Product manufacturing process carbon footprint evaluation method and system based on design characteristics

Abstract

The invention belongs to the technical field of green manufacturing of mechanical and electrical products. The invention provides a product manufacturing process carbon footprint evaluation method and system based on design features, and the method comprises the steps: building a design feature adjacency attribute graph, determining a plurality of manufacturing unit process scenes in a manufacturing process scene, and obtaining a plurality of manufacturing unit process scenes according to the design features and the carbon emission data generated by each manufacturing unit process scene; determining a carbon footprint corresponding to the to-be-manufactured product in the whole manufacturing scene; when the design features are changed, determining an adjacent attribute graph of the changed design features, matching a similar product from the case library according to the adjacent attribute graph of the design features, and extracting a manufacturing unit process scene corresponding to the similar design features of the similar product in the case library to update the manufacturing scene, according to the updated manufacturing scene and the changed design features, carbon footprint prediction of the changed to-be-manufactured product is carried out; according to the method, accurate calculation of the carbon footprint and accurate prediction of the carbon footprint after design feature change are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of green manufacturing of electromechanical products, and in particular to a method and system for evaluating the carbon footprint of a product manufacturing process based on design features. Background Art

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

[0003] The manufacturing process of electromechanical products is a major source of carbon emissions in the manufacturing industry, generating approximately one-third of the total greenhouse gas emissions over the product's entire lifecycle. During new product development, manufacturing companies typically make design changes based on the existing product. During this phase of product design changes, the lack of actual manufacturing process data (such as process parameters and tooling) makes it difficult to support carbon footprint estimation, hindering the product's low-carbon optimization process.

[0004] Currently, researchers have conducted extensive research on design information expression, manufacturing process carbon footprint modeling, and carbon footprint prediction. However, existing solutions still have the following problems: (1) The numerous design features of products, the multi-source heterogeneity of design information, and the complex correlations between design features make it difficult for existing methods to fully express 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 footprints; (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 estimation. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present invention provides a method and system for carbon footprint assessment of product manufacturing processes based on design features. The method achieves accurate expression of product design features according to the geometric features and precision features of the product, and closely links product design features with the carbon footprint calculation of the product to achieve more accurate carbon footprint calculation. When the product design changes, similar products in the case library are matched through similarity calculation to construct an updated manufacturing scenario, thereby achieving more accurate carbon footprint prediction.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for evaluating the carbon footprint of a product manufacturing process based on design features.

[0007] A carbon footprint assessment method for product manufacturing process based on design features, including 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; Determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on scenario attributes of the manufacturing process of the product to be manufactured; Determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured based on the design features and carbon emission data generated by each manufacturing unit process scenario; When the design features of the product to be manufactured change, the adjacency attribute graph of the changed design features is determined, and 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 similar design features of similar products in the case library are extracted to update the manufacturing scenarios. Based on the updated manufacturing scenarios and the changed design features, the carbon footprint of the changed product to be manufactured is predicted.

[0008] In a second aspect, the present invention provides a carbon footprint assessment system for a product manufacturing process based on design features.

[0009] A carbon footprint assessment system for product manufacturing processes based on design features, including: 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; A manufacturing scenario determination unit is configured to: determine a plurality of manufacturing unit process scenarios under a manufacturing process scenario according to scenario attributes of a manufacturing process of a product to be manufactured; The carbon footprint assessment unit is configured to: determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured 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 are changed, 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 similar design features of similar products in the case library to update the manufacturing scenarios, and predict the carbon footprint of the changed product to be manufactured based on the updated manufacturing scenarios and the changed design features.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention innovatively proposes a carbon footprint assessment method for product manufacturing processes based on design features. Based on the geometric and precision features of the product, it achieves accurate expression of product design features, closely links product design features with the carbon footprint calculation of the product, and achieves more accurate carbon footprint calculation. When the product design changes, similar products in the case library are matched through similarity calculation to construct an updated manufacturing scenario, thereby achieving more accurate carbon footprint prediction.

