Device and method for detecting process parameters of parts

Abstract

The present invention discloses a device and method for detecting process parameters of parts, which relate to the field of automation control. The device comprises: a part information acquisition module for performing image acquisition and obtaining part laser point cloud information; a part space model acquisition module for spatially registering and fusing part image information and part laser point cloud information to obtain a standard part space model; a multidimensional loss analysis module for performing multidimensional loss analysis on target and standard part space models; a part process detection feature parameter acquisition module for obtaining internal defect feature parameters and part process detection feature parameters; and a part production optimization control module for performing process parameter compensation optimization and production control through part production process optimization parameters. The device solves the technical problems of insufficient detection accuracy and low detection efficiency in existing part process parameter detection, achieving the technical effect of efficient and accurate detection of parts and adaptive optimization of production processes.

Description

Technical Field

[0001] The present application relates to fields related to automation control, and in particular to a device and method for detecting process parameters of parts. Background Art

[0002] In the manufacturing industry, inspecting part process parameters is a critical step in ensuring product quality and production efficiency. Traditional inspection methods, such as using vernier calipers and micrometers to measure part dimensions, while offering a certain degree of accuracy, have limited measurement range, are difficult to accurately measure parts with complex shapes, and are time-consuming, failing to meet the production efficiency requirements of modern manufacturing.

[0003] In the current related technologies, the process parameter detection of parts has technical problems such as insufficient detection accuracy and low detection efficiency. Summary of the Invention

[0004] The present application provides a device and method for detecting process parameters of parts, utilizes an industrial camera and a 3D scanner to simultaneously collect image information and laser point cloud information of parts, spatially aligns and fuses the image information and laser point cloud information of parts, generates a target part space model, and performs multi-dimensional loss analysis with the standard part space model to obtain the surface deviation parameters of the part, obtains the internal defect characteristic parameters of the part through ultrasonic sensor detection, obtains the part process detection characteristic parameters in combination with the part surface deviation parameters, performs process parameter compensation optimization based on the part process detection characteristic parameters, obtains the part production process optimization parameters, and uses these parameters to perform production optimization control on the target parts, thereby achieving the technical effects of efficient and accurate detection of parts, adaptive optimization of production processes, and improved product process quality.

[0005] The present application provides a device for detecting process parameters of a part, comprising:

[0006] A part information acquisition module, the part information acquisition module is used to capture images of the target part through an industrial camera to obtain part image information, and at the same time, to acquire part laser point cloud information through a three-dimensional scanner; a part space model acquisition module, the part space model acquisition module is used to spatially align and fuse the part image information and the part laser point cloud information to generate a target part space model, and at the same time, obtain a standard part space model based on the drawing information of the target part; a multi-dimensional loss analysis module, the multi-dimensional loss analysis module is used to perform multi-dimensional loss analysis on the target part space model and the standard part space model to obtain part surface deviation parameters; a part process detection feature parameter acquisition module, the part process detection feature parameter acquisition module is used to obtain the internal defect feature parameters of the target part through ultrasonic sensor detection, and obtain the part process detection feature parameters based on the part surface deviation parameters and the internal defect feature parameters; a part production optimization control module, the part production optimization control module is used to perform process parameter compensation optimization based on the part process detection feature parameters, obtain part production process optimization parameters, and perform production optimization control on the target part through the part production process optimization parameters.

[0007] In a possible implementation, the target part space model is generated by performing the following processing:

[0008] The industrial camera and the three-dimensional scanner are calibrated separately to determine camera calibration parameters and scanner calibration parameters; a joint calibration is performed based on the camera calibration parameters and the scanner calibration parameters to establish an image pixel-laser point cloud mapping relationship; key feature points are extracted from the part image information and the part laser point cloud information in turn to obtain an image feature point set and a point cloud feature point set; feature point matching is performed on the image feature point set and the point cloud feature point set according to the image pixel-laser point cloud mapping relationship to obtain part feature point matching information; data fusion and three-dimensional reconstruction are performed on the part image information and the part laser point cloud information based on the part feature point matching information to generate the target part space model.

[0009] In a possible implementation, the part surface deviation parameter is obtained by performing the following processing:

[0010] According to the part process requirement standards, a feature twin network is constructed, which includes a standard twin network and a target twin network, and the standard twin network and the target twin network are shared weight networks; based on the standard twin network and the target twin network, multi-dimensional feature extraction is performed on the standard part space model and the target part space model respectively, and the standard part feature set and the target part feature set are output; a feature loss function is introduced to perform loss analysis on the standard part feature set and the target part feature set to obtain the part surface deviation parameters.

