Product manufacturing process carbon footprint assessment method and system based on design features

Abstract

This invention belongs to the field of green manufacturing technology for electromechanical products. It provides a method and system for assessing the carbon footprint of a product manufacturing process based on design features. The method establishes an adjacency attribute graph of design features, identifies multiple manufacturing unit process scenarios within a manufacturing process context, and determines the carbon footprint of the product to be manufactured within the entire manufacturing scenario based on the design features and carbon emission data generated by each manufacturing unit process scenario. When a design feature changes, the method determines the adjacency attribute graph of the modified design features, matches similar products from a case library based on the design feature adjacency attribute graph, extracts the manufacturing unit process scenarios corresponding to similar design features of the similar products in the case library to update the manufacturing scenario, and predicts the carbon footprint of the modified product to be manufactured based on the updated manufacturing scenario and the modified design features. This invention achieves accurate calculation of the carbon footprint and accurate prediction of the carbon footprint after design feature changes.

Description

Technical Field

[0001] This invention relates to the field of green manufacturing technology for electromechanical products, specifically to a method and system for assessing the carbon footprint of product manufacturing processes based on design features. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The manufacturing process of electromechanical products is one of the main sources of carbon emissions in the manufacturing industry, accounting for about one-third of the total greenhouse gas emissions throughout the product's life cycle. During the new product development process, manufacturing companies typically make design changes to existing products. However, in the product design change stage, the lack of relevant data on the actual manufacturing process (process parameters, tooling, etc.) makes it difficult to support carbon footprint estimation, hindering the low-carbon optimization process of the product.

[0004] Currently, researchers have conducted extensive research on design information representation, manufacturing process carbon footprint modeling, and carbon footprint prediction. However, existing solutions still have the following problems: (1) The characteristics of numerous product design features, multi-source heterogeneous design information, and complex relationships between design features make it difficult for existing methods to fully represent design feature information; (2) The carbon footprint information of products is discrete and the process types are diverse. Existing solutions do not associate design features with product carbon footprint; (3) The product design change process usually introduces new design features. Due to the lack of detailed and complete manufacturing process scenario data, it is difficult to achieve accurate carbon footprint prediction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for assessing the carbon footprint of product manufacturing processes based on design features. It achieves a precise expression of product design features by considering the product's geometric and precision characteristics, and closely links these features with the product's carbon footprint calculation, resulting in more accurate carbon footprint calculation. When product design changes, it matches similar products in a case library using similarity calculation to construct an updated manufacturing scenario, thus achieving more accurate carbon footprint prediction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for assessing the carbon footprint of a product manufacturing process based on design features.

[0008] A method for assessing the carbon footprint of a product manufacturing process based on design features includes the following steps:

[0009] Extract the design features of the product to be manufactured. The design features include design feature type, geometric features, and precision features. Construct an adjacency attribute graph of the design features based on the design features.

[0010] Based on the scenario attributes of the manufacturing process of the product to be manufactured, determine multiple manufacturing unit process scenarios under the manufacturing process scenario;

[0011] Based on the design features and the carbon emission data generated by each manufacturing unit process scenario, determine the carbon footprint of the product to be manufactured in the entire manufacturing scenario.

[0012] When the design features of the product to be manufactured change, the adjacency attribute graph of the changed design features is determined. Similar products are matched from the case library based on the adjacency attribute graph of the design features. The manufacturing unit process scenarios corresponding to the similar design features of similar products in the case library are extracted to update the manufacturing scenario. Based on the updated manufacturing scenario and the changed design features, the carbon footprint of the product to be manufactured after the change is predicted.

[0013] Secondly, the present invention provides a carbon footprint assessment system for product manufacturing processes based on design features.

[0014] A product manufacturing process carbon footprint assessment system based on design features, comprising:

[0015] The design feature extraction unit is configured to: extract the design features of the product to be manufactured, including design feature type, geometric features and precision features, and construct a design feature adjacency attribute graph based on the design features;

[0016] The manufacturing scenario determination unit is configured to: determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on the scenario attributes of the manufacturing process of the product to be manufactured;

[0017] The carbon footprint assessment unit is configured to determine the carbon footprint of the product to be manufactured in the entire manufacturing scenario based on the design features and the carbon emission data generated by each manufacturing unit process scenario.

[0018] The carbon footprint prediction unit is configured to: when the design features of the product to be manufactured change, determine the adjacency attribute graph of the changed design features, match similar products from the case library based on the adjacency attribute graph of the design features, extract the manufacturing unit process scenarios corresponding to the similar design features of the similar products in the case library to update the manufacturing scenario, and predict the carbon footprint of the changed product to be manufactured based on the updated manufacturing scenario and the changed design features.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. This invention innovatively proposes a carbon footprint assessment method for product manufacturing processes based on design features. According to the geometric and precision features of the product, it realizes the accurate expression of product design features and closely links product design features with the calculation of product carbon footprint to achieve more accurate carbon footprint calculation. When the product design changes, it matches similar products in the case library through similarity calculation to construct an updated manufacturing scenario, thereby achieving more accurate carbon footprint prediction.

[0021] 2. In view of the characteristics of numerous product design features and complex relationships between features, this invention proposes a method for constructing a design feature adjacency attribute graph, which defines and represents design features from both geometric and precision aspects, thereby achieving a more accurate expression of design features.

[0022] 3. In response to the problems of discrete and diverse information in the product manufacturing process and the lack of systematic correlation with design features, this invention proposes a scenario-based measurement method for manufacturing process information, which realizes a unified representation of manufacturing information and ensures the continuity of the manufacturing scenario.

[0023] 4. To address the problem that existing carbon footprint modeling methods for manufacturing processes are coarse-grained and do not fully consider the impact of design features and scenario attributes on carbon footprint quantification, making it difficult to achieve comprehensive and accurate calculation, a feature-scenario-based carbon footprint quantification strategy for product manufacturing processes is proposed to support refined accounting of carbon footprint in product manufacturing processes.

[0024] 5. To address the problem that new product design features lack detailed and complete manufacturing scenario data after design changes, making it difficult to support carbon footprint prediction, this invention proposes a manufacturing process carbon footprint prediction strategy based on feature similarity matching. By calculating graph structure similarity, similar cases in the case library are matched; scenario reconstruction rules are formulated, and by splitting and reorganizing unit processes, a new product manufacturing process scenario is constructed to support carbon footprint accounting and achieve efficient and accurate prediction of new product carbon footprint.