[0011] 2. In view of the characteristics of numerous product design features and complex relationships between features, the present invention proposes a method for constructing a design feature adjacency attribute graph, which defines and characterizes design features from the aspects of geometric features and precision features, thereby achieving a more accurate expression of design features.

[0012] 3. To address the problem that product manufacturing process information is discrete, diverse in types, and lacks systematic association with design features, the present invention proposes a scenario-based manufacturing process information expression measurement, which realizes the unified representation of manufacturing information and ensures the coherence of manufacturing scenarios.

[0013] 4. In view of the fact that the existing manufacturing process carbon footprint modeling methods 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 product manufacturing process carbon footprint quantification strategy is proposed to support the refined accounting of the product manufacturing process carbon footprint.

[0014] 5. To address the problem that after design changes, the design features of new products are difficult to support carbon footprint estimation due to the lack of detailed and complete manufacturing scenario data, the present invention proposes a manufacturing process carbon footprint prediction strategy based on feature similarity matching. By calculating the similarity of the graph structure, similar cases in the case library are matched; scenario reconstruction rules are formulated, and by splitting and reorganizing the unit process, the manufacturing process scenario of the changed product is constructed to support carbon footprint accounting, thereby achieving efficient and accurate prediction of the carbon footprint of new products.

[0015] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1 A schematic diagram of a process for evaluating the carbon footprint of a product manufacturing process based on design features according to an exemplary embodiment of the present invention; Figure 2 A schematic diagram of a process for constructing a geometric feature adjacency attribute graph provided by an exemplary embodiment of the present invention; Figure 3 A schematic diagram of the extraction and expression of precision features provided by an exemplary embodiment of the present invention; Figure 4 A schematic diagram of a design feature adjacency attribute graph construction process provided for an exemplary embodiment of the present invention; Figure 5A schematic diagram of the carbon footprint transfer path of a manufacturing process provided by an exemplary embodiment of the present invention; Figure 6 A schematic diagram of turning processing is provided for an exemplary embodiment of the present invention, wherein: Figure 6 (a) is a schematic diagram of turning the outer circle. Figure 6 (b) is a schematic diagram of the turning end face. Figure 6 Middle (c) is a schematic diagram of the turning groove; Figure 7 A schematic diagram of a design feature attribute graph change process provided by an exemplary embodiment of the present invention; Figure 8 A schematic diagram of a reconstruction of a manufacturing process scenario with a changed design feature provided by an exemplary embodiment of the present invention; Figure 9 A schematic diagram of a system for evaluating the carbon footprint of a product manufacturing process based on design features according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0020] Currently, nearly 70% of a product's manufacturing process carbon footprint is determined during the design phase. During new product development, manufacturing companies typically make design changes based on the original product. During this phase, the lack of actual manufacturing process data (such as process parameters and tooling) makes it difficult to support carbon footprint estimation, hindering the product's low-carbon optimization. Therefore, a design-based carbon footprint modeling and prediction method for product manufacturing processes is urgently needed to provide important technical support for companies during the design and development phase. This approach would primarily address two key aspects: first, enabling early estimation of a new product's carbon footprint, providing data support for low-carbon optimization and improvement; and second, enabling quantitative comparison of the carbon footprints of different design solutions, assisting decision makers in selecting the optimal option.

[0021] In terms of design information expression, some researchers have proposed a multi-level low-carbon design model for the entire product life cycle design scheme. By integrating the function-structure-design feature-processing technology hierarchical architecture, it realizes the correlation mapping with the entire life cycle carbon emissions. However, this method has a low modeling granularity, the correlation between information is fuzzy, and it is difficult to accurately characterize the correlation between design information and carbon footprint.