[0011] In a possible implementation, the parts production process optimization parameters are obtained and the following processing is performed:

[0012] A part process feature simulation model is established, and process control compensation analysis is performed on the part process detection feature parameters based on the part process feature simulation model to obtain a process control parameter selection threshold; a plurality of process control parameters are randomly generated based on the process control parameter selection threshold, and the plurality of process control parameters are simulated and evaluated through the part process feature simulation model to obtain a plurality of part process feature prediction qualities; the plurality of process control parameters are clustered, expanded and optimized according to the plurality of part process feature prediction qualities to obtain the part production process optimization parameters.

[0013] In a possible implementation, the part process feature simulation model is established by performing the following processing:

[0014] The production process parameter space of the target part is constructed by data mining technology, and the production process parameter space includes production process control parameters and corresponding part process detection feature data; the part process detection feature data is classified into multi-dimensional indicators according to the part process requirement standard to obtain a part process feature indicator classification data set; the production process control parameters are respectively associated with the part process feature indicator classification data set to obtain a process control parameter-process indicator feature model set; each associated model in the process control parameter-process indicator feature model set is subjected to equal-weight simulation fusion to establish the part process feature simulation model.

[0015] In a possible implementation, the part production process optimization parameters are obtained and the following processing is performed:

[0016] The multiple process control parameters are optimized according to the predicted qualities of the multiple part process features to obtain multiple parent process control parameters with preset proportions; the multiple parent process control parameters are respectively used as cluster centers to cluster the multiple process control parameters to obtain multiple process control parameter clusters; based on the multiple parent process control parameters, the multiple process control parameter clusters are expanded and optimized to obtain multiple process control optimization parameter clusters; based on the multiple process control optimization parameter clusters, a global comparison and optimization is performed to output the part production process optimization parameters.

[0017] In a possible implementation, the multiple process control optimization parameter clusters are obtained and the following processing is performed:

[0018] Based on the multiple parent process control parameters, preset intra-cluster distance parameters are screened within the multiple process control parameter clusters to obtain multiple intra-cluster adjacent control parameters; the multiple parent process control parameters and the multiple intra-cluster adjacent control parameters are cross-combined and parameter mutated to obtain multiple child process control parameters; based on the multiple child process control parameters, the multiple process control parameter clusters are expanded with child parameters to obtain multiple process control expanded parameter clusters; and iterative optimization and updating are performed within the multiple process control expanded parameter clusters to obtain the multiple process control optimized parameter clusters.

[0019] This application also provides a method for detecting process parameters of a part, including:

[0020] The target part is imaged by an industrial camera to obtain part image information, and the part laser point cloud information is obtained by a three-dimensional scanner; the part image information and the part laser point cloud information are spatially aligned and fused to generate a target part space model, and the standard part space model is obtained based on the drawing information of the target part; a multi-dimensional loss analysis is performed on the target part space model and the standard part space model to obtain part surface deviation parameters; the internal defect characteristic parameters of the target part are obtained by ultrasonic sensor detection, and the part process detection characteristic parameters are obtained based on the part surface deviation parameters and the internal defect characteristic parameters; process parameter compensation optimization is performed based on the part process detection characteristic parameters to obtain part production process optimization parameters, and the target part is subjected to production optimization control through the part production process optimization parameters.

[0021] The present application proposes a device and method for detecting process parameters of a part. The industrial camera in the part information acquisition module is used to capture the image of the target part to obtain the part image information. At the same time, the part laser point cloud information is acquired through the three-dimensional scanner. The part image information and the part laser point cloud information are spatially aligned and fused through the part space model acquisition module to generate a target part space model. At the same time, according to the drawing information of the target part, a standard part space model is obtained. The multi-dimensional loss analysis module is used to perform multi-dimensional loss analysis on the target part space model and the standard part space model to obtain the part surface deviation parameters. The ultrasonic sensor in the part process detection feature parameter acquisition module is used to detect and obtain the internal defect feature parameters of the target part. According to the part surface deviation parameters and the internal defect feature parameters, the part process detection feature parameters are obtained. The part production optimization control module is used to perform process parameter compensation optimization based on the part process detection feature parameters to obtain the part production process optimization parameters. The target part is then optimized and controlled through the part production process optimization parameters, thereby achieving the technical effect of efficient and accurate detection of parts, adaptive optimization of production processes, and improved product process quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A schematic structural diagram of a device for detecting process parameters of a part provided in an embodiment of the present application.