[0025] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 A flowchart illustrating the direction of carbon footprint assessment for product manufacturing processes based on design features, provided as an exemplary embodiment of the present invention;

[0028] Figure 2A schematic diagram of the geometric feature adjacency attribute graph construction process provided for an exemplary embodiment of the present invention;

[0029] Figure 3 A schematic diagram illustrating the extraction and representation of precision features as provided in an exemplary embodiment of the present invention;

[0030] Figure 4 A schematic diagram illustrating the design feature adjacency attribute graph construction process provided as an exemplary embodiment of the present invention;

[0031] Figure 5 A schematic diagram of the carbon footprint transfer path in the manufacturing process provided as an exemplary embodiment of the present invention;

[0032] Figure 6 A schematic diagram of a turning process is provided for an exemplary embodiment of the present invention, wherein... Figure 6 (a) is a schematic diagram of turning the outer diameter. Figure 6 (b) is a schematic diagram of the machined end face. Figure 6 (c) is a schematic diagram of the turning groove;

[0033] Figure 7 A schematic diagram illustrating the design feature attribute diagram modification process provided as an exemplary embodiment of the present invention;

[0034] Figure 8 A schematic diagram illustrating the reconstruction of a manufacturing process scenario for modified design features, provided as an exemplary embodiment of the present invention;

[0035] Figure 9 This is a schematic diagram of a product manufacturing process carbon footprint assessment system based on design features, provided as an exemplary embodiment of the present invention. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] Currently, nearly 70% of the carbon footprint of a product manufacturing process is determined during the design phase. Manufacturing companies typically make design changes to existing products during new product development. However, the lack of relevant data on the actual manufacturing process (process parameters, tooling, etc.) during the design change phase hinders carbon footprint prediction and impedes the low-carbon optimization process. Therefore, there is an urgent need for a carbon footprint modeling and prediction method based on design features to provide crucial technical support for companies during the design and development phase. This method should primarily address two aspects: first, enabling early prediction of the carbon footprint of new products, providing data support for low-carbon optimization and improvement; and second, supporting quantitative comparison of carbon footprints between different design schemes, assisting decision-makers in selecting the optimal solution.

[0039] In terms of design information representation, some researchers have proposed a multi-level low-carbon design model for the entire product lifecycle design scheme. By integrating the hierarchical architecture of function, structure, design features, and processing technology, this model achieves a correlation mapping with carbon emissions throughout the entire lifecycle. However, this method has a low modeling granularity, and the correlation between information is vague, making it difficult to accurately characterize the relationship between design information and carbon footprint.

[0040] In modeling the carbon footprint of manufacturing processes, researchers have modeled it from different levels, including industry, workshop, and equipment levels. At the industry level, the LCA (Limited Input-Output Analysis) method was used to quantitatively describe the carbon flow trends in manufacturing processes across industries. At the workshop level, the focus was on the distribution and dynamic control of carbon flows between production units, providing guidance for workshop carbon data management and carbon quota planning. At the equipment level, the impact of machine tool spindle systems, auxiliary devices, and operating conditions on machine tool energy consumption was analyzed, and a mathematical model of energy consumption in machine tool processing was constructed. However, these methods emphasize the analysis of the manufacturing process flow, with relatively coarse modeling granularity. They do not fully consider the impact of product or component characteristics on the carbon footprint, making it difficult to achieve a comprehensive and accurate quantification of the carbon footprint of the manufacturing process.

[0041] In carbon footprint prediction, some studies rely on similarity matching algorithms based on case libraries. By reusing data or knowledge, these algorithms effectively integrate existing resources to improve the efficiency of design changes. However, similarity matching often involves a limited range of information types and fails to consider the similarity of topological relationships between features, leading to reduced accuracy and making it difficult to guarantee the precision of carbon footprint prediction.

[0042] Therefore, existing methods do not fully consider the impact of product feature information on the carbon footprint of the manufacturing process, and lack a unified correlation model, making it difficult to achieve carbon footprint modeling and accurate prediction based on product features.

[0043] In view of the problems existing in the current solution, this implementation proposes a method for assessing the carbon footprint of the product manufacturing process based on design features, such as... Figure 1As shown, the process includes the following:

[0044] S101: Extract the design features of the product to be manufactured, including design feature type, geometric features and precision features, and construct a design feature adjacency attribute graph based on the design features;

[0045] S102: Based on the scenario attributes of the manufacturing process of the product to be manufactured, determine multiple manufacturing unit process scenarios under the manufacturing process scenario;

[0046] S103: Based on the design features and the carbon emission data generated by each manufacturing unit process scenario, determine the carbon footprint of the product to be manufactured in the entire manufacturing scenario;

[0047] S104: When the design features of the product to be manufactured change, determine the adjacency attribute graph of the changed design features, match similar products from the case library according to the adjacency attribute graph of the design features, extract the manufacturing unit process scenarios corresponding to the similar design features of the similar products in the case library to update the manufacturing scenario, and predict the carbon footprint of the changed product to be manufactured according to the updated manufacturing scenario and the changed design features.

[0048] In step S101 of this implementation, specifically, it includes:

[0049] S101-1: Geometric Feature Extraction and Representation.

[0050] The geometric and topological information contained in the geometric structure model can be expressed in the form of an attribute adjacency graph, where nodes represent geometric features (faces) and arcs represent common edges between adjacent faces. When faces and edges are assigned relevant attributes, these attributes can be represented on the nodes and edges, specifically as follows:

[0051] (1);

[0052] In the formula, Represents the adjacency attribute graph of geometric features. Represents the set of all faces in a geometric feature. ,in Indicates the first Each side; E It represents All adjacent edges in, ,in Representation surface With noodles If there is no common edge between them, then ; H This represents the set of attributes added to faces and edges. .

[0053] As shown in Table 1, the present invention defines the attribute information of nodes and edges in the geometric feature adjacency attribute graph.

[0054] Table 1: Definition of Face and Edge Attributes in Geometric Feature Attribute Map

[0055]

[0056] To extract the geometric structure information of the product, such as Figure 2 As shown, this invention proposes a process for constructing a geometric feature adjacency attribute graph based on feature datum planes, as detailed below:

[0057] Step 1: Create a face node based on the feature base face, and then traverse all feature faces and edges of the entire design feature;

[0058] Step 2: Whenever a face is traversed, a face node is created, and the face's attribute information is extracted. The face type, area, concavity / convexity, and number of adjacent faces are stored in the face node.

[0059] Step 3: When traversing the common intersection line of two adjacent faces, an edge is created between the two nodes, and the number and concavity / convexity information of the edge are extracted and stored in the edge. To make the concavity / convexity of the edge clearly visible, it is marked as 0 when the common edge is concave and as 1 when the common edge is convex.

[0060] Step 4: After all feature faces and edges have been traversed, connect the nodes and edges according to the topological relationships to construct a geometric feature attribute graph.

[0061] S101-2: Precision Feature Extraction and Representation.