[0022] In terms of modeling the carbon footprint of the manufacturing process, researchers have modeled the carbon footprint of the manufacturing process from different levels of perspective, covering the industry level, workshop level, and equipment level. At the industry level, based on the economic input-output LCA method, the carbon flow trend of the manufacturing process between industries is quantitatively described. At the workshop level, the focus is on the distribution and dynamic regulation of carbon flows between production units, which plays a guiding role in the workshop's carbon data management and carbon quota planning. At the equipment level, the impact of the machine tool spindle system, auxiliary devices and operating status on the energy consumption of the machine tool is analyzed, and a mathematical model of the energy consumption of the machine tool processing process is constructed. However, the above methods focus on the analysis of the manufacturing process flow, and the modeling granularity is relatively coarse. They do not fully consider the impact of product or component characteristic information on the carbon footprint, making it difficult to achieve comprehensive and accurate quantification of the carbon footprint of the manufacturing process.

[0023] In the area of ​​carbon footprint prediction, some studies have used similarity matching algorithms based on case libraries. By reusing data or knowledge, they effectively integrate existing resources to improve the efficiency of design changes. However, similarity matching relies on a relatively single type of information and fails to consider the similarity of topological relationships between feature information. This results in a low matching rate and makes it difficult to guarantee accurate carbon footprint predictions.

[0024] Therefore, existing methods do not fully consider the impact of product characteristic information on the carbon footprint of the manufacturing process, and lack a unified association model, making it difficult to achieve product characteristic-oriented carbon footprint modeling and accurate prediction.

[0025] In view of the problems existing in the existing solutions, this implementation proposes a carbon footprint assessment method for product manufacturing process based on design features, such as Figure 1 As shown, the following process is included: S101: extracting design features of the product to be manufactured, wherein the design features include design feature type, geometric features, and precision features, and constructing a design feature adjacency attribute graph based on the design features; S102: Determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on scenario attributes of the manufacturing process of the product to be manufactured; S103: Determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured based on the design features and the carbon emission data generated by each manufacturing unit process scenario; S104: When the design features of the product to be manufactured are changed, an 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, and manufacturing unit process scenarios corresponding to similar design features of the similar products in the case library are extracted to update the manufacturing scenarios. Based on the updated manufacturing scenarios and the changed design features, a carbon footprint prediction of the changed product to be manufactured is performed.

[0026] In step S101 of this implementation, specifically, the following steps are included: S101-1: Geometric feature extraction and expression.

[0027] The geometric and topological information contained in the geometric structure model can be expressed in the form of an attribute adjacency graph, with nodes representing geometric feature faces and arcs representing common edges between adjacent faces. When faces and edges are assigned relevant attributes, they can be represented on nodes and edges, specifically expressed as: (1); Where, represents the geometric feature adjacency attribute graph, represents the set of all faces in the geometric feature, ,in Indicates the face; E Represents All adjacent edges in , ,in Display surface with noodles If there is no common edge between them, then ; H Represents a collection of attributes added to faces and edges. .

[0028] As shown in Table 1, the present invention defines the attribute information of nodes and edges in the geometric feature adjacency attribute graph.

[0029] Table 1: Definition of face and edge attributes in geometric feature attribute graph

[0030] To extract the geometric structure information of the product, such as Figure 2 As shown, the present invention proposes a process for constructing a geometric feature adjacency attribute graph based on a feature base surface, which is as follows: Step 1: Create a face node based on the feature base surface, and then traverse all feature faces and edges of the entire design feature; Step 2: Whenever a face is traversed, a face node is created and the face attribute information is extracted. Information such as the face type, face area, face convexity, and number of adjacent faces is stored in the face node. Step 3: When the common intersection of two adjacent faces is reached, an edge is created between the two nodes. Information such as the number of edges and their concavity is extracted and stored in the edge. To make the concavity of the edge clearly visible, concave edges are marked as 0, and convex edges are marked as 1. Step 4: After all feature faces and edges have been traversed, the nodes and edges are connected according to the topological relationship to construct a geometric feature attribute graph.

[0031] S101-2: Precision feature extraction and expression.