[0024] Figure 2 A schematic flow chart of a method for detecting process parameters of a part provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: part information acquisition module 10 , part space model acquisition module 20 , multi-dimensional loss analysis module 30 , part process detection characteristic parameter acquisition module 40 , part production optimization control module 50 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments” which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The embodiment of the present application provides a device for detecting process parameters of a part, such as Figure 1 As shown, the device includes:

[0030] Part information acquisition module 10 is used to capture images of the target part using an industrial camera to obtain part image information, and simultaneously to acquire laser point cloud information of the part using a 3D scanner. Specifically, an industrial camera (a high-precision, high-speed image acquisition device used to capture 2D images of objects) is used to capture the target part and obtain 2D image information. Simultaneously, a 3D scanner (a device capable of acquiring 3D coordinate information on an object's surface, achieved through laser technology) is used to scan the target part. The reflected laser signal is then received to obtain 3D coordinate information of the part's surface, generating laser point cloud data. This laser point cloud data contains geometric information such as the part's surface shape and dimensions.

[0031] Part space model acquisition module 20 is used to spatially register and fuse the part image information and the part laser point cloud information to generate a target part space model. Simultaneously, based on the target part's drawing information, a standard part space model is obtained. Specifically, the collected image and point cloud data are preprocessed using denoising and filtering to improve data accuracy and reliability. The preprocessed image and point cloud data are aligned and fused using a spatial registration algorithm to form a target part space model in a unified spatial coordinate system. Based on the target part's drawing information, a standard part space model is constructed using CAD software or other 3D modeling tools.

[0032] In one possible implementation, generating a target part space model includes: calibrating the industrial camera and the three-dimensional scanner respectively to determine camera calibration parameters and scanner calibration parameters; performing joint calibration based on the camera calibration parameters and scanner calibration parameters to establish an image pixel-laser point cloud mapping relationship; extracting key feature points from the part image information and the part laser point cloud information in turn to obtain an image feature point set and a point cloud feature point set; performing feature point matching on the image feature point set and the point cloud feature point set according to the image pixel-laser point cloud mapping relationship to obtain part feature point matching information; performing data fusion and three-dimensional reconstruction on the part image information and the part laser point cloud information based on the part feature point matching information to generate the target part space model.

[0033] Specifically, using a specific calibration plate or calibration object, by capturing multiple images of the calibration plate at different angles or positions, image processing algorithms are used to calculate the industrial camera's internal parameters (such as focal length, optical center position, etc.) and external parameters (such as the camera's position and attitude relative to the world coordinate system). These parameters are known as the camera calibration parameters. For 3D scanners, by scanning an object of known shape and size and comparing the scanned results with the object's actual dimensions, the scanner's calibration parameters are adjusted. These parameters are known as the scanner calibration parameters. The industrial camera and 3D scanner are mounted together or fixed relative to each other. By simultaneously capturing and scanning the same object, the known camera and scanner calibration parameters are used to establish a mapping relationship between image pixels and the laser point cloud. In other words, through joint calibration, it is determined which position in the point cloud each pixel in the image corresponds to.

[0034] Image and point cloud data are processed using image and point cloud processing algorithms, extracting points with significant features (such as corners and edges) as feature points. Leveraging the established image pixel-to-laser point cloud mapping relationship, the image feature point set is matched with the point cloud feature point set to identify their corresponding relationships. The matched image and point cloud feature points are fused to form a unified data representation. Based on this fused data, a 3D reconstruction algorithm is used to generate a spatial model of the target part through steps such as point cloud stitching and surface reconstruction. This implementation ensures the accuracy of the measurement data through calibration of the industrial camera and 3D scanner, as well as joint calibration, thereby improving the precision of the 3D reconstruction.

[0035] The multidimensional loss analysis module 30 is used to perform a multidimensional loss analysis on the target part space model and the standard part space model to obtain part surface deviation parameters. Specifically, the target part space model and the standard part space model are compared and analyzed, including comparisons of shape, size, position, and other aspects. A loss function or error measurement method is introduced to calculate the difference between the target part and the standard part, and obtain part surface deviation parameters. These parameters include deviation value, deviation direction, deviation distribution, etc.

