MBD-based optical element practical model construction method, equipment and medium

Abstract

The invention relates to the technical field of optical element processing, and discloses an MBD-based optical element practical model construction method and device and a medium, and the method comprises the steps: generating an optical element practical model processing structure frame, constructing an MBD model of an optical element, and designing a process and a process execution result. Identifying feature information of the optical element, and calling an API function to extract attribute information of each feature; traversing PMI information of the MBD model, and systematically arranging the attribute information and the PMI information to generate a processing feature information set; and processing the optical element based on the processing feature information set, obtaining a process execution result, and optimizing the practical model. According to the optical element practical model construction method based on MBD provided by the invention, the process design information, the process execution information and the processing characteristics of the optical element can be quickly obtained in a virtual environment, and the attribute relationship between the processing process and the processing characteristics is reasonably expressed, so that the processing efficiency of the optical element is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical element processing, and in particular to a method, device, and medium for constructing an optical element implementation model based on MBD. Background Art

[0002] Currently, research on ontological models in the field of optical processing is relatively scarce. From the perspective of the optical processing implementation process, a large amount of structured data is generated during both the process design and specific execution stages. During the process design phase, the hierarchical relationships of this process-related data vary slightly between different processes, and the differences mainly occur in the structured process data instances. Currently, process designers lack the conditions to promptly and accurately locate the grinding wheels, gauges, tooling, and machine tools required for each process step within existing process knowledge bases and resource libraries. Summary of the Invention

[0003] In view of this, the present invention provides a method, device and medium for constructing an optical element implementation model based on MBD to solve the above problems.

[0004] To solve the above technical problems, the present invention provides a method for constructing an optical element implementation model based on MBD, comprising:

[0005] Generate the processing structure framework of the optical component implementation model, and build the MBD model, design process and process execution results of the optical component;

[0006] Identify the feature information of the optical component and call the API function to extract the attribute information of each feature; traverse the PMI information of the MBD model, systematically organize the attribute information and PMI information to generate a processing feature information set;

[0007] Based on the processing feature information set, the optical components are processed and the process execution results are obtained, and the implementation model is optimized.

[0008] As an optional method, build the optical component implementation model processing structure framework and design the MBD model of the optical component, including:

[0009] Perform structural analysis on optical components, determine the implementation model, MBD model, and MBD process model of the optical components, and establish the relationship between the implementation model, MBD model, and MBD process model;

[0010] Among them, the MBD body model includes geometric features, material features, surface features, and service features; the MBD process model consists of annotation information, process attribute information, and three-dimensional models corresponding to parts at different production stages.

[0011] As an option, build a design process that includes:

[0012] Assign process information and step information to optical components through information modeling;

[0013] Among them, process information includes process number, process name, process content, process inspection, whether it is a critical process, whether it is a special process and whether it is sent for standard inspection; work step information includes work step sequence number, work step number, work step name, work step content and processing requirements, whether it is a critical process step and precautions and remarks.

[0014] As an optional method, the build process execution results include:

[0015] Through information modeling, optical components are given product information, process master information, process execution information and step execution results during actual processing;

[0016] Among them, product information includes product classification, product name, drawing number and product version; process main information includes process number, process code, process name and process version; process execution information includes execution process number, execution process name, execution process content, whether process inspection is executed, whether key process is executed, whether special process is executed and whether standard inspection is executed; work step execution results include execution work step sequence number, execution work step number, execution work step name, whether key process is executed and whether precautions and remarks are executed, process inspection information and work step inspection results.

[0017] As an optional method, PMI information includes design information, basic attribute information and processing technology information; design information refers to PMI information directly associated with the geometric features of the three-dimensional model; basic attribute information is a general description of the part; processing technology information is the PMI information generated by the part during the production and manufacturing stage.

[0018] As an optional method, the characteristic information for identifying the optical component includes:

[0019] A processing feature dataset is constructed, and a three-dimensional model of the optical component is constructed using computer software and then converted into a corresponding two-dimensional processing feature image. After the two-dimensional processing feature image is annotated, the processing features are extracted through the target detection model.

[0020] As an optional method, systematically organizing attribute information to generate a processing feature information set includes:

[0021] The identified features are classified to obtain image classification and text classification, and a processing feature information set is generated through systematic processing. The processing feature information set is used to express the mapping between processing features and PMI information.