[0032] Precision features include roughness, dimensional tolerance, and geometric tolerance. Precision features are dependent on the surface of geometric features. The machined surfaces of different precision features directly influence machining methods, tool selection, and the determination of machining parameters. The area of ​​the machined surface affects the machining time of a design feature, and machining methods and time directly affect the carbon emissions of the product manufacturing process. Therefore, a method is proposed that associates the area of ​​the machined surface with roughness and tolerance and uses this area to represent the precision information of the design feature. Specifically, the areas of machined surfaces with the same roughness grade and the areas of machined surfaces associated with the same tolerance type and tolerance grade are first extracted from the design feature. Then, the areas of surfaces with the same precision type and precision grade are added together. Finally, a one-dimensional vector of the design feature's precision information is constructed, consisting of the areas of machined surfaces of the same precision type but different precision grades. Therefore, each element in SR (Surface Roughness) represents the surface area associated with a roughness grade. Each element in DTA (Dimensional Tolerance) represents the surface area associated with a specific dimensional tolerance grade. Each element of GTA (Geometric Tolerance) represents the surface area associated with a specific geometric tolerance type and grade. Within the geometric feature, all surface areas relevant to precision characteristics are explicitly expressed. The area of ​​the machined surface affects the machining time of a design feature, and the machining method and time directly impact the carbon emissions of the product manufacturing process. Therefore, this paper proposes a method that associates the area of ​​the machined surface with roughness and tolerance, and uses this area to represent the precision information of a design feature. Specifically, first, the areas of machined surfaces with the same roughness level and the areas of machined surfaces associated with the same tolerance type and the same tolerance grade are extracted from the design features; then, the areas of surfaces with the same precision type and the same precision grade are added together; finally, the one-dimensional vector of the design feature precision information is composed of the areas of machined surfaces with the same precision type and different precision grades. Therefore, each element in SR (Surface Roughness) represents the surface area associated with the roughness grade, each element in DTA (Dimensional Tolerance) represents the surface area associated with a specific dimensional tolerance grade, and each element in GTA (Geometric Tolerance) represents the surface area associated with a specific geometric tolerance type and a specific geometric tolerance grade. Figure 3 shown.

[0033] More specifically, Figure 3In the figure, Ra1.6 represents the surface roughness parameter, Ra is the arithmetic average roughness, and a value of 1.6 μm means that the arithmetic average roughness value of the surface is 1.6 microns; and Respectively represent the area of ​​two specific regions, the unit is square millimeter; For a cylindrical hole with a diameter of 40 mm, the upper deviation is +0.036 mm and the lower deviation is -0.011 mm; Indicates a dimension of 10 mm in length, 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 is the roughness parameter of a specific area (S1+S2); DTA = [0 0 0 0 0 0 (1256.64) (1256.64) 00 0 0 0 0 0 0 0 0] is a dimensional tolerance vector, and the two 1256.64s before and after correspond to the area tolerances of areas S1 and S2; GTA is a geometric tolerance matrix, which corresponds 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 specific area S1, and the value 1256.64 in the second row of the matrix represents the cylindricity tolerance corresponding to the specific area S1; the value 1256.64 in the second row of the matrix represents the parallelism tolerance corresponding to the specific area S2; A is the datum symbol.

[0034] S101-3: Design feature extraction and expression. Design feature attribute information includes design feature type, geometric features, and precision features. Their definitions are shown in Table 2.

[0035] Table 2: Design feature attributes

[0036] The design feature adjacency attribute graph of a product is composed of the design feature attributes The relationship between the design features is constructed. The design feature adjacency attribute graph is defined as follows: (2); in, Represents design features, , Indicates a product i design feature nodes; ,in 、 and Respectively represent The type, geometry and precision of each design feature Representing pattern interaction information, a set of array coupling design features The values ​​are the same. A one-hot vector is a coding method for representing discrete data, commonly used in fields such as machine learning and natural language processing. Its characteristic is that only one element in the vector is 1, and the rest 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}. Representing the geometric features of a design feature, the attributes of the faces and edges in the geometric features are structured through the expression of the adjacency attribute graph, covering attribute information such as the area of ​​the face, the convexity of the face and edge, etc. ,in 、 、 Respectively represent Roughness information, dimensional tolerance information and geometric tolerance information of each design feature, Represents a design feature attribute.