[0062] Accuracy features include surface roughness, dimensional tolerances, and geometric tolerances. These features are attached to geometric feature surfaces, and the machining of surfaces with different accuracy features directly affects the machining method, tool selection, and determination of machining parameters. The area of ​​the machined surface affects the machining time of the design feature, and the machining method and time directly affect the carbon emissions of the product manufacturing process. Therefore, a method is proposed that correlates the area of ​​the machined surface with roughness and tolerances, and uses this area to represent the accuracy information of the design feature. Specifically, firstly, the areas of machined surfaces with the same roughness level and the areas of machined surfaces associated with the same tolerance type and tolerance level are extracted from the design feature. Then, the areas of surfaces with the same accuracy type and accuracy level are summed. Finally, the one-dimensional vector of the design feature accuracy information is composed of the areas of machined surfaces with the same accuracy type but different accuracy levels. Therefore, each element in SR (Surface Roughness) represents the surface area associated with the roughness level. Each element in DTA (Dimensional Tolerance) represents the surface area associated with a specific dimensional tolerance level. Each element of the GTA (Geometric Tolerance) represents the surface area associated with a specific geometric tolerance type and grade. In geometric features, all surface areas related to precision features are explicitly expressed. The area of ​​the machined surface affects the machining time of the design feature, and the machining method and time directly impact the carbon emissions of the product manufacturing process. Therefore, this invention proposes a method that correlates the area of ​​the machined surface with roughness and tolerance, and uses this area to represent the precision information of the design feature. Specifically, firstly, the areas of machined surfaces with the same roughness level and the areas of machined surfaces associated with the same tolerance type and tolerance level are extracted from the design features. Then, the areas of surfaces with the same precision type and precision level are summed. Finally, the one-dimensional vector of design feature precision information is composed of the areas of machined surfaces with the same precision type but different precision levels. Therefore, each element in SR (Surface Roughness) represents the surface area associated with the roughness level, each element in DTA (Dimensional Tolerance) represents the surface area associated with a specific dimensional tolerance level, and each element in GTA (Geometric Tolerance) represents the surface area associated with a specific geometric tolerance type and a specific geometric tolerance level. For example... Figure 3 As shown.

[0063] More specifically, Figure 3 In this context, Ra1.6 represents the surface roughness parameter, where Ra is the arithmetic mean roughness, and a value of 1.6 μm indicates that the arithmetic mean roughness of the surface is 1.6 micrometers. and These represent the areas of two specific regions, expressed in square millimeters. This indicates a cylindrical hole with a diameter of 40 mm, with an upper deviation of +0.036 mm and a lower deviation of -0.011 mm; This indicates a dimension with a length of 10 mm, with both upper and lower deviations of ±0.012 mm; SR = [0 0 0 0 0 (2513.28) 0 0 0 0 0] is a roughness parameter vector; the value 2513.28 represents the roughness parameter of a specific region (S1+S2); DTA = [0 0 0 0 0 0 (1256.64)(1256.64) 0 0 0 0 0 0 0 0 0 [0] is a dimensional tolerance vector, with the two 1256.64 values ​​corresponding to the area tolerances of regions S1 and S2; GTA is a geometric tolerance matrix, corresponding to the tolerance values ​​of flatness, cylindricity, and parallelism, respectively. The value 1256.64 in the first row of the matrix represents the flatness tolerance corresponding to the area S1 of a specific region, the value 1256.64 in the second row of the matrix represents the cylindricity tolerance corresponding to the area S1 of a specific region, and the value 1256.64 in the second row of the matrix represents the parallelism tolerance corresponding to the area S2 of a specific region; A is the datum symbol.

[0064] S101-3: Design Feature Extraction and Representation. Design feature attribute information includes design feature type, geometric features, and precision features, the definitions of which are shown in Table 2.

[0065] Table 2: Design Feature Attributes

[0066]

[0067] The product's design feature adjacency attribute graph is composed of design feature attributes. The relationship between design features is constructed. The adjacency attribute graph of design features is defined as follows:

[0068] (2);

[0069] in, Representative design features , Indicates the number of a certain product i Each design feature node; ,in , and They represent the first The type, geometric features, and precision features of each design feature, and Representing pattern interaction information, a set of array-coupled design features The values ​​are the same. A one-hot vector is an encoding method used to represent discrete data, commonly used in machine learning and natural language processing. Its characteristic is that only one element in the vector is 1, and all other elements are 0. The position of 1 represents a specific category or state; therefore, the geometric feature is defined as {0,1}, and the precision feature is defined as {1,0}. Geometric features representing a design feature are expressed in a structured way using an adjacency attribute graph, which includes the properties of faces and edges, such as the area of ​​the face and the concavity and convexity of the face and edges. ,in , , They represent the first Roughness information, dimensional tolerance information, and geometric tolerance information of each design feature. Represents design features and attributes.

[0070] R The set of edges representing the relationships between design features. , This represents the boundary line between two design feature nodes. ,in and These represent the distance and coupling type between the two nodes, respectively. ,in , , and This indicates whether the two nodes have the following four types of coupling relationships ( (where 0 represents negative and 1 represents positive): dependency interaction, adjacency interaction, baseline interaction, and pattern interaction. Based on the degree of coupling among the four types of relationships, the distance between nodes corresponding to coupled relationships is defined as... , respectively, represent the distances between nodes corresponding to dependency interaction, adjacency interaction, baseline interaction, and pattern interaction relationships.

[0071] Associative binary adjacency matrix A For explanation MF and R The correlation between them, among which and Represents a node neighborhood , Represented as:

[0072] (3);

[0073] The process of constructing the adjacency attribute graph of product design features is as follows: First, construct a node set based on the attributes of the design features. VBy determining whether design features are adjacent, an associated binary adjacency matrix is ​​generated. A .like , And the geometric features in the design features are surfaces and There are intersecting edges, design features and They are adjacent. Finally, an edge set is constructed based on the distance and coupling relationship between adjacent design features. E Complete the construction of the attribute graph.

[0074] More specifically, such as Figure 4 As shown, the process includes the following:

[0075] Start: Initiate the entire process;

[0076] Part design model: Obtain or create a design model of the part as the basis for subsequent processing;

[0077] Machining feature extraction: Extracting machining features from the part design model is one of the key steps in the entire process;

[0078] Geometric feature extraction: Extracting geometric features from processing features and constructing an attribute map based on these geometric features;

[0079] Precision feature extraction: Extract precision features from processing features and determine relevant attributes;

[0080] Processing feature node construction: The extracted processing features are constructed into nodes to prepare for subsequent graph structure processing;

[0081] Determine if extraction is complete: Check if all processing features have been extracted;

[0082] If not completed: Return to the "Processing Feature Extraction" step and continue extracting the remaining processing features;

[0083] If complete: Proceed to the next step;

[0084] Node relationship determination: Determine the relationships between various processing feature nodes;

[0085] Node distance calculation: Calculate the distance between each processing feature node;

[0086] Construction of Adjacency Attribute Graph for Part Machining Features: Based on node relationships and node distances, construct the adjacency attribute graph for part machining features;

[0087] End: The entire process is complete.