[0022] As an optional method, optimizing the implementation model includes optimizing the implementation model based on the process execution results and the nearest neighbor algorithm to match the different information conditions in the processing process, including:

[0023] The nearest neighbor algorithm is used to obtain the distance information between the processing feature and the part material, the distance information between the main dimension and the characteristic dimension during the processing process, the root mean square value, peak-to-valley value and surface roughness of the surface accuracy, and the upper and lower tolerances of the main dimension;

[0024] Set up target layer, criterion layer and solution layer. The target layer is used to determine the processing method; the criterion layer is used to reflect the key information that affects the selection of the processing method, including geometric features, material indicators, surface indicators, surface features and service features; the solution layer includes the processing methods in the knowledge base;

[0025] A judgment matrix of characteristic attributes is constructed, and the characteristic attributes of the information are scaled according to the definition of the judgment matrix scale to obtain the final judgment matrix. After the final judgment matrix is normalized, the weight of each key information feature is obtained; after introducing the random consistency index, the consistency ratio is calculated, and the comprehensive distance measurement value is calculated by weighted summation to evaluate the similarity between the information to be matched and the process knowledge in the knowledge base, and based on the similarity, the processing method with the highest similarity is screened out.

[0026] On the other hand, the present invention further provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned method for constructing an optical element implementation model based on MBD when executing the computer program.

[0027] On the other hand, the present invention further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for constructing an optical element implementation model based on MBD are implemented.

[0028] The beneficial effects of the present invention are:

[0029] The present invention provides an MBD-based optical element implementation model construction method, which can quickly obtain optical element process design information, process execution information and processing characteristics in a virtual environment, reasonably express the attribute relationship between the processing technology and processing characteristics, and significantly improve the processing efficiency of optical elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a machining process based on MBD provided in an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of the processing structure framework of an optical element implementation model provided by an embodiment of the present invention;

[0032] Figure 3 A schematic diagram illustrating the association between the process structure tree and the MBD process model provided in an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of the hierarchical relationship between related models in the optical element manufacturing process provided by an embodiment of the present invention;

[0034] Figure 5 A schematic diagram of MBD ontology model information provided by an embodiment of the present invention;

[0035] Figure 6 A schematic diagram of the classification of optical element process information provided by an embodiment of the present invention;

[0036] Figure 7 Schematic diagram of the hierarchical structure of optical element processing information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods.

[0038] See also Figure 1-Figure 7 This embodiment provides a method for constructing an optical element implementation model based on MBD, including: generating a processing structure framework of the optical element implementation model, constructing the MBD model, design process and process execution results of the optical element; identifying the feature information of the optical element, calling the API function to extract the attribute information of each feature; traversing the PMI information of the MBD model, systematically organizing the attribute information and PMI information to generate a processing feature information set; processing the optical element based on the processing feature information set and obtaining the process execution results, and optimizing the implementation model.

[0039] Specifically, this embodiment aims to address the current lack of optical component processing technology implementation models for process reasoning, component quality and efficiency correlation analysis, and other research issues. By studying the relationship and structure design methods of implementation models, semi-structured and unstructured data feature element extraction methods, model data construction and update methods, and implementation model optimization analysis methods, the above methods are provided to achieve dynamic updating of optical component processing technology implementation models and iterative optimization of ontology models. Based on the above methods, in one implementation scenario, this embodiment is implemented as follows:

[0040] Step 1: Analyze the implementation model of the optical element in detail, determine the implementation model construction framework, and clearly describe the relationship between process design, MBD body model, processing features (geometric features, material features, surface features, and service features of the optical element) and process execution results, such as Figure 2As shown, the relationship between the process structure and the MBD ontology model is as follows Figure 3 shown.

[0041] Step 2: Quickly analyze the construction principle of the optical component process implementation model, and clearly describe the model's structural information, attribute information, relationship information, and the model hierarchical relationship of each stage of product manufacturing, such as Figure 4 As shown in Figure 1. In this step, the design model represents the target state of CNC machining, while the blank represents the initial state of CNC machining. The final result of each process primarily presents the evolution of geometric elements and their true form, which is also the structural information expressed by the implementation model. Other attribute information (such as processing resources and manufacturing quality) is added based on this structural information. Therefore, the construction of an optical component implementation model is divided into three levels: structural information, attribute information, and relationship information. Structural information is the foundation, while attribute information depends on the structure.

[0042] This embodiment applies some basic object-oriented concepts and methods to construct an optical element implementation model. Complex systems are composed of interconnected subsystems, which in turn are composed of their own subsystems. To model a complex system, it can be viewed as "a layer" and "part of a layer." Object-oriented methods define the notation and process for building complex software systems. The mature development of software engineering has led to the development of object-oriented analysis, design, and programming methods. Object-oriented programming emphasizes data abstraction, encapsulation, and inheritance, and its basic components are classes and objects.