[0037] R A set of edges representing relationships between design features, , Represents an edge between two design feature nodes. ,in and Respectively represent the distance and coupling relationship type between two nodes. ,in 、 、 and Respectively indicate whether the two nodes have the following four coupling relationships ( , where 0 represents negative and 1 represents positive): dependency interaction, adjacency interaction, baseline interaction, and pattern interaction. According to the degree of coupling of the four relationships, the distance between the nodes corresponding to the coupling relationship is defined as , which represent the distances between nodes corresponding to dependent interaction, adjacent interaction, benchmark interaction, and pattern interaction relationships, respectively.

[0038] Binary adjacency matrix of association A For illustration MF and R The correlation between and Representation node Neighborhood , Expressed as: (3); The construction process of the product design feature adjacency attribute graph is as follows: First, construct a node set based on the attributes of the design features V; Generate the associated binary adjacency matrix by judging whether the design features are adjacent A .like , , and the surface of the geometric features in the design feature and There are intersecting edges between them, design features and Finally, the edge set is constructed based on the distance and coupling relationship between adjacent design features. E , completing the construction of the property graph.

[0039] More specifically, Figure 4 As shown, the following process is included: Start: start the whole process; Part design model: obtain or create a part design model as the basis for subsequent processing; Machining feature extraction: Extracting machining features from the part design model is one of the key steps in the entire process; Geometric feature extraction: extract geometric features from processing features and construct attribute graphs based on these geometric features; Precision feature extraction: extract precision features from processing features and determine related attributes; Processing feature node construction: construct the extracted processing features into nodes to prepare for subsequent graph structure processing; Determine whether the extraction is completed: Check whether the extraction of all processing features has been completed; If not completed: return to the "Processing Feature Extraction" step and continue to extract the remaining processing features; If completed: go to the next step; Node relationship determination: determine the relationship between each processing feature node; Node distance calculation: calculate the distance between each processing feature node; Construction of adjacency attribute graph of part processing features: Constructing the adjacency attribute graph of part processing features based on node relationships and node distances; End: The entire process ends.

[0040] In step S102 of this implementation, specifically, the following steps are included: S102-1: Manufacturing Unit Process Definition. Each unit in the manufacturing process converts resource and energy inputs into intermediate product outputs, generating environmental emissions (such as wastewater, exhaust gas, and dust). Therefore, the manufacturing unit process is the smallest unit that generates carbon emissions. Specifically defined as: it refers to the complete process of completing a specific step on a specific piece of processing equipment, while maintaining constant process parameters, tools, auxiliary materials, clamping methods, and processing locations.

[0041] S102-2: Manufacturing Unit Process Scenario Information Expression. The manufacturing process scenario is a comprehensive description of the specific environment, technology, processes, and interactions involved in each step of product manufacturing. It covers the entire production chain from rough material to finished product, highlighting the dynamic synergy between equipment, data, processes, and the environment at different stages. Scenario information is composed of multiple attributes, and the manufacturing unit process, as the smallest unit of the manufacturing process, forms the foundation for the entire manufacturing process scenario.

[0042] Based on the definition of the manufacturing unit process scenario in step 1, it can be formalized into a multivariate attribute space. In view of the diverse information types of manufacturing process scenarios, combined with the seven elements of the process scenario (process type, equipment, process object, process parameters, auxiliary materials, energy type, and process description), this paper proposes a mathematical expression for the manufacturing unit process scenario MUPS based on set theory: (4); In the formula, 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).

[0043] S102-3: Manufacturing process scene information expression matrix, product manufacturing process MP Depend on n A manufacturing unit process with a time sequence relationship MUP The composition can be expressed as: (5); in, Representative A manufacturing unit process.