[0036] In one possible implementation, obtaining the part surface deviation parameters includes: building a feature twin network according to the part process requirement standard, the feature twin network including a standard twin network and a target twin network, and the standard twin network and the target twin network are shared weight networks; based on the standard twin network and the target twin network, performing multi-dimensional feature extraction on the standard part space model and the target part space model respectively, and outputting a standard part feature set and a target part feature set; introducing a feature loss function to perform loss analysis on the standard part feature set and the target part feature set to obtain the part surface deviation parameters.

[0037] Specifically, to compare the differences between the standard part space model and the target part space model, a feature twin network is constructed based on process parameter standards such as part geometry, dimensional accuracy, flatness, and surface defects. This network consists of two sub-networks with identical structures and shared weights: the standard twin network and the target twin network. The standard and target part space models are input into the standard and target twin networks, respectively. Through network layers such as convolutional and pooling layers, the spatial features of the parts (shape, size, surface quality, etc.) are extracted. Each twin network outputs a feature set representing the multi-dimensional features of the standard and target parts, respectively. A defined feature loss function is used to calculate the loss between the standard and target part feature sets. A backpropagation algorithm is used to adjust the network weights to minimize the feature loss. When the feature loss reaches a preset threshold or the number of training cycles reaches a preset upper limit, training is terminated and the part surface deviation parameters are output. This implementation method extracts high-dimensional features of parts through the feature twin network and measures the difference between the standard and target parts using the loss function, enabling accurate detection of part surface deviations.

[0038] The part process detection characteristic parameter acquisition module 40 is used to obtain the internal defect characteristic parameters of the target part through ultrasonic sensor detection, and obtain the part process detection characteristic parameters based on the part surface deviation parameters and the internal defect characteristic parameters. Specifically, the ultrasonic sensor is used to perform internal quality inspection on the target part, and the internal defects of the part are detected by emitting ultrasonic waves and receiving the reflected signals, such as cracks, pores, inclusions and other structures or materials that do not meet the requirements. Based on the detection results of the ultrasonic sensor, the internal defect characteristic parameters of the part are extracted, such as the defect location, size, shape, etc. The part surface deviation parameters and the internal defect characteristic parameters are integrated to form a complete set of part process detection characteristic parameters.

[0039] The part production optimization control module 50 is used to compensate and optimize process parameters based on the part process detection characteristic parameters, obtain part production process optimization parameters, and use these part production process optimization parameters to optimize the production of the target part. Specifically, based on the part process detection characteristic parameters, manufacturing process parameters (such as temperature, pressure, and time) are compensated and optimized to reduce deviations and defects during the part manufacturing process. Based on the compensated and optimized process parameters, the part production process optimization parameters are generated. The production process optimization parameters are applied to the production process of the target part, and production optimization control is achieved by adjusting parameters such as machinery and process conditions. The embodiment of the present application utilizes an industrial camera and a three-dimensional scanner to simultaneously collect image information and laser point cloud information of a part, spatially aligns and fuses the image information and laser point cloud information of the part, generates a target part space model, and performs multi-dimensional loss analysis with the standard part space model to obtain the surface deviation parameters of the part, obtains the internal defect characteristic parameters of the part through ultrasonic sensor detection, obtains the part process detection characteristic parameters in combination with the part surface deviation parameters, performs process parameter compensation optimization based on the part process detection characteristic parameters, obtains the part production process optimization parameters, and uses these parameters to perform production optimization control on the target part, thereby achieving the technical effect of efficient and accurate detection of parts, adaptive optimization of production processes, and improving product process quality.

[0040] In one possible implementation, obtaining the part production process optimization parameters includes: establishing a part process feature simulation model, performing process control compensation analysis on the part process detection feature parameters based on the part process feature simulation model, and obtaining a process control parameter selection threshold; randomly generating multiple process control parameters based on the process control parameter selection threshold, and performing simulation evaluation on the multiple process control parameters through the part process feature simulation model to obtain multiple part process feature prediction qualities; clustering, expanding and optimizing the multiple process control parameters according to the multiple part process feature prediction qualities to obtain the part production process optimization parameters.

[0041] Specifically, to accurately simulate the part production process and its results, a simulation model is established that reflects the relationship between part process characteristics and production conditions. By collecting historical production data, process parameters, and part quality data, and using simulation tools based on the characteristics of the part production process, the simulation model structure is designed. The collected data is input into the model, and the model's initial parameters are set. The model's accuracy and reliability are verified by comparing it with actual production data. Considering process control parameter deviations caused by factors such as equipment errors, a compensation analysis is performed on the part process detection characteristic parameters. Specifically, the impact of factors such as equipment errors and process fluctuations on the process control parameters is analyzed. Based on the analysis results, a compensation strategy is determined, such as adjusting the range of process control parameters or introducing correction factors. Based on the compensation strategy, a reasonable parameter value range or limit is determined within the process control parameter value range, taking into account factors such as equipment errors, to ensure that the compensated process control parameters meet the part process requirements.