[0043] According to the object-oriented thinking method, in the stage of building the optical component implementation model, we regard the design model as a "metaclass", the process model and the inspection model as "classes" under the metaclass, and the optical component implementation model as an "object". In this way, the metaclass-class-object can directly have association, inheritance, aggregation, and usage relationships. The modeling process of the optical component implementation model can be regarded as a process from "generalization" to "specialization". The implementation model of the process state (process) is the "instance" corresponding to the process model, and the implementation model of the final state is the "instance" corresponding to the design model. In this way, the model hierarchy relationship of each stage of optical component manufacturing is as follows: Figure 4 As shown in the figure, the "detection data", "collection data" and "manufacturing data" at the data level are obtained with the support of the data acquisition system. The data acquisition system involves hardware and software and is a functional system specially developed or configured for digital intelligent workshops. It is only used as an independent tool in the process of establishing the optical component implementation model.

[0044] The source of structural information is the process model, which is the main data set that ensures the uniqueness of the data source. It mainly includes geometric data such as the geometric structure, reference plane, coordinate system, form and position tolerances of optical components; attribute data is descriptive data for the manufacturing process, including blank information, manufacturing process information, processing resource information, quality information, etc.; relationship information is an index that describes the relationship between different types of data sets, such as the relationship between physical geometric information in detection data and geometric elements in structural information, the relationship between raw material information and blank information, the relationship between processing equipment information and processing resources, etc. It is a special type of attribute data.

[0045] The first thing to be faced in the construction of the optical element implementation model is the reconstruction of the geometric elements, that is, to modify the corresponding geometric elements according to the actual state of the processed optical element (test data). This process can actually be considered as the instantiation process of the geometric elements in the designed theoretical model; similarly, the attribute information is also an instantiation process based on the test data and sensor data; the relationship information is based on the determined logical structure of the optical element implementation model, giving the association relationship index, which is a specific type of attribute data.

[0046] Step 3: Quickly assign the optical element MBD ontology model information to realize the construction of the MBD ontology model. The optical element MBD ontology model instance is not limited in this embodiment. Among them, the MBD model mainly includes geometric information, annotation information, labeling information and other definition information, such as Figure 5 In this step, the MBD-based part process model can effectively solve the problem of poor correlation between process information and three-dimensional models in the design and processing of optical components. Directly applying the MBD process model to guide the subsequent production and processing of parts can greatly shorten the production cycle and improve production efficiency. This embodiment defines the MBD-based part process model Mtm as follows:

[0047] Mtm={Mg,Am,Ap}

[0048] Where Mg represents the 3D model of the part's MBD process model, including a 3D model that reflects the part's shape and function, as well as a 3D model that reflects the material removed from the part during the various stages of the manufacturing process. Am represents annotation information associated with the part's 3D model, such as basic dimensions, tolerances, surface accuracy, and surface roughness. Ap represents the part's process attribute information, which includes basic process information such as the part's name, material, and quantity, as well as processing information related to the part's manufacturing process, such as cutting tools, machine tools, fixtures, processing methods, and processing parameters.

[0049] Since the manufacturing process of a part is a process of obtaining a product part by continuously removing materials from the blank according to the designed processing route, the MBD process model of the part can well show the changes in this process from a geometric perspective. Therefore, the three-dimensional model Mg of the MBD process model of the part can be defined as:

[0050] Mg={Md,Mr,Mp1,Mp2,……,Mpn}

[0051] Where Md represents the design model that reflects the three-dimensional shape and function of the product part. Mr represents the blank model that reflects the three-dimensional shape and function of the part being machined. Mpi represents the process model corresponding to the remaining portion of the part after material is removed through the i-th machining process. It represents the number of machining processes included in the part manufacturing process.

[0052] According to the definition of the MBD-based part process model, the part MBD process model is composed of annotation information, process attribute information and the three-dimensional models corresponding to the part at different production stages.

[0053] Step 4: Construct a processing feature data set, use computer software to construct a three-dimensional model of the optical element and convert it into a corresponding two-dimensional processing feature image; after annotating the two-dimensional processing feature image, extract the processing features through the target detection model. As an optional method, this embodiment uses the MBD ontology model and the YOLOv5 algorithm to extract processing information and process feature elements and identify processing features, and complete the structured management of processing feature information. The process information classification of optical elements is as follows: Figure 6 In one scenario, the implementation process is as follows:

[0054] This example uses the UG platform and the UG / OpenAPI interface to automatically extract PMI information from MBD models. We develop corresponding information extraction modules for different types of PMI information (including dimensional tolerances, geometric tolerances, surface requirements, and service requirements). All extracted PMI information is stored in TXT format.