[0044] From formula (5), we can see that the manufacturing process scenario is a complex scenario composed of manufacturing unit processes according to the process route (PR). The 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 sequence to form a manufacturing process scenario information expression matrix MPS , as shown below: (6); In the matrix, nRepresents the number of unit process scenarios contained in a manufacturing process. Each row of the matrix corresponds to a set of manufacturing unit process scenarios, and each column represents a set of various scenario attributes. More specifically, 、 、 、 Respectively represent the first, second, third, The processing type of each unit process scenario; 、 、 、 Respectively represent the first, second, third, Processing equipment for each unit process scenario; 、 、 、 Respectively represent the first, second, third, The processing object of each unit process scene; 、 、 、 Respectively represent the first, second, third, Process parameters for each unit process scenario; 、 、 、 Respectively represent the first, second, third, Supporting materials for each unit process scenario; 、 、 、 Respectively represent the first, second, third, Energy type for each unit process scenario; 、 、 、 Respectively represent the first, second, third, The scenario description of each unit process scenario. The scenario attribute information in each unit process scenario may be the same. Ultimately, through the coupling relationship between the unit process scenarios, a complete product manufacturing process scenario is formed. This matrix form standardizes the complex scenario information of the workshop-level manufacturing process, reflecting the manufacturing process from rough to finished product in a fine-grained manner.

[0045] In step S103 of this implementation, specifically, the following steps are included: S103-1: Determination of the system boundary of the carbon footprint of the manufacturing process. The carbon footprint refers to the total amount of greenhouse gas emissions generated by a product, production process or service system throughout its 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 composed of a plurality of unit processes in an orderly combination according to the process route. The present invention defines it as the range from "gate to gate", that is, the product manufacturing process from blank to finished product. Within this range, the carbon footprint is accumulated by the carbon emissions of several unit processes, and the carbon emissions generated by each unit process are mainly divided into two categories: energy carbon emissions and material carbon emissions.

[0046] In a manufacturing unit process, energy carbon emissions refer to the carbon emissions caused by the consumption of electricity or other energy conversion due to the operation of processing equipment, material transportation, etc. during the manufacturing process; material carbon emissions refer to the carbon emissions generated by cutting tools, cutting fluids, etc. during the preparation, use and disposal process. The carbon footprint of the manufacturing process is essentially the cumulative result of energy flow and material flow in the process of product from blank to finished product. Figure 5 As shown, taking cutting as an example, in this process, the main Each processing equipment needs to complete multiple steps, that is, multiple unit processes. Therefore, the whole process is that the blank is processed from the first unit process of the first equipment to the first unit process of the kth equipment. Each unit process ends with the final product. These unit processes include the input of electricity and auxiliary materials, as well as the output of direct carbon emissions. In addition, the transfer of products between various devices involves logistics and transportation, which also generates carbon emissions.

[0047] More specifically, the blank is processed by processing equipment 1 (including unit process 1, unit process 2 and unit process 3), and then goes through logistics transportation 1 to processing equipment 2 (including unit process 4, unit process 5 and unit process 6) to obtain intermediate parts, which are then transported to processing equipment 3 (including unit process 7, unit process 8 and unit process 9) through logistics transportation 2. The intermediate parts processed by processing equipment 3 are then sent to processing equipment 4 (including unit process 10, unit process 11 and unit process 12) through logistics transportation 3, and then transported to processing equipment 4 (including unit process 10, unit process 11 and unit process 12). , transported to processing equipment (including unit processes , unit process and unit processes ), and finally the finished product is obtained. The processing characteristics of the finished product are 、 ··· Each processing equipment includes the input of electricity and auxiliary materials, and each processing equipment and logistics transportation produces carbon dioxide emissions.

[0048] Analyze the carbon footprint transmission path of the manufacturing process, CF MP It can be expressed as: (7); Where, Indicates the Carbon emissions generated by each unit process, i = 1, 2, 3, ···, n; and Respectively Energy and material carbon emissions generated by each unit process; For the manufacturing process The carbon emissions generated by logistics and transportation of each unit process, Represents the number of unit processes.

