized based on the actual motion parameters of the coordinate measuring machine probe to obtain an optimized measurement path.

[0149] Example 4

[0150] See Figure 3 The diagram shown is a structural schematic of a coordinate measuring machine provided in an embodiment of this application. In a preferred embodiment of this application, the coordinate measuring machine 3 includes: a side head 300, a memory 301, at least one processor 302, and at least one communication bus 303.

[0151] Those skilled in the art should understand that Figure 3 The structure of the coordinate measuring machine shown is not intended to limit the embodiments of this application. The coordinate measuring machine 3 may also include more or fewer other hardware or software, or different component arrangements than shown.

[0152] In some embodiments, the memory 301 stores a computer program and an operating system. When executed by the at least one processor 302, the computer program implements all or part of the steps in the digital twin-based coordinate measuring machine measurement path optimization method described above. The memory 301 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for a function, etc.

[0153] In some embodiments, the at least one processor 302 is the control unit of the coordinate measuring machine 3, connecting various components of the coordinate measuring machine 3 via various interfaces and lines. It executes programs or modules stored in the memory 301 and calls data stored in the memory 301 to perform various functions and process data of the coordinate measuring machine 3. For example, when the at least one processor 302 executes the computer program stored in the memory, it implements all or part of the steps of the coordinate measuring machine measurement path optimization method based on digital twins described in this application embodiment; or it implements all or part of the functions of the coordinate measuring machine measurement path optimization device based on digital twins. The at least one processor 302 may be composed of integrated circuits, such as a single-packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0154] In some embodiments, the at least one communication bus 303 is configured to enable communication between the memory 301 and the at least one processor 302, etc. Although not shown, the coordinate measuring machine 3 may also include a power supply (e.g., a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 302 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, a rechargeable power fault detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components. The coordinate measuring machine 3 may also include various sensors, a Bluetooth module, a Wi-Fi module, internal memory, a network interface, input positions, and a display screen, etc., which will not be described in detail here.

[0155] The integrated unit, implemented as a software functional module, can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a coordinate measuring machine to execute portions of the methods described in the various embodiments of this application.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of modules is merely a logical functional division, and other division methods may be used in actual implementation.

[0157] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for optimizing the measurement path of a coordinate measuring machine based on digital twins, characterized in that, The measurement path optimization method for a coordinate measuring machine based on digital twins includes: Establish a digital twin model of the part to be measured and the coordinate measuring machine; In the digital twin model, the measurement area is classified according to the measurement accuracy of the measured part to obtain multiple area types; Obtain an initial set of measurement viewpoints, and filter out a valid set of measurement viewpoints from the initial set of measurement viewpoints according to the region type; Initialize the parameters of the ant colony algorithm; In the digital twin model, each viewpoint in the effective measurement viewpoint set is used as a node of the ant colony algorithm to establish an initial pheromone matrix and a heuristic information matrix; Construct a complete measurement path based on pheromone concentration and heuristic information; For each constructed measurement path, the target values ​​are calculated with path distance, measurement accuracy compliance rate, and motion energy consumption as optimization objectives. Based on the target value of each measurement path, update the pheromone concentration of each edge on the measurement path. The measurement path with the better target value will obtain a larger pheromone increment. When the iteration meets the stopping requirement, the current optimal path is output as the initial measurement path; For each adjacent measuring point in the initial measurement path, the angle conversion time and the pause time are dynamically calculated based on the actual motion parameters of the probe using a pre-established correlation model between the probe angle conversion time and the conversion amplitude, and between the pause time and the change amplitude of the motion direction. A time-cost adjacency matrix is ​​generated based on the angle conversion time and the pause time. Based on the time-cost adjacency matrix, the initial measurement path is optimized using the ant colony algorithm to obtain an optimized measurement path.

2. The method for optimizing the measurement path of a coordinate measuring machine based on digital twins according to claim 1, characterized in that, The step of filtering the effective set of measurement viewpoints from the initial set of measurement viewpoints according to the region type includes: The sampling ratio is determined based on the region type. According to the sampling ratio, a candidate measurement viewpoint set is obtained by sampling from the initial measurement viewpoint set of the corresponding region. Calculate the error between the measurement data under the candidate measurement viewpoint set and the measurement data obtained from simulation; Determine whether the error is greater than a preset error threshold; If the error is greater than the preset error threshold, the measurement viewpoints in the candidate measurement viewpoint set are removed to obtain the effective measurement viewpoint set.

3. The method for optimizing the measurement path of a coordinate measuring machine based on digital twins according to claim 1, characterized in that, The correlation model between probe angle conversion time and conversion amplitude, and between pause time and change in motion direction amplitude, is established in the following way: By experimentally collecting the actual conversion time of the probe under different angle conversion amplitudes, a functional relationship between angle conversion time t1 and angle conversion amplitude Δθ is established: t1=a×Δθ+b, where a and b are coefficients obtained by fitting experimental data; Analyze the correlation between the magnitude of the change in motion direction and the pause time, and establish a functional relationship between the pause time t2 and the magnitude of the change in motion direction Δα: t2=c×Δα2+d×Δα+e, where c, d, and e are fitting coefficients.

4. The method for optimizing the measurement path of a coordinate measuring machine based on digital twins according to claim 1, characterized in that, The establishment of a digital twin model between the measured part and the coordinate measuring machine includes: Geometric tolerance and material property data are obtained from the three-dimensional CAD model of the part under test; Obtain the structural parameters, kinematic parameters, and sensor performance parameters of the coordinate measuring machine; Based on the 3D CAD model, the geometric tolerances, the material property data, the structural parameters, the kinematic parameters, and the sensor performance parameters, an integrated digital twin model is constructed on the digital twin platform.

5. The method for optimizing the measurement path of a coordinate measuring machine based on digital twins according to claim 1, characterized in that, The coordinate measuring machine measurement path optimization method based on digital twin also includes: Acquire the status data of the coordinate measuring machine during the actual measurement process; The parameters of the digital twin model are corrected based on the state data and the simulation data of the digital twin model; The measurement path is iteratively optimized based on the revised digital twin model; After the iterative optimization is completed, the target measurement path is output.

6. The method for optimizing the measurement path of a coordinate measuring machine based on digital twins according to claim 5, characterized in that, The coordinate measuring machine measurement path optimization method based on digital twin also includes: The coordinate measuring machine is controlled to measure the part under test based on the target measurement path.

7. A coordinate measuring machine, characterized in that, The coordinate measuring machine includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the coordinate measuring machine measurement path optimization method based on digital twin according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the measurement path optimization method for a coordinate measuring machine based on digital twins according to any one of claims 1 to 6.

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