[0055] In order to better manage the process information of parts, it is necessary to classify the process information of parts. Based on the different expressions of process information, this paper divides the process information stored in the MBD process model into: design information, basic attribute information and processing technology information.

[0056] Design information refers to process information directly associated with the geometric features of a 3D model, such as dimensions, tolerances, surface accuracy, roughness, surface shape error, and damage threshold. This information can be directly annotated on the 3D model. Design information is composed of design information for the design model, the rough model, and the process model, and serves as the basis for all model building.

[0057] Basic attribute information is a general description of the part, such as the part's name, material, quantity, number, etc. It has no direct relationship with the geometric features of the model, so it generally does not need to be directly marked on the three-dimensional model. This embodiment associates the basic attribute information with the design model in the form of an attribute information table.

[0058] Machining process information is generated during the manufacturing phase of a part. It includes tool attributes such as the machine tool, cutting tool, and fixture used in the machining process, as well as machining parameters such as spindle speed, feed rate, and back-cut depth. Machining process information is directly linked to the part's manufacturing process and guides the manufacturing process. Therefore, this article associates this information with the corresponding process model in the form of information tables and text files, making it easier for process personnel to directly retrieve and view it.

[0059] At the same time, this embodiment adopts a processing feature recognition method based on the YOLOv5 algorithm, taking advantage of the algorithm's efficient feature extraction and real-time advantages, and using the MBD three-dimensional model of optical components to accurately identify processing features. By achieving information exchange between CAD and CAPP systems, it breaks down data barriers and provides technical support for intelligent manufacturing.

[0060] First, a processing feature dataset was constructed. Based on the principle of processing feature classification, a three-dimensional model of the optical component was constructed using UG three-dimensional design software. The three-dimensional model was converted into its corresponding two-dimensional processing feature image using the screenshot method, ensuring that the image could reflect the geometric form of the processing feature. In order to improve the diversity and adaptability of the dataset, a multi-angle and multi-level method was adopted when taking screenshots. These operations increased the richness of the dataset. After these processed image sets, a preliminary dataset containing diverse processing features was formed. Then, in order to make the dataset meet the training requirements of the deep learning model, LabelImg software was used to annotate each processing feature image. The annotation results of each image will be automatically saved as a TXT format file. These files contain the specific location information and category labels of the processing features in the image. Finally, through this series of steps, the construction and preprocessing of the processing feature dataset were completed.

[0061] Secondly, a processing feature identifier based on the YOLOv5 algorithm is constructed. In order to achieve efficient and accurate processing feature recognition, it is necessary to construct a processing feature recognition classifier based on the YOLOv5 algorithm. The above-mentioned pre-processed processing feature data set is input into the constructed YOLOv5 model as the data source for network training. Subsequently, through multiple rounds of training and parameter optimization, the network structure and hyperparameters are continuously adjusted to improve the recognition accuracy and training efficiency of the model. During the training process, the algorithm extracts the key feature points of the processing features from a large number of samples through a learning mechanism, and can gradually adjust the weight values in the network, thereby achieving accurate recognition of different processing features. After sufficient training and verification, a YOLOv5 deep neural network model with optimal performance and the highest recognition accuracy is obtained. Finally, the trained YOLOv5 model is encapsulated and deployed in the CAPP system developed in this embodiment.

[0062] Finally, machining feature recognition is performed on the 3D model of the optical component. Within the CAD system, UG secondary development technology is used to preprocess the 3D model of the optical component and convert it into image and video files. This ensures that various machining features can be effectively extracted and identified during subsequent processing. The generated image and video files are then transferred to the CAPP system. Within the CAPP system, machining feature recognition is performed on the input image and video files using a pre-built machining feature classifier. Based on a pre-trained deep learning model, the machining feature classifier automatically identifies various machining features contained in the optical component, such as planes, spherical surfaces, and aspherical surfaces. After recognition is complete, the system outputs the machining feature recognition results in two formats: an image format, where the identified machining features are visually displayed within the optical component image as bounding boxes, making them easy for designers and engineers to quickly understand and review; and a text format, where the system lists the identified machining features in text format, facilitating subsequent process design and data archiving.

[0063] After completing processing feature recognition and PMI information extraction, since the two types of information come from different processing flows, the processing features and their corresponding PMI information are separated and cannot be directly associated. To achieve efficient application of information, this embodiment systematically integrates and processes the originally scattered and disorganized data into a detailed information set based on processing features. This integrated information set is called the Processed Feature Information Set (PFIS). It provides effective data support for intelligent process design by accurately mapping processing features and PMI information. It can be described by the following formula:

[0064] PFIS={T, D1, D2, D3, PV, T u , T l}

[0065] Where T is the type of processing feature, D1 is the first dimension information of the processing feature (such as the length of the optical element), D2 is the second dimension information (such as the width of the optical element), D3 is the third dimension information (such as the height of the optical element), PV is the surface error, T u is the upper tolerance, T l The upper and lower tolerances are the tolerance information of the main dimensions of the processing features.