[0088] In step S102 of this implementation, specifically, it includes:

[0089] S102-1: Definition of a Manufacturing Cell Process. Each cell in the manufacturing process transforms resource and energy inputs into intermediate product outputs, while simultaneously generating environmental emissions (such as wastewater, exhaust gas, and dust). Therefore, a manufacturing cell process is the smallest unit that generates carbon emissions. Specifically, it is defined as the complete process by which a product completes its step on a specific processing machine, under conditions where process parameters, cutting tools, auxiliary materials, clamping methods, and processing location remain constant.

[0090] S102-2: Representation of Manufacturing Cell Process Scenario Information. A manufacturing process scenario is a comprehensive description of the specific environment, technology, processes, and interaction methods involved in each stage of product manufacturing activities. It covers the entire production chain from raw material to finished product, highlighting the dynamic synergy of equipment, data, processes, and environment at different stages. A scenario is composed of multiple attributes, and the representation of scenario information in the manufacturing cell process, as the smallest unit of the manufacturing process, forms the basis of the entire manufacturing process scenario.

[0091] Based on the definition of the manufacturing unit process scenario in step 1, it can be formalized into a multi-attribute space. Considering the diverse information types of manufacturing process scenarios, and combining the seven elements of a process scenario (process type, equipment, process object, process parameters, auxiliary materials, energy type, and process description), this invention proposes a mathematical expression for the Manufacturing Unit Process Scenario (MUPS) based on set theory:

[0092] (4);

[0093] In the formula, the scene attributes include seven types of attributes: Machining Type (MT), Machining Equipment (ME), Machining Object (MO), Process Parameter (PP), Auxiliary Materials (AM), Energy Type (ET), and Scene Description (SD).

[0094] S102-3: Manufacturing Process Scenario Information Representation Matrix, Product Manufacturing Process MP Depend on n A manufacturing unit process with a time sequence MUP The composition can be represented as:

[0095] (5);

[0096] in, Representing the Each manufacturing unit process.

[0097] As shown in equation (5), a manufacturing process scenario is a complex scenario composed of manufacturing unit processes according to the process route (PR). A manufacturing process scenario can be expressed in the form of a matrix, that is, the unit process scenarios included in the product manufacturing process are arranged and combined according to the processing order to form a manufacturing process scenario information expression matrix. MPS As shown below:

[0098] (6);

[0099] In the matrix, n This indicates the number of unit process scenarios included in a certain manufacturing process. Each row of the matrix corresponds to a set of manufacturing unit process scenarios, while each column represents a set of various scenario attributes. More specifically... , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Processing type of each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Processing equipment for each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. The processing objects of each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Process parameters for each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Supporting materials for each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Energy type for each unit process scenario; , , , They represent the 1st, 2nd, 3rd, and 4th respectively. Each unit process scenario is described. Scenario attribute information may be identical across different unit process scenarios. Ultimately, through the coupling relationships between these unit process scenarios, a complete product manufacturing process scenario is constructed. Using a matrix format, the complex scenario information of the workshop-level manufacturing process is expressed in a standardized way, reflecting the manufacturing process from raw material to finished product with fine granularity.

[0100] In step S103 of this implementation, specifically, it includes:

[0101] S103-1: Defining the System Boundary of the Carbon Footprint of the Manufacturing Process. A carbon footprint refers to the total greenhouse gas emissions generated by a product, production process, or service system throughout its entire life cycle. The manufacturing process is a dynamic system involving energy and material inputs, as well as product and waste outputs. The system boundary of the carbon footprint of the manufacturing process is formed by the orderly combination of multiple unit processes according to the technological route. This invention defines it as the "door-to-door" scope, that is, the product manufacturing process from raw material to finished product. Within this scope, the carbon footprint is accumulated from the carbon emissions of several unit processes. The carbon emissions generated by each unit process are mainly divided into two categories: energy carbon emissions and material carbon emissions.

[0102] In a manufacturing unit process, energy carbon emissions refer to the carbon emissions caused by the consumption of electrical energy or other energy conversions during the operation of processing equipment and material transportation; material carbon emissions refer to the carbon emissions generated during the preparation, use, and disposal of tools, cutting fluids, etc. The carbon footprint of a manufacturing process is essentially the cumulative result of energy and material flows throughout the process from raw material to finished product. For example... Figure 5 As shown, taking cutting machining as an example, the main processes required in this process are... Each processing equipment participates in the process, and each piece of equipment needs to complete multiple steps, or multiple unit processes. Therefore, the entire process starts with the first unit process of the first equipment and continues until the k-th equipment completes the process. Each unit process concludes to produce the final product. These unit processes involve the input of electrical energy and auxiliary materials, as well as the output of direct carbon emissions. Furthermore, the transfer of the product between different devices involves logistics and transportation, which also generates carbon emissions.

[0103] More specifically, the blank is processed by processing equipment 1 (including unit processes 1, 2, and 3), and then transported to processing equipment 2 (including unit processes 4, 5, and 6) via logistics transportation 1 to obtain intermediate parts. These intermediate parts are then transported to processing equipment 3 (including unit processes 7, 8, and 9) via logistics transportation 2. Finally, the intermediate parts processed by processing equipment 3 are transported to processing equipment 4 (including unit processes 10, 11, and 12) via logistics transportation 3, and subsequently transported by logistics transportation. Transported to processing equipment (including unit procedures) Unit process and unit process The final product is obtained, and the processing characteristics of the finished product are as follows: , ··· Each of the processing equipment involves the input of electricity and auxiliary materials, and each processing equipment and logistics transportation generates carbon dioxide emissions.

[0104] Analyzing the carbon footprint transfer pathways of the manufacturing process, and the carbon footprint of the manufacturing process. CF MP This can be expressed as:

[0105] (7);

[0106] In the formula, Indicates the first Carbon emissions generated by each unit process, i = 1, 2, 3, ..., n; and The first Energy and material carbon emissions generated by each unit process; For the first step in the manufacturing process Carbon emissions generated by logistics transportation in each unit process This represents the number of unit procedures.

[0107] S103-2: Feature-Scenario-Based Modeling of Energy and Carbon Emissions in Part Manufacturing Processes.