[0042] Based on the set threshold, the value range of the process control parameters is determined. Within the value range, a random number generator is used to generate multiple process control parameter combinations. The generated process control parameter combinations are input into the part process feature simulation model. The simulation model is run to simulate the part production process, and the simulation results, including the part process feature prediction quality, are collected. A clustering algorithm is used to cluster the simulation results, and similar process control parameter combinations are grouped together. The average process feature prediction quality of each type of process control parameter combination is calculated. Based on the average quality, the optimal or relatively optimal process control parameter combination is selected. Further search and optimization are performed near the optimal parameters to obtain more accurate optimization parameters as the part production process optimization parameters. This implementation method achieves accurate evaluation and optimization of process control parameters by establishing a part process feature simulation model and simulating the part production process and its results.

[0043] In one possible implementation, the establishment of a part process feature simulation model includes: constructing a production process parameter space for the target part through data mining technology, wherein the production process parameter space includes production process control parameters and corresponding part process detection feature data; performing multi-dimensional index classification on the part process detection feature data according to the part process requirement standard to obtain a part process feature index classification data set; correlating and fitting the production process control parameters with the part process feature index classification data set respectively to obtain a process control parameter-process index feature model set; performing equal-weighted simulation fusion on each associated model in the process control parameter-process index feature model set to establish the part process feature simulation model.

[0044] Specifically, historical data related to the production of target parts is collected from sources such as production records and quality inspection reports. The collected data is preprocessed through cleaning, deduplication, and normalization to ensure data quality and consistency. Data mining techniques (such as cluster analysis and association rule mining) are used to process the preprocessed data, constructing a production process parameter space that includes production process control parameters (such as temperature, pressure, and time) and corresponding part process inspection feature data (such as size, hardness, and surface roughness). Based on the specific requirements set for various process indicators in the part production process (such as dimensional accuracy and material properties), the specific standards and dimensions for multi-dimensional (i.e., multiple process inspection feature) indicator classification are determined. Classification algorithms (such as decision trees and support vector machines) are then used to classify the part process inspection feature data, resulting in a classification dataset for part process feature indicators. Utilizing correlation fitting methods (such as linear regression, nonlinear regression, and neural networks), the production process control parameters are correlated with a classified dataset of part process characteristic indicators to train a set of process control parameter-process indicator characteristic models. This set is a group of models, each describing the correlation between a specific process control parameter and a corresponding process indicator characteristic. Each correlation model in the set is fused with equal weights to produce a comprehensive simulation model, the part process characteristic simulation model, which can predict part process characteristics based on the production process control parameters. This implementation method constructs a production process parameter space through data mining technology, fully utilizing information from historical data. By fusing the prediction results of multiple models, it leverages the strengths of each model, reduces potential deviations and errors in individual models, and improves overall prediction accuracy.

[0045] In one possible implementation, obtaining the part production process optimization parameters includes: optimizing the multiple process control parameters according to the predicted quality of the multiple part process features to obtain multiple parent process control parameters with preset proportions; clustering the multiple process control parameters using the multiple parent process control parameters as cluster centers to obtain multiple process control parameter clusters; expanding and optimizing the multiple process control parameter clusters based on the multiple parent process control parameters to obtain multiple process control optimization parameter clusters; performing global comparison and optimization based on the multiple process control optimization parameter clusters to output the part production process optimization parameters.

[0046] Specifically, based on the prediction quality of multiple part process characteristics (these qualities are evaluated by simulating and evaluating randomly generated process control parameters using a part process characteristic simulation model), multiple process control parameters are optimized, selecting those with high prediction quality. For example, the top 10% or 20% of excellent parameters are selected as parent parameters. These optimized parent process control parameters are used as cluster centers, and the remaining process control parameters are clustered, grouping similar parameters together to form multiple process control parameter clusters. Within each cluster, the parameters at the cluster center are expanded and optimized. This includes adding new parameters to the cluster and fine-tuning the cluster center parameters to explore possible optimal parameter combinations within the cluster. After all clusters are expanded and optimized, a global comparison is performed on all the optimized process control parameter clusters to identify the parameter combination with the best prediction quality across all clusters. This combination is then determined as the optimized parameters for the part production process. This implementation method quickly narrows the search scope through optimization and clustering operations, reduces the number of process control parameter combinations that need to be evaluated, and improves the efficiency of the optimization process. By performing expanded optimization within each cluster, it fully utilizes the similarity of parameters within the cluster. At the same time, global comparison ensures that the final selected parameter combination is the best among all possible options, enhancing the optimization effect.