[0066] The core task of the machining feature information set is to associate each machining feature with its corresponding PMI information. This organized information becomes more organized and clearly reflects information such as the dimensional requirements, tolerance standards, and surface roughness of each machining feature. This not only helps improve data readability but also allows this information to be directly applied to subsequent process design. In intelligent process design, in particular, using these structured data sets, designers can determine the machining process and select appropriate machining equipment based on specific machining feature types, thereby providing data support for the entire production process.

[0067] The processing feature information management page uses a visual interface design, allowing users to import, edit, delete, add, and save information in the PFIS. This page lists the PMI information for each feature in a table format, enabling information display and management. Through this systematic information integration and management approach, this embodiment successfully transforms raw data into structured data that can be used for intelligent process design, providing reliable technical support for the digital and intelligent transformation of manufacturing processes.

[0068] Step 5: Quickly assign each process information and step information of the optical component to realize the construction of the design process model.

[0069] Step 6: Rapidly assign product information, process master information, process execution information, and step execution results during the actual optical component processing process. Complete the ontology modeling of the actual geometric and non-geometric data, respectively, to construct the optical component process execution model. Based on the relationships between the MBD ontology model, design process information model, processing features, and process execution model in Step 1, these models are integrated to complete the construction of the optical component process execution model. Because actual data is stored in electronic or even paper form in media such as manufacturing execution systems, enterprise resource planning systems, or MAS systems, and because engineers focus more on pass rates than specific data during product delivery, actual data is easily overlooked. Based on the understanding of the data dimension of digital twins and referring to machining manuals, this embodiment defines actual data as follows: "Actual data" refers to the collection of actual geometric data, actual surface shape data, actual surface features, actual usage data, actual process parameters, actual manufacturing resource data, and other necessary data after part processing.

[0070] The ontology of an optical element can describe geometric information and non-geometric information in a unified syntax. However, the ontology modeling process is highly subjective, making its results lack authority and professionalism. To this end, this embodiment introduces the STEP / STEP-NC standard, which provides an authoritative and professional high-level information model for describing physical objects in the processing field. Optionally, two sub-processes, geometric information and non-geometric information, can be set, and finally the processing steps are used as indexes to merge and obtain the implementation data ontology. The construction of the implementation model focuses on the implementation data, so this embodiment selects the processing feature as the smallest unit to describe the ontology. After constructing the processing feature ontology, the implementation geometric data can be imported to generate a single optical element ontology instance. Subsequently, by adding rules, all instances of this type of optical element can be generated at one time.

[0071] Step 7: Use the feature tree traversal method to complete the scanning of all PMI information in the MBD body model. Based on the process execution results and the nearest neighbor algorithm, optimize the optical component implementation model by setting weights to complete the optimization of the optical component process implementation model, including:

[0072] After years of research and practice, the inventors have considered that the core of the nearest neighbor algorithm lies in the calculation of similarity, and the similarity matching between knowledge depends on the measurement of this similarity. When performing similarity calculation, commonly used distance measurement methods include Euclidean distance, Manhattan distance, cosine similarity and Minkowski distance. Different numerical types and data characteristics determine which distance measurement method to choose. According to the specific application scenario and data characteristics, choosing a suitable measurement method is the key to ensuring matching accuracy. The information required for the matching of the processing method in this embodiment includes processing characteristics, feature size 1, feature size 2, feature size 3, surface roughness, upper tolerance, lower tolerance and part material. In order to facilitate the description of the matching process, this embodiment represents the information to be matched as X (x1, x2, x3, x4, x5, x6, x7, x8), where x1 to x8 represent the various information matched by the above-mentioned processing method respectively. The relevant information already existing in the knowledge base is represented as Y (y1, y2, y3, y4, y5, y6, y7, y8), and the distance measurement calculation method between different information is as follows:

[0073] Machining features and part materials are both categorical variables, and there is no direct or indirect correlation between them. Different machining features and materials can lead to significant differences in the selection of machining methods. For example, holes are typically machined using drilling, while slots require milling. Therefore, to measure the differences between these categorical variables, the Hamming distance is used as a distance metric. The specific calculation method is shown below:

[0074]

[0075] Among them, d i is the distance metric, x i X is the processing feature or part material information, y i It is the processing feature or part material information of Y. If the two information are the same, the value is 0, otherwise it is 1.