[0108] Energy carbon emissions refer to the carbon emissions generated by the electrical energy consumed during machine tool operation, which can be calculated by multiplying the electrical energy carbon emission factor by the electricity consumption. Because the power of machine tools varies significantly under different operating conditions, their energy consumption and corresponding carbon emissions exhibit dynamic changes. Analysis of the power curve of a machine tool in a manufacturing unit reveals that its operation can be divided into five stages: startup, standby, spindle acceleration / deceleration, no-load operation, and machining. Considering the large energy consumption fluctuations and short duration of the startup and spindle acceleration / deceleration stages, this invention ignores them in carbon emission calculations.

[0109] Based on this, this invention quantifies the energy carbon emissions during machine tool operation by dividing them into three stages: standby, no-load, and cutting / machining stages. The calculation methods for each stage are as follows:

[0110] (1) Carbon emissions during standby phase.

[0111] The state in which the basic function modules are activated but the main drive system is not yet started after the machine tool is started is called the standby mode. The standby phase is mainly used for table zeroing, workpiece transport waiting, workpiece clamping and unclamping, and tool changing. The carbon emissions during the standby phase can be calculated using the following formula:

[0112] (8);

[0113] In the formula, Carbon emission factor of electricity; This refers to the machine tool's standby time. This refers to the machine tool's standby power, which is equivalent to the operating power of its basic components. During stable operation phases such as standby, no-load, and machining, the machine tool power fluctuation is small, and the power in each phase can be approximated as a stable value. Therefore, the standby energy consumption of the machine tool can be expressed as:

[0114] (9);

[0115] (2) Carbon emissions during the no-load phase.

[0116] The operating state when the machine tool's spindle system, feed mechanism, and chip removal device are running, but the tool has not yet contacted the workpiece, is defined as the no-load operating condition. Energy consumption in this condition persists throughout the entire machining cycle and is mainly caused by the tool's feed and retraction movements. Carbon emissions during the no-load operating phase can be calculated using the following formula:

[0117] (10);

[0118] (11);

[0119] In the formula, The no-load operating power of the machine tool, including the operating power of the basic components. No-load operating power of cutting-related systems Two parts; This refers to the no-load phase time.

[0120] In actual machining, the feed force and feed rate of the feed system have a smaller impact than the spindle system, so their effect is ignored in the calculation. Spindle system power Mainly related to spindle speed The two are related and exhibit a quadratic function relationship, as expressed below:

[0121] (12);

[0122] In the formula, This is the minimum no-load power of the machine tool; , This is the spindle speed coefficient, which is related to the machine tool model.

[0123] (3) Carbon emissions during the cutting stage.

[0124] The process of a cutting tool removing excess material from a workpiece is the machining stage, and its carbon emissions are calculated using the following formula:

[0125] (13);

[0126] In the formula, Power for the A-stage machining process of the machine tool; This refers to the cutting and machining time.

[0127] During the cutting process, the total power of the system mainly consists of the following three parts: the operating power of the basic components. No-load operating power of cutting-related systems and material removal power Furthermore, during machining, additional load power is generated due to the load operation of the machining system. The power loss is approximately directly proportional to the material removal power, as detailed below:

[0128] (14);

[0129] (15);

[0130] In the formula, The additional load loss factor has a value range of 0.15 to 0.25.

[0131] Material removal power and cutting force and cutting speed The relevant calculation formula is as follows:

[0132] (16);

[0133] Cutting force can be estimated using empirical formulas. For details, please refer to relevant machining process manuals. The calculation formulas for cutting force of common machining types are shown in Table 3.

[0134] Table 3: Cutting Force Calculation Formula

[0135]

[0136] In summary, the calculation formula for the energy carbon emission quantification model for manufacturing unit process scenarios is as follows:

[0137] (17);

[0138] S103-3: Feature-Scenario-Based Modeling of Material Carbon Emissions in Part Manufacturing Processes. Material Carbon Emissions This includes carbon emissions from tool wear and cutting fluid consumption. These emissions are not directly generated by machining energy consumption, therefore, specific methods are needed to allocate and integrate carbon emissions from different sources. Design features are components of a part, and each design feature is completed by a machining chain consisting of one or more machining units. This invention allocates tool wear and cutting fluid consumption to each machining unit, and then integrates and calculates the carbon emissions of the entire part manufacturing process on a unit-by-unit basis.

[0139] (1) Carbon emissions from tool wear.

[0140] Friction between the cutting tool and the workpiece during cutting generates a large amount of heat. Cutting fluid can cool the tool and remove this heat, but approximately 20% of the heat is still transferred to the tool, accelerating its wear. Carbon emissions from tool wear can be calculated by amortizing the carbon emissions from tool manufacturing energy consumption over machining time, using the following formula:

[0141] (18);

[0142] In the formula, This refers to the carbon emissions from tool wear during the machining process in this unit. Carbon emission factor for cutting tools; For tool life; For tool quality.

[0143] Tool life is determined by the number of sharpening cycles. N Tool life The calculation, combined with the formula, is as follows:

[0144] (19);

[0145] Where x, y, and z are all exponents. This represents the tool life coefficient.

[0146] (2) Carbon emissions from cutting fluid loss.

[0147] Cutting fluid is an indispensable auxiliary material in the manufacturing process. Common types include oil-based and water-based cutting fluids. This invention takes water-based cutting fluid as an example, and its carbon emission calculation formula is as follows:

[0148] (20);

[0149] In the formula, H The cutting fluid replacement cycle is generally 2 to 3 months; δ This refers to the concentration of the cutting fluid; the concentration of water-based cutting fluid after dilution is generally 5%. , These are the carbon emission factors for cutting fluid preparation and waste fluid treatment, respectively. C This refers to the amount of cutting fluid used. t c This refers to the cutting and machining time.

[0150] S103-4: A method for calculating the processing time of a unit process based on features and scenarios.

[0151] Processing time is a key factor affecting carbon emissions, with part design features and application scenarios playing a decisive role. Therefore, it is necessary to analyze the impact of geometric parameters, process parameters, and other information on processing time, dividing the process into time segments based on processing stages. The processing time of a single unit process is also considered. T From workpiece clamping to tool retraction, the time includes idle time, cutting time, and no-load time. Idle time encompasses machining preparation and tool change time. Details are as follows:

[0152] (twenty one);

[0153] (twenty two);

[0154] In the formula, Unit procedure i The standby time is determined by the clamping time. and tool change time composition; Unit procedure i Idle time; Unit procedure The cutting time.

[0155] (1) Preparatory work and standby time before processing .