[0049] S103-2: Feature-scenario based energy and carbon emission modeling of parts manufacturing process.

[0050] Energy carbon emissions, namely the carbon emissions generated by the electricity consumed by machine tools during operation, can be calculated by multiplying the electricity carbon emission factor by the power consumption. Since the power of machine tools varies significantly under different operating conditions, their energy consumption and corresponding carbon emissions show dynamic changes. By analyzing the power curve of a certain manufacturing unit machine tool, its operation process can be divided into five stages: startup, standby, spindle acceleration and deceleration, no-load and cutting processing stages. Among them, considering that the startup stage and the spindle acceleration and deceleration stage have the characteristics of large energy consumption fluctuations and short duration, the present invention ignores them when performing carbon emission accounting.

[0051] Based on this, the present invention divides the energy carbon emissions during machine tool operation into three stages for quantitative evaluation: standby, no-load, and cutting processing stages. The calculation method for each stage is as follows: (1) Carbon emissions during the standby phase.

[0052] After the machine tool is started, the state in which the basic functional modules are activated but the main drive system is not yet started is called the standby state. The standby stage is mainly used for worktable zero positioning, workpiece transport waiting, workpiece clamping and removal, and tool replacement. The carbon emissions during the standby stage can be calculated using the following formula: (8); Where, is the carbon emission factor of electricity; It is the machine tool standby time; The standby power of the machine tool is equivalent to the operating power of the basic components During stable operation stages such as standby, no-load, and cutting, the power fluctuation of the machine tool is small, and the power in each stage can be approximately regarded as a stable value. Therefore, the standby energy consumption of the machine tool can be expressed as: (9); (2) Carbon emissions during the no-load phase.

[0053] The no-load operating condition is defined as the state in which a machine tool's machining execution units, including the spindle system, feed mechanism, and chip removal device, are in operation but the tool has not yet made contact with the workpiece. Energy consumption in this condition occurs throughout the entire machining cycle, primarily due to the tool's feed and retract movements. Carbon emissions during the no-load operating phase can be calculated using the following formula: (10); (11); Where, The no-load operating power of the machine tool, including the operating power of basic components No-load operating power of cutting-related systems Two parts; It is the no-load phase time.

[0054] In actual cutting, the feed force and feed speed of the feed system have less influence than the spindle system, so their effects are ignored in the calculation. Mainly related to spindle speed The two are related in a quadratic function relationship, which can be expressed as follows: (12); Where, It is the minimum no-load power of the machine tool; 、 It is the spindle speed coefficient, which is related to the machine tool model.

[0055] (3) Carbon emissions during the cutting stage.

[0056] The process of cutting the excess material from the workpiece by the tool is called the cutting process. The carbon emissions calculation formula is: (13); Where, The power of the machine tool during the cutting process; is the cutting processing time.

[0057] During the cutting process, the total system power is mainly composed of the following three parts: basic component operating power , No-load operating power of cutting related systems and material removal power In addition, in the cutting process, due to the load operation of the machining system, additional additional load power will be generated. The power loss and material removal power are approximately in direct proportion, as follows: (14); (15); Where, is the additional load loss coefficient, and its value range is 0.15~0.25.

[0058] Material removal power and cutting force and cutting speed The calculation formula is as follows: (16); The cutting force can be estimated using empirical formulas. For details, please refer to the relevant machining process manuals. The cutting force calculation formulas for common machining types are shown in Table 3.

[0059] Table 3: Cutting force calculation formula

[0060] In summary, the calculation formula of the energy carbon emission quantification model for the manufacturing unit process scenario is as follows: (17); S103-3: Feature-scenario based modeling of material carbon emissions in the parts manufacturing process. Material carbon emissions This includes carbon emissions from tool wear and cutting fluid consumption. These emissions are not directly generated by machining energy consumption, so specific methods are needed to apportion and integrate carbon emissions from these different sources. Design features are components of a part, and each design feature is completed by a processing chain consisting of one or more machining units. This method allocates tool wear and cutting fluid consumption to each machining unit and then integrates and calculates the carbon emissions from the entire part manufacturing process, taking each machining unit as a unit.