[0076] In this embodiment, three dimensions of the processing feature are extracted. When determining the processing method, it is usually only necessary to consider the dimension that has the greatest impact on the processing process, that is, the main dimension. However, in some cases, other dimensions of the processing feature may also have a significant impact on the processing method. Therefore, in such cases, the impact of other dimensions on the processing method must be comprehensively considered. Based on this, this embodiment selects weighted Euclidean distance as the distance measurement method for feature dimensions to more accurately reflect the relative importance of each dimension. The specific calculation formula is shown below:

[0077]

[0078] Among them, d2 is the distance measurement value of the characteristic size, x2, x3, and x4 are the characteristic size 1, characteristic size 2, and characteristic size 3 of X respectively, y2, y3, and y4 are the characteristic size 1, characteristic size 2, and characteristic size 3 of Y respectively, and w1, w2, and w3 are the weights of the characteristic size 1, characteristic size 2, and characteristic size 3 respectively. In this embodiment, the characteristic size 1 is the main size of the processing feature, so w1=0.8, w2=0.1, and w3=0.1 are taken, and the sum of the three is 1.

[0079] In order to accurately capture nonlinear features such as PV value, RMS value, and surface roughness Rq value, this embodiment uses Manhattan distance as the distance measurement method, which is calculated as shown in the following formula:

[0080] d5=|x5-y5|

[0081] Wherein, d5 is the distance measurement value of the surface roughness, x5 is the surface roughness information of X, and y5 is the surface roughness information of Y.

[0082] During the process design, the choice of processing method mainly depends on the dimensional accuracy requirements of the parts. However, since dimensional accuracy itself is difficult to directly quantify using traditional distance measurement formulas, this embodiment uses the upper and lower tolerances of the main dimensions as quantitative indicators of dimensional accuracy, and uses weighted Manhattan distance to evaluate the differences between different tolerance requirements. The weighted Manhattan distance can effectively reflect the width of the tolerance zone and its impact on the selection of processing methods. Its calculation formula is shown below:

[0083] d6=w s |x6-y6|+w x |x7-y7|

[0084] Among them, d6 is the distance measurement value of the tolerance, x6 and y6 are the upper tolerance values of X and Y respectively, x7 and y7 are the lower tolerance values of X and Y respectively, and w s and w x are their respective weight coefficients. In this embodiment, w s =0.5, w x =0.5, the sum of the two is 1.

[0085] After calculating the distance metric values for each piece of information, its weight must be determined. Because the choice of processing method is influenced by multiple dimensions of information, including processing characteristics, feature dimensions, surface roughness, and tolerance requirements, and these factors have complex interactions, this embodiment uses the analytic hierarchy process (AHP) to determine the weight values for each piece of information.

[0086] The purpose of building a hierarchical structure is to clearly divide the hierarchical relationship of each decision-making factor, so as to form a clear and systematic hierarchical indicator system. This embodiment designs and divides the entire hierarchical structure into three main levels: the target level, the criterion level and the solution level. Figure 7 shown.

[0087] The goal layer, the highest level in the hierarchy, represents the ultimate goal of the entire decision-making process: selecting the appropriate machining method. The determination of the machining method directly impacts production efficiency, product quality, and cost control, making it a crucial decision in process design. Therefore, the goals of this layer are clear and guiding, forming the core of the entire decision-making process. The criteria layer then primarily includes key information influencing machining method selection: geometric characteristics, material specifications, surface characteristics, surface features, and service characteristics. These criteria represent the various process conditions that must be considered when selecting a machining method, providing multi-dimensional support and basis for decision-making at the goal layer. Finally, the solution layer primarily encompasses various machining methods within the knowledge base. These methods represent machining knowledge accumulated based on experience, specifications, and historical data, encompassing all technical solutions related to the goal and criterion layers. The solution layer provides specific implementation plans for the decisions made at the upper layers, selecting the most suitable machining method within the requirements and constraints defined by the criterion layer.

[0088] The judgment matrix U of the feature attribute is as shown below:

[0089]

[0090] Among them, u ij for u i To u j The relative importance scale of the information is then used, and then the characteristic attributes of the information are scaled according to the definition of the judgment matrix scale, as shown in Table 1.

[0091] Table 1 Judgment matrix scale and its definition

[0092]

[0093]

[0094] This embodiment comprehensively considers the influence of information such as geometric features, material indicators, surface indicators, surface features, and service characteristics on the processing method, and obtains the importance judgment between the information, as shown in Table 2.