[0156] In the manufacturing process, pre-processing preparation mainly includes three typical scenarios: initial preparation for the first process, fixture conversion and adjustment between different processing units, and transition preparation for cross-machine tool processing. Actual production data shows that the equipment downtime caused by pre-processing preparation mainly depends on the operator's skill level, and its fluctuation range is relatively controllable; therefore, it can be approximated as a constant, as follows:

[0157] (twenty three);

[0158] In the formula, Unit procedure The decision variable is whether preparatory work is needed; it takes the value 1 if needed and 0 otherwise. Representative and Unit Procedure Related constants.

[0159] (2) Wear and tool change time .

[0160] During the manufacturing process, when the tool wear reaches a certain level, it will affect the machining quality, thus requiring tool replacement. Since a single tool may be used in multiple unit processes, the tool change time for a particular unit process can be calculated by allocating it according to the ratio of its cutting time to tool life, as shown in the following formula:

[0161] (twenty four);

[0162] In the formula, This indicates the average tool change time.

[0163] Based on the tool life calculation method, unit process i The wear and tool change time can be expressed as:

[0164] (25);

[0165] (3) Tool advance and retraction idle time .

[0166] In unit process In the middle, the idle time generated by the tool advance and retraction operation t i,a The calculation formula is as follows:

[0167] (26);

[0168] In the formula, Unit procedure The total length of the cut path; Unit procedure Rapid feed rate.

[0169] (4) Cutting time .

[0170] During the manufacturing process, the cutting time The cutting time calculation methods differ for different machining types, primarily influenced by material removal (design features) and process parameters (scenario attributes). Furthermore, based on feature-scenario information, a cutting time quantification model is established, realizing the correlation between cutting time and feature-scenario, providing methodological support for refined carbon footprint calculation. This invention takes turning as an example to analyze its cutting time calculation model.

[0171] like Figure 6 As shown in (a), this unit process is turning an outer diameter. By analyzing its motion characteristics and cutting elements, the machining time calculation formula for turning an outer diameter can be obtained as follows:

[0172] (27);

[0173] In the formula, Representing unit projects The processing length is in mm; Unit procedure Machining allowance, in mm; Spindle speed, in r / min; Feed per revolution ( Figure 6 China (This can be used in all unit processes), the unit is mm / r; For the depth of cut ( Figure 6 China (This can be used in all unit processes), and the unit is mm.

[0174] Similarly, we can obtain the following: Figure 6 The turning end face shown in (b) and Figure 6 The formulas for calculating the machining time of the turning groove shown in (c) are as follows:

[0175] (28);

[0176] (29);

[0177] Spindle speed for turning outer diameters, end faces, and grooves ( Figure 6 China n (This can be used in each unit process) and cutting line speed ( Figure 6 China The relationships between the various unit procedures (which can all be used) are expressed as follows:

[0178] (30);

[0179] (31);

[0180] (32);

[0181] In the formula, This refers to the cutting speed, expressed in m / min. ( Figure 6 China d (Each unit process can be used) as a manufacturing unit process i The large diameter of the workpiece being processed ( Figure 6 China (This can be used in all unit processes) is the small diameter, and the unit is mm.

[0182] In summary, the formulas for calculating the machining time for turning external diameters, turning end faces, and turning grooves are expressed as follows:

[0183] (33);

[0184] (34);

[0185] (35);

[0186] In step S104 of this implementation, specifically, it includes:

[0187] S104-1: Determination of Design Change Type. Based on the product design feature attribute diagram representation method proposed in step S101, the changed design feature can be expressed as:

[0188] (36);

[0189] in, Design feature attribute diagrams for the modified parts; For the modified design feature nodes; These are the edges between the modified design feature nodes, i.e., the relationships between the design feature nodes. There are six main types of changes to nodes and edges: replacement, insertion, and deletion. For example... Figure 7 As shown, the replacement, insertion, and deletion of nodes together constitute a complete change process. More specifically, starting from the design feature attribute diagram in the upper left corner, the first step is a replacement operation, which means replacing one of the design features with a new design feature; next is an insertion operation, which means adding a new design feature based on the existing design feature; finally, one of the design features is removed, and the corresponding node in the attribute diagram is deleted. The above steps constitute the design feature change process. More specifically, the original design feature is... , , , , , , can replace for Then insert You can also delete it. .

[0190] Based on the attribute information of nodes and edges in the design feature attribute graph from step S103, part design changes are defined as geometric feature changes, precision feature changes, and changes in the topological relationships between design features. Geometric feature changes include: changes in face type, face concavity / convexity, face area, edge type, and edge concavity / convexity; precision feature changes include: changes in surface roughness, dimensional tolerances, and geometric tolerances; changes in the relationships between design features include: changes in node distance and coupling relationships.

[0191] S104-2: A similarity matching algorithm for design features based on a case library. Historical cases in the case library differ from target cases in some design features, making the retrieval process ambiguous. To improve matching accuracy, this invention divides similarity matching into design feature node matching and topological relationship matching between nodes, based on the design feature attributes of the parts.

[0192] (1) Design feature node matching.

[0193] For the similarity calculation of design feature nodes, the modified part and the parts in the case library are denoted as PartT and PartC respectively, and a geometric shape pairing matrix of the two parts is constructed:

[0194] (37);

[0195] In the matrix, Indicates the first of Part T Geometric features With Part C j Geometric features If similar, the value is 1; otherwise, it is 0. The matrix is ​​traversed row by row from left to right. When, it indicates that the pair of features Matchable, then delete the first one. i row and number j All elements in the column indicate that the pair of features has been matched and used. This process is repeated until there are no more elements with a value of 1 in the matrix. Based on the similarity calculation results of the designed feature nodes, the top few most similar products are selected.

[0196] (2) Matching of topological relationships between nodes.

[0197] In discrete mathematics, a compatibility class is a set of elements that satisfy a certain compatibility condition. In graph theory, a compatibility class can be understood as nodes or subgraphs in a graph that satisfy a certain similarity condition. The following steps can be used to find the maximum similar connected subgraph of a topological graph:

[0198] 1) Create a topology graph G 1 and GCompatibility table of 2 C’ ;

[0199] 2) Solve the table C’ The largest compatible class (i.e., the largest connected subgraph) in the graph.

[0200] First, a compatibility table is constructed (based on the adjacency attribute graph of the design features of the product to be manufactured and the adjacency attribute graph of the design features of similar products in the case library). All elements of the set of compatible node pairs in the two graphs are determined according to the following rules, and are used as the rows and columns of the compatibility table respectively.

[0201] Rule: For non-leaf nodes, there must be at least two identical topological relationships between pairable nodes, i.e., the distance and coupling relationships must be the same, except for 0;

[0202] Rule: For leaf nodes, there must be at least one topological relationship between any two paired nodes, except for 0.

[0203] In this context, a leaf node refers to an end node in the graph structure that has no child nodes, while a non-leaf node is an intermediate node that has at least one child node.