[0047] In one possible implementation, obtaining multiple process control optimization parameter clusters includes: performing preset intra-cluster distance parameter screening within the multiple process control parameter clusters based on the multiple parent process control parameters to obtain multiple intra-cluster adjacent control parameters; cross-combining and parameter mutating the multiple parent process control parameters and the multiple intra-cluster adjacent control parameters to obtain multiple child process control parameters; expanding the multiple process control parameter clusters with child parameters based on the multiple child process control parameters to obtain multiple process control expansion parameter clusters; and performing iterative optimization and updating within the multiple process control expansion parameter clusters to obtain the multiple process control optimization parameter clusters.

[0048] Specifically, within each process control parameter cluster, the distance between each parent parameter and other parameters is calculated (this distance can be Euclidean, Manhattan, or other distances, depending on the parameter dimensionality and properties). Parameters with close proximity to each parent parameter are screened based on a preset distance threshold. These selected parameters are the intra-cluster neighboring control parameters. Parent parameters are randomly selected with intra-cluster neighboring parameters, and a portion of their genes (i.e., a portion of the parameters) is exchanged to generate new offspring parameters. These offspring parameters are then subjected to small, random or probabilistic adjustments to introduce new variation and increase solution diversity. These offspring parameters are then added to their corresponding process control parameter clusters. This results in each cluster containing more parameters, forming an expanded process control parameter cluster. The above steps are repeated (starting with the preset intra-cluster distance parameter screening), but each iteration is based on the currently optimal or near-optimal process control parameter cluster. In each iteration, the process control parameter cluster is updated based on the new offspring parameters, and a global comparison is performed again to optimize the process control parameter cluster. This iterative process continues until a stopping condition is met (e.g., the number of iterations or performance improvement less than a threshold). Through iterative optimization and updating, the quality of the process control parameter cluster is continuously improved until the optimal or near-optimal process control optimization parameter cluster is found. This implementation adopts the concept of genetic algorithms. Through crossover, parameter mutation, and iterative optimization, it enhances global search capabilities and increases the diversity of solutions, helping to find better solutions during the search process and avoiding premature convergence.

[0049] In the above, refer to Figure 1 A device for detecting process parameters of a part according to an embodiment of the present invention is described in detail. Figure 2 A method for detecting process parameters of a part according to an embodiment of the present invention is described.

[0050] A method for detecting process parameters of a part according to an embodiment of the present invention is used to solve the technical problems of insufficient detection accuracy and low detection efficiency in existing part process parameter detection, thereby achieving the technical effects of efficient and accurate detection of parts, optimizing the adaptability of production processes, and improving product process quality.

[0051] A method for detecting process parameters of a part includes: acquiring an image of a target part through an industrial camera to obtain part image information, and simultaneously acquiring part laser point cloud information through a three-dimensional scanner; spatially aligning and fusing the part image information and the part laser point cloud information to generate a target part space model, and simultaneously obtaining a standard part space model based on the drawing information of the target part; performing multi-dimensional loss analysis on the target part space model and the standard part space model to obtain part surface deviation parameters; acquiring internal defect characteristic parameters of the target part through ultrasonic sensor detection, and acquiring part process detection characteristic parameters based on the part surface deviation parameters and the internal defect characteristic parameters; performing process parameter compensation optimization based on the part process detection characteristic parameters to obtain part production process optimization parameters, and performing production optimization control on the target part through the part production process optimization parameters.

[0052] Among them, the generating of the target part space model may further include: calibrating the industrial camera and the three-dimensional scanner respectively to determine the camera calibration parameters and the scanner calibration parameters; performing joint calibration based on the camera calibration parameters and the scanner calibration parameters to establish an image pixel-laser point cloud mapping relationship; extracting key feature points of the part image information and the part laser point cloud information in turn to obtain an image feature point set and a point cloud feature point set; performing feature point matching on the image feature point set and the point cloud feature point set according to the image pixel-laser point cloud mapping relationship to obtain part feature point matching information; performing data fusion and three-dimensional reconstruction on the part image information and the part laser point cloud information based on the part feature point matching information to generate the target part space model.