[0095] Table 2 Judgment of importance between information

[0096] Geometric features Material indicators Surface characteristics causative features Face shape indicators Geometric features 1 3 3 5 7 Face shape indicators 1 / 3 1 1 3 5 Surface characteristics 1 / 3 1 1 3 5 causative features 1 / 5 1 / 3 1 / 3 1 3 Material indicators 1 / 7 1 / 5 1 / 5 1 / 3 1

[0097] The judgment matrix U is finally obtained from the above table, as shown in the formula:

[0098]

[0099] Normalize each column of the judgment matrix U using the following formula:

[0100]

[0101] in, is the normalized value of the element in row i and column j, is the sum of all elements in the jth column. The normalized data is shown in Table 5.4, and the result retains three decimal places.

[0102] Table 3 Normalized values of matrix U

[0103] Geometric features Material indicators Surface characteristics causative features Face shape indicators Geometric features 0.498 0.542 0.542 0.405 0.333 Material indicators 0.166 0.181 0.181 0.243 0.238 Surface characteristics 0.166 0.181 0.181 0.243 0.238 causative features 0.099 0.060 0.060 0.081 0.143 Face shape indicators 0.071 0.036 0.036 0.027 0.048

[0104] Next, take the average of the normalized values in each row to get the weight of each piece of information. The arithmetic mean is calculated using the following formula:

[0105]

[0106] Among them, w i is the average value of the i-th row, that is, the weight of the item, and m is the number of columns in the matrix. Based on the above data processing, the weight coefficient of each information obtained in this embodiment is: w i =(0.464, 0.202, 0.202, 0.089, 0.043), which are the weight coefficients of processing features, dimensional tolerances, surface roughness, feature dimensions, and materials, respectively. The result is retained to three decimal places.

[0107] Since it is difficult to achieve completely consistent measurements when comparing information attributes pairwise, there will usually be certain errors. Therefore, it is necessary to perform consistency testing on the judgment matrix to ensure its accuracy. The consistency index Cl is introduced as shown in the following formula:

[0108]

[0109] Among them, λ max is the maximum eigenvalue of the matrix U, which is calculated to be 5.1284, n is the matrix order, and all calculations result in a Cl value of 0.0321.

[0110] Then, the consistency ratio CR is calculated. At this time, the average random consistency index RI needs to be considered. The RI value can be found through the order of the matrix and Table 4.

[0111] Table 4 RI values of judgment matrix

[0112] Order 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0113] In this embodiment, the order of matrix U is 5, so the RJ value obtained from the table is 1.12. The CR value is calculated as shown in the following formula:

[0114]

[0115] From the above formula, we can get the CR value of 0.029, that is, CR<0.10, so the matrix U has satisfactory consistency.

[0116] After completing the distance metric calculation for each piece of information and determining its weight coefficient, the comprehensive distance metric value is calculated by weighted summation to evaluate the similarity between the information to be matched and the process knowledge in the knowledge base. The calculation of the comprehensive distance metric value is shown in the following formula:

[0117] d z =0.464d1+0.202d2+0.202d5+0.089d6+0.043d8

[0118] Among them, d z This is the comprehensive distance metric between the information to be matched and a piece of process knowledge in the knowledge base. A smaller value indicates a higher similarity and a better match. During the processing method matching process, the system calculates the comprehensive distance metric between the information to be matched and each piece of process knowledge in the knowledge base according to the above formula and selects the processing method with the highest similarity.

[0119] To ensure the uniqueness of the matching results, this embodiment adopts the following strategy: First, the comprehensive distance metric values of all candidate process knowledge items are calculated and sorted in ascending order. The processing method with the smallest comprehensive distance metric value (i.e., the highest similarity) is then displayed as the optimal match on the system interface. In extreme cases, if multiple pieces of process knowledge have the same comprehensive distance metric value, the system will prioritize the process knowledge item with the highest similarity calculated first to avoid ambiguity in the matching results.

[0120] Through the above scheme, this embodiment uses MBD technology, based on the characteristics of optical component processing technology knowledge with mainly structured and partially unstructured expression and storage, and first establishes an optical processing technology design ontology model by manual modeling. After establishing the optical processing technology design ontology model, the production process elements of optical processing are analyzed with people, machines, materials, methods, environment, and measurement as the core, and the open domain ontology model is integrated to refine the concepts of personnel, equipment, main and auxiliary materials, and then establish an optical component process implementation model based on the process design ontology model by manual modeling. The implementation model is the basis for instance data mapping and can be continuously iterated and optimized during subsequent use. Production planners and managers can use the implementation model to count and calculate the cost and time of resources consumed and occupied by product manufacturing.

[0121] On the other hand, this embodiment further provides an electronic device, including: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned method for constructing an optical element implementation model based on MBD when executing the computer program.