[0204] The compatibility table indicates the first... i OK j The value of the column (only when) i > j hour (defined), if With a large number of values ​​becoming 0, the size of the compatibility table can be reduced to decrease the computational load. Therefore, the following conditions for reducing the size of the compatibility table are proposed:

[0205] Condition: If the weights of the connections between two nodes are different, then ;

[0206] Condition: For node pairs and ,like or If the value is 0, then the corresponding row and column value is 0. express The first of the figures i One node, the others are similar.

[0207] Based on the correspondence between maximal compatibility classes and complete covers in set theory, a maximal compatibility class in a set refers to the largest subset that satisfies a specific compatibility condition, where all elements satisfy the given condition and no other elements can be added. A complete cover, on the other hand, refers to dividing the set into several subsets (called a cover) that ensure each element in the set belongs to at least one subset, and these subsets satisfy specific conditions (such as disjointness or compatibility). This paper uses the matrix method to calculate the maximal compatibility class, and the relevant theory and calculation process are described below.

[0208] The compatibility table is shown in Table 4. Indicates A's i A vector composed of column elements Indicates the first i In the list j +1 line to the n A vector composed of row elements is represented by a dot product operation. a i ) j and a i The result of the logical operation is:

[0209] (38);

[0210] (39);

[0211] (40);

[0212] Table 4: Compatibility Table

[0213]

[0214] Assuming vector Pairwise compatible, their logical operation is:

[0215] (41);

[0216] In the formula, vector The median value of 1 indicates All elements and Compatible.

[0217] Following the above rules, the compatibility table is scanned column by column from left to right: the elements with a value of 1 in each column are calculated until the vector... All elements are set to zero, resulting in a compatibility class. This process is repeated to extract all compatibility classes. Then, a maximum compatibility test is performed on the obtained compatibility classes, selecting the class with the most nodes that remains connected as the maximum compatibility class (i.e., the maximum similar connected topological subgraph). Finally, the graph is calculated according to the following formula. G 1 and G Similarity of 2:

[0218] (42);

[0219] In the formula, and These represent the total number of nodes in their respective attribute graphs; This represents the number of nodes in the largest similar connected subgraph.

[0220] Based on this, determine the number of units with the same geometric shape, denoted as . g And calculate the geometric similarity:

[0221] (43);

[0222] Furthermore, even if two parts have the same geometry and topological relationship, differences in size specifications can still lead to different manufacturing processes. Therefore, based on Euclidean distance, a formula for calculating geometric similarity is derived:

[0223] (44);

[0224] In the formula, For the first i The influence weight of each geometric shape in the most similar connected topological subgraph;

[0225] (45);

[0226] In the formula, l The total number of parameters for each geometry; , They are respectively X and Y No. i The first geometric shape k One parameter.

[0227] Ultimately, the geometric feature similarity of the cases is:

[0228] (46);

[0229] In the formula, , , These represent the weights of the similarity of the modified part and the parts in the case library in terms of topological relationship, geometric shape, and dimensional parameters to the design features.

[0230] S104-3: Carbon footprint prediction of product manufacturing process based on scenario reconstruction. Through the above similarity calculation, parts with similar design features can be matched from the case library, but the matching results only represent a portion of the design features of similar products. For example... Figure 8 As shown, to support subsequent carbon footprint prediction, the manufacturing process scenarios in similar parts need to be "migrated" to the manufacturing process scenarios of the target case in order to reconstruct the scenario information missing after the change in design features. More specifically, the change node and its connected other nodes are extracted from the design feature attribute map, and the case node with the most similarity to it is searched in the case library. Then, the corresponding unit process scenario is extracted and replaced with the unit process scenario corresponding to the change node and its connected other nodes to complete the reconstruction of the changed design feature scenario.

[0231] More specifically, in the target case scenario, the original state was... After the change, it becomes At this point, the processing feature nodes were changed, and similarity matching was performed to extract similar manufacturing scenarios from the case library. In the case library, the information representation matrix of the first manufacturing process scenario includes... , , ··· , among them , , The modified processing feature points are quite similar to the processing attribute feature map; the information representation matrix of the second manufacturing process scenario includes... , , ··· , among them ··· The modified processing feature points are quite similar to the processing attribute feature map; the information representation matrix of the third manufacturing process scenario includes... , , ··· , among them and The modified processing feature points are quite similar to the modified processing feature points on the processing attribute feature map; The information representation matrix of each manufacturing process scenario includes , , ··· , among them and The modified processing feature points are most similar to the modified processing feature points on the processing attribute feature map; the scene before the modification... of and Replace with and , and obtain the final changed (i.e., the modified manufacturing process scenario information expression matrix).

[0232] Based on the reconstructed scenario data and combined with design feature data, the carbon footprint prediction of the product manufacturing process after design changes is realized according to the carbon footprint quantification model in step S103.

[0233] Figure 9 A product manufacturing process carbon footprint assessment system based on design features is shown, including:

[0234] The design feature extraction unit is configured to: extract design features of the product to be manufactured, the design features including design feature type, geometric features and precision features, and construct a design feature adjacency attribute graph based on the design features;

[0235] The manufacturing scenario determination unit is configured to: determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on the scenario attributes of the manufacturing process of the product to be manufactured;

[0236] The carbon footprint assessment unit is configured to determine the carbon footprint of the product to be manufactured in the entire manufacturing scenario based on the design features and the carbon emission data generated by each manufacturing unit process scenario.

[0237] The carbon footprint prediction unit is configured to: when the design features of the product to be manufactured change, determine the adjacency attribute graph of the changed design features, match similar products from the case library according to the design feature adjacency attribute graph, extract the manufacturing unit process scenarios corresponding to the similar design features of the similar products in the case library to update the manufacturing scenario, and predict the carbon footprint of the changed product to be manufactured according to the updated manufacturing scenario and the changed design features.