[0053] Among them, the obtaining of part surface deviation parameters can further include: building a feature twin network according to the part process requirement standard, the feature twin network including a standard twin network and a target twin network, and the standard twin network and the target twin network are shared weight networks; based on the standard twin network and the target twin network, multi-dimensional feature extraction is performed on the standard part space model and the target part space model respectively, and a standard part feature set and a target part feature set are output; a feature loss function is introduced to perform loss analysis on the standard part feature set and the target part feature set to obtain the part surface deviation parameters.

[0054] Among them, the obtaining of part production process optimization parameters may further include: establishing a part process feature simulation model, performing process control compensation analysis on the part process detection feature parameters based on the part process feature simulation model, and obtaining a process control parameter selection threshold; randomly generating multiple process control parameters based on the process control parameter selection threshold, and performing simulation evaluation on the multiple process control parameters through the part process feature simulation model to obtain multiple part process feature prediction qualities; clustering, expanding and optimizing the multiple process control parameters according to the multiple part process feature prediction qualities to obtain the part production process optimization parameters.

[0055] Among them, the establishment of the part process feature simulation model may further include: constructing the production process parameter space of the target part through data mining technology, the production process parameter space includes production process control parameters and corresponding part process detection feature data; performing multi-dimensional index classification on the part process detection feature data according to the part process requirement standard to obtain a part process feature index classification data set; correlating and fitting the production process control parameters with the part process feature index classification data set respectively to obtain a process control parameter-process index feature model set; performing equal-weight simulation fusion on each associated model in the process control parameter-process index feature model set to establish the part process feature simulation model.

[0056] Among them, obtaining the part production process optimization parameters may further include: optimizing the multiple process control parameters according to the predicted quality of the multiple part process characteristics to obtain multiple parent process control parameters with preset proportions; clustering the multiple process control parameters with the multiple parent process control parameters as cluster centers to obtain multiple process control parameter clusters; expanding and optimizing the multiple process control parameter clusters based on the multiple parent process control parameters to obtain multiple process control optimization parameter clusters; performing global comparison and optimization based on the multiple process control optimization parameter clusters to output the part production process optimization parameters.

[0057] Wherein, obtaining a plurality of process control optimization parameter clusters may further include:

[0058] Based on the multiple parent process control parameters, preset intra-cluster distance parameters are screened within the multiple process control parameter clusters to obtain multiple intra-cluster adjacent control parameters; the multiple parent process control parameters and the multiple intra-cluster adjacent control parameters are cross-combined and parameter mutated to obtain multiple child process control parameters; based on the multiple child process control parameters, the multiple process control parameter clusters are expanded with child parameters to obtain multiple process control expanded parameter clusters; and iterative optimization and updating are performed within the multiple process control expanded parameter clusters to obtain the multiple process control optimized parameter clusters.

[0059] A device for detecting process parameters of a part provided by an embodiment of the present invention can execute a method for detecting process parameters of a part provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0060] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0061] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A device for detecting process parameters of a part, characterized in that: The device comprises: A part information acquisition module is used to acquire images of target parts using an industrial camera to obtain part image information, and simultaneously acquire part laser point cloud information using a 3D scanner; A part space model acquisition module is used to perform spatial registration and fusion of the part image information and the part laser point cloud information to generate a target part space model, and simultaneously obtain a standard part space model based on the drawing information of the target part; A multidimensional loss analysis module, configured to perform multidimensional loss analysis on the target part space model and the standard part space model to obtain part surface deviation parameters; A part process detection characteristic parameter acquisition module, the part process detection characteristic parameter acquisition module is used to obtain the internal defect characteristic parameters of the target part through ultrasonic sensor detection, and obtain the part process detection characteristic parameters based on the part surface deviation parameters and the internal defect characteristic parameters; A parts production optimization control module, the parts production optimization control module is used to perform process parameter compensation optimization based on the parts process detection characteristic parameters, obtain parts production process optimization parameters, and perform production optimization control on the target parts using the parts production process optimization parameters; The part production process optimization parameters are obtained, including: Establishing a part process feature simulation model, performing process control compensation analysis on the part process detection feature parameters based on the part process feature simulation model, and obtaining a process control parameter selection threshold; Randomly generate multiple process control parameters based on the process control parameter selection threshold, and simulate and evaluate the multiple process control parameters through the part process feature simulation model to obtain multiple part process feature prediction qualities; Clustering, expanding, and optimizing the multiple process control parameters according to the predicted qualities of the multiple part process characteristics to obtain the optimized parameters of the part production process; The establishment of the part process feature simulation model includes: Constructing a production process parameter space of the target part by using data mining technology, wherein the production process parameter space includes production process control parameters and corresponding part process detection feature data; Performing multi-dimensional index classification on the part process detection feature data according to the part process requirement standard to obtain a part process feature index classification data set; Correlation fitting is performed between the production process control parameters and the part process characteristic index classification data set to obtain a process control parameter-process index characteristic model set; Each associated model in the process control parameter-process indicator feature model set is subjected to equal-weight simulation fusion to establish the part process feature simulation model.