[0122] On the other hand, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for constructing an optical element implementation model based on MBD are implemented.

[0123] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. Persons skilled in the art will appreciate that improvements and modifications may be made without departing from the spirit and scope of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing an optical element implementation model based on MBD, characterized in that: include: Generate an optical element implementation model processing structure framework, and construct the MBD model, design process, and process execution results of the optical element; Identify the feature information of the optical element and call the API function to extract the attribute information of each feature; traverse the PMI information of the MBD model and systematically organize the attribute information and PMI information to generate a processing feature information set; The optical element is processed based on the processing feature information set, a process execution result is obtained, and the implementation model is optimized.

2. The method for constructing an optical element implementation model based on MBD according to claim 1, characterized in that: The step of constructing a processing structure framework for an optical element implementation model and designing an MBD model of the optical element includes: Performing structural analysis on the optical element, determining the implementation model, MBD body model, and MBD process model of the optical element, and establishing a correlation between the implementation model, the MBD body model, and the MBD process model; The MBD body model includes geometric features, material features, surface features, and usage features; the MBD process model includes annotation information, process attribute information, and three-dimensional models corresponding to parts at different production stages.

3. The method for constructing an optical element implementation model based on MBD according to claim 1, characterized in that: The construction design process includes: Assign process information and step information to optical components through information modeling; Among them, the process information includes the process number, process name, process content, process inspection, whether it is a critical process, whether it is a special process and whether it is sent for standard inspection; the work step information includes the work step sequence number, work step number, work step name, work step content and processing requirements, whether it is a critical work step and precautions and remarks.

4. The method for constructing an optical element implementation model based on MBD according to claim 3, characterized in that: The construction process execution results include: Through information modeling, optical components are given product information, process master information, process execution information and step execution results during actual processing; Among them, the product information includes product classification, product name, drawing number and product version; the process main information includes process number, process code, process name and process version; the process execution information includes the executed process number, executed process name, executed process content, whether to execute process inspection, whether to execute key processes, whether to execute special processes and whether to execute standard inspection; the step execution results include the executed step sequence number, executed step number, executed step name, whether to execute key processes and whether to execute precautions and remarks, process inspection information and step inspection results.

5. The method for constructing an optical element implementation model based on MBD according to claim 1, wherein: The PMI information includes design information, basic attribute information and processing technology information; the design information refers to the PMI information directly associated with the geometric features of the three-dimensional model; the basic attribute information is a general description of the part; and the processing technology information is the PMI information generated by the part during the production and manufacturing stage.

6. The method for constructing an optical element implementation model based on MBD according to claim 1, characterized in that: The characteristic information for identifying the optical element includes: A processing feature data set is constructed, and a three-dimensional model of the optical element is constructed using computer software and then converted into a corresponding two-dimensional processing feature image; after the two-dimensional processing feature image is annotated, the processing features are extracted using a target detection model.

7. The method for constructing an optical element implementation model based on MBD according to claim 6, characterized in that: The systematically arranging the attribute information to generate a processing feature information set includes: The identified features are classified to obtain image classification and text classification, and a processing feature information set is generated through systematic processing. The processing feature information set is used to express the mapping between the processing features and PMI information.

8. The method for constructing an optical element implementation model based on MBD according to claim 1, wherein: Optimizing the implementation model includes optimizing the implementation model based on the nearest neighbor algorithm according to the process execution results to match different information conditions in the processing process, specifically including: The nearest neighbor algorithm is used to obtain the distance information between the processing feature and the part material, the distance information between the main dimension and the characteristic dimension during the processing process, the root mean square value, peak-to-valley value and surface roughness of the surface accuracy, and the upper and lower tolerances of the main dimension; Set up a target layer, a criterion layer and a solution layer, wherein the target layer is used to determine the processing method; the criterion layer is used to reflect the key information affecting the selection of the processing method, the key information including geometric features, material indicators, surface indicators, surface features and service features; the solution layer includes the processing methods in the knowledge base; A judgment matrix of characteristic attributes is constructed, and the characteristic attributes of the information are scaled according to the definition of the judgment matrix scale to obtain the final judgment matrix. After normalizing the final judgment matrix, the weight of each key information feature is obtained; after introducing the random consistency index, the consistency ratio is calculated, and the comprehensive distance measurement value is calculated by weighted summation to evaluate the similarity between the information to be matched and the process knowledge in the knowledge base, and based on the similarity, the processing method with the highest similarity is screened out.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the method for constructing an optical element implementation model based on MBD as claimed in any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for constructing an optical element implementation model based on MBD are implemented as claimed in any one of claims 1 to 8.

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