[0238] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0239] According to another embodiment of this application, the system described in this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method described in Embodiment 1 on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0240] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0241] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0242] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the carbon footprint of a product manufacturing process based on design features, characterized in that, Includes the following processes: Extract the design features of the product to be manufactured, including design feature type, geometric features and precision features, and construct a design feature adjacency attribute graph based on the design features; Based on the scenario attributes of the manufacturing process of the product to be manufactured, determine multiple manufacturing unit process scenarios under the manufacturing process scenario; Based on the design features and the carbon emission data generated by each manufacturing unit process scenario, the carbon footprint of the product to be manufactured in the entire manufacturing scenario is determined. When the design features of the product to be manufactured change, the adjacency attribute graph of the changed design features is determined, similar products are matched from the case library according to the adjacency attribute graph of the design features, the manufacturing unit process scenario corresponding to the similar design features of the similar products in the case library is extracted to update the manufacturing scenario, and the carbon footprint of the changed product to be manufactured is predicted according to the updated manufacturing scenario and the changed design features. The adjacency attribute graph of the design features is expressed as follows: , Representative design features , Indicates the number of a certain product Each design feature node; ,in , and They represent the first The type, geometric features, and precision features of each design feature, and Indicates the first Pattern interaction information of each design feature The total number of design features, Represents design features and attributes; Indicates the first The geometric features of each design feature are represented in a structured way using an adjacency attribute graph, which includes the properties of faces and edges, such as face area and the concavity and convexity of faces and edges. ,in, , and They represent the first Roughness information, dimensional tolerance information, and geometric tolerance information of each design feature; Determining the carbon footprint of the product to be manufactured across the entire manufacturing scenario includes: The carbon footprint of the entire manufacturing scenario is the sum of the carbon emission data generated by each manufacturing unit process scenario. The carbon emission data generated by any one manufacturing unit process scenario is the sum of energy carbon emission data, material carbon emission data, and logistics and transportation carbon emission data. The energy carbon emission data is the sum of energy carbon emissions during standby, no-load, and cutting processes; the material carbon emission data is the sum of tool wear carbon emissions and cutting fluid loss carbon emissions. The working duration of energy carbon emissions during the cutting process is determined based on the design features.

2. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 1, characterized in that, The adjacency attribute graph is used to structurally represent the attributes of faces and edges in geometric features. The methods for constructing the adjacency attribute graph of geometric features include: Create a face node based on the feature base plane, and then traverse all feature faces and edges of the entire design feature; Each time a face is traversed, a face node is created, and the face's attribute information is extracted. The face type, face area, face concavity / convexity, and number of adjacent faces are stored in the face node. When the common intersection line of two adjacent faces is encountered, an edge is created between the two nodes, and the number and concavity / convexity information of the edge are extracted and stored in the edge. When the common edge is concave, it is marked as 0, and when the common edge is convex, it is marked as 1. After all faces and edges have been traversed, the nodes and edges are connected according to the topological relationships to obtain the adjacency attribute graph of the geometric features.

3. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 1, characterized in that, Based on the scenario attributes of the manufacturing process of the product to be manufactured, multiple manufacturing unit process scenarios are determined within the manufacturing process scenario, including: The manufacturing process of a product consists of multiple manufacturing unit process scenarios with temporal relationships. The unit process scenarios contained in the manufacturing process scenario are arranged and combined according to the processing order to form a manufacturing process scenario information expression matrix.

4. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 1, characterized in that, Based on the design features, the working time for energy and carbon emissions during the cutting process is determined. The outer diameter, end face, and groove of the product to be manufactured are determined. The calculation formulas for the machining time of turning the outer diameter, turning the end face, and turning the groove are expressed as follows: ； ； ； in, The spindle speed for turning outer diameters, end faces, and grooves. For cutting speed, For manufacturing unit processes The large diameter of the workpiece being processed Small diameter, Representing unit projects The processing length; Unit procedure Machining allowance; Feed per revolution; For cutting depth, For machining time of turning the outer diameter, For the machining time of turning the end face, This refers to the machining time for turning the groove.

5. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 1, characterized in that, The design features of the product to be manufactured have been changed, including: changes in geometric features, changes in precision features, and changes in the topological relationships between design features; Geometric feature changes include: changes to face type, face concavity / convexity, face area, edge type, and edge concavity / convexity; precision feature changes include: changes to surface roughness, dimensional tolerance, and geometric tolerance; changes to the topological relationships between design features include: changes to node distance and coupling relationships. Based on the modified design feature adjacency attribute graph, similar products are identified by matching design feature nodes and topological relationships between nodes with products in the case library.

6. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 5, characterized in that, Matching based on design feature nodes includes: Construct a geometric shape pairing matrix by combining the modified product to be manufactured with any product to be matched in the case library. In the geometric shape pairing matrix... Indicates the first of the products to be manufactured The geometric shape feature matches the first... of the product to be matched. If the geometric features are similar, the value is 1; otherwise, it is 0. Traverse the matrix row by row from left to right, when... When, it indicates a feature With features Matchable, delete the first row and number All elements of the column represent characteristics. With features The matched elements have been used; the calculation continues until there are no more elements with a value of 1 in the matrix.

7. The carbon footprint assessment method for product manufacturing processes based on design features as described in claim 6, characterized in that, The matching results of the design feature nodes are used to determine the geometric feature similarity, which is a weighted sum of topological relationship similarity, geometric shape similarity, and geometric shape size similarity.

8. A product manufacturing process carbon footprint assessment system based on design features, characterized in that, include: The design feature extraction unit is configured to: extract design features of the product to be manufactured, the design features including design feature type, geometric features and precision features, and construct a design feature adjacency attribute graph based on the design features; The manufacturing scenario determination unit is configured to: determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on the scenario attributes of the manufacturing process of the product to be manufactured; The carbon footprint assessment unit is configured to determine the carbon footprint of the product to be manufactured in the entire manufacturing scenario based on the design features and the carbon emission data generated by each manufacturing unit process scenario. The carbon footprint prediction unit is configured to: when the design features of the product to be manufactured change, determine the adjacency attribute graph of the changed design features, match similar products from the case library according to the adjacency attribute graph of the design features, extract the manufacturing unit process scenarios corresponding to the similar design features of the similar products in the case library to update the manufacturing scenario, and predict the carbon footprint of the changed product to be manufactured according to the updated manufacturing scenario and the changed design features. The adjacency attribute graph of the design features is expressed as follows: , Representative design features , Indicates the number of a certain product Each design feature node; ,in , and They represent the first The type, geometric features, and precision features of each design feature, and Indicates the first Pattern interaction information of each design feature The total number of design features, Represents design features and attributes; Indicates the first The geometric features of each design feature are represented in a structured way using an adjacency attribute graph, which includes the properties of faces and edges, such as face area and the concavity and convexity of faces and edges. ,in, , and They represent the first Roughness information, dimensional tolerance information, and geometric tolerance information of each design feature; Determining the carbon footprint of the product to be manufactured across the entire manufacturing scenario includes: The carbon footprint of the entire manufacturing scenario is the sum of the carbon emission data generated by each manufacturing unit process scenario. The carbon emission data generated by any one manufacturing unit process scenario is the sum of energy carbon emission data, material carbon emission data, and logistics and transportation carbon emission data. The energy carbon emission data is the sum of energy carbon emissions during standby, no-load, and cutting processes; the material carbon emission data is the sum of tool wear carbon emissions and cutting fluid loss carbon emissions. The working duration of energy carbon emissions during the cutting process is determined based on the design features.

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