2. A device for detecting process parameters of a part according to claim 1, characterized in that: Generating the target part space model includes: Calibrate the industrial camera and the three-dimensional scanner respectively to determine camera calibration parameters and scanner calibration parameters; Performing joint calibration based on the camera calibration parameters and the scanner calibration parameters to establish an image pixel-laser point cloud mapping relationship; Extracting key feature points from the part image information and the part laser point cloud information in sequence to obtain an image feature point set and a point cloud feature point set; Performing feature point matching on the image feature point set and the point cloud feature point set according to the image pixel-laser point cloud mapping relationship to obtain part feature point matching information; Based on the part feature point matching information, the part image information and the part laser point cloud information are subjected to data fusion and three-dimensional reconstruction to generate the target part space model.

3. A device for detecting process parameters of a part according to claim 1, characterized in that: The obtaining of part surface deviation parameters includes: According to the part process requirement standards, a feature twin network is built, wherein the feature twin network includes a standard twin network and a target twin network, and the standard twin network and the target twin network are shared weight networks; Based on the standard twin network and the target twin network, multi-dimensional feature extraction is performed on the standard part space model and the target part space model respectively, and a standard part feature set and a target part feature set are output; A feature loss function is introduced to perform loss analysis on the standard part feature set and the target part feature set to obtain the part surface deviation parameters.

4. A device for detecting process parameters of a part according to claim 1, characterized in that: The obtaining of the optimized parameters of the parts production process includes: Optimizing the plurality of process control parameters according to the predicted qualities of the plurality of part process features to obtain a plurality of parent process control parameters of a preset proportion; Taking the multiple parent process control parameters as cluster centers respectively, clustering the multiple process control parameters to obtain multiple process control parameter clusters; Based on the multiple parent process control parameters, expansion optimization is performed on the multiple process control parameter clusters to obtain multiple process control optimization parameter clusters; A global comparison and optimization is performed based on the multiple process control optimization parameter clusters to output the part production process optimization parameters.

5. A device for detecting process parameters of a part according to claim 4, characterized in that: The obtaining of multiple process control optimization parameter clusters includes: Performing a preset intra-cluster distance parameter screening within the plurality of process control parameter clusters based on the plurality of parent process control parameters to obtain a plurality of intra-cluster neighboring control parameters; Performing cross-combination and parameter mutation on the plurality of parent-generation process control parameters and the plurality of adjacent control parameters within the cluster to obtain a plurality of child-generation process control parameters; performing child parameter expansion on the plurality of process control parameter clusters based on the plurality of child process control parameters to obtain a plurality of process control expansion parameter clusters; Iterative optimization and updating are performed within the plurality of process control expansion parameter clusters to obtain the plurality of process control optimization parameter clusters.

6. A method for detecting process parameters of a part, characterized in that: The method is implemented by a part process parameter detection device according to any one of claims 1 to 5, and the method includes: The target part is imaged by an industrial camera to obtain part image information, and the part laser point cloud information is obtained by a 3D scanner. Performing spatial registration and fusion of the part image information and the part laser point cloud information to generate a target part space model, and at the same time obtaining a standard part space model based on the drawing information of the target part; Performing multi-dimensional loss analysis on the target part space model and the standard part space model to obtain part surface deviation parameters; Acquire internal defect characteristic parameters of the target part through ultrasonic sensor detection, and acquire part process detection characteristic parameters based on the part surface deviation parameters and the internal defect characteristic parameters; Based on the part process detection characteristic parameters, process parameter compensation optimization is performed to obtain part production process optimization parameters, and production optimization control of the target part is performed using the part production process optimization parameters.

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