[0061] (1) Carbon emissions from tool wear.

[0062] The friction between the tool and the workpiece during cutting generates a large amount of heat. The cutting fluid can cool the tool and remove the heat, but about 20% of the heat is still transferred to the tool, causing it to wear more severely. The carbon emissions from tool wear can be calculated by allocating the carbon emissions generated by the energy consumption of tool preparation to the processing time. The formula is: (18); Where, is the carbon emission of tool wear in the machining unit process; is the carbon emission factor of the tool; is the tool life; For tool quality.

[0063] Tool life is determined by the number of sharpening times N and tool life Calculation, combined formula, the calculation formula is: (19); Where x, y and z are all indices, Represents tool life coefficient.

[0064] (2) Carbon emissions from cutting fluid loss.

[0065] Cutting fluid is an indispensable auxiliary material in the manufacturing process. Common types include oil-based and water-based cutting fluids. This paper takes water-based cutting fluid as an example. Its carbon emission calculation formula is: (20); Where, H The cutting fluid replacement cycle is generally 2 to 3 months; δ is the cutting fluid concentration. The concentration of water-based cutting fluid after dilution is generally 5%; 、 are the carbon emission factors for cutting fluid preparation and waste fluid treatment, respectively; C is the amount of cutting fluid, t c is the cutting processing time.

[0066] S103-4: Unit process processing time calculation method based on feature-scenario.

[0067] Processing time is a key factor affecting carbon emissions, and part design features and scene attributes play a decisive role in it. Therefore, it is necessary to analyze the impact of information such as geometric parameters and process parameters on processing time and divide the time period according to the processing stage. The processing time of a unit process T From the start of workpiece clamping to the end of tool retraction, it includes standby time, cutting time and idle time, of which standby time includes processing preparation and tool change time. The details are as follows: (twenty one); (twenty two); Where, Unit process i The standby time is determined by the clamping time and tool change time composition; Unit process i Dead time; Unit process cutting time.

[0068] (1) Waiting time for preparation before processing .

[0069] During the manufacturing process, pre-processing preparation typically encompasses three key tasks: initial preparation for the first process step, fixture transfers between different processing units, and transition preparation for cross-machine processing. Actual production data indicates that the equipment downtime associated with pre-processing preparation primarily depends on the operator's technical proficiency, and its fluctuation range is relatively controllable, so it can be approximated as a constant, as follows: (twenty three); Where, Unit process The decision variable for whether preparation work is needed, if necessary, the value is 1, otherwise it is 0. Representatives and Unit Processes Related constants.

[0070] (2) Tool change time due to wear .

[0071] During the manufacturing process, when tool wear reaches a certain level, it will affect the processing quality and therefore require tool replacement. Since a tool may be used in multiple unit processes, the wear and tool replacement time for a unit process can be calculated based on the ratio of its cutting processing time to the tool life. The specific formula is as follows: (twenty four); Where, Indicates the average tool change time.

[0072] According to the tool life calculation method, unit process i The tool change time based on wear can be expressed as: (25); (3) No-load time of tool advance and retract .

[0073] In the unit process The idle time caused by the advance and retract operations t i,a The calculation formula is as follows: (26); Where, Unit process The total length of the air cutting path; Unit process Rapid feed rate.

[0074] (4) Cutting time .

[0075] During the manufacturing process, cutting time Cutting time calculation methods vary across different machining types, primarily influenced by material removal (design features) and process parameters (scenario attributes). Furthermore, a cutting time quantification model based on feature-scenario information is established, linking cutting time with feature-scenario information and providing support for refined carbon footprint calculation. This paper analyzes the cutting time calculation model for turning machining as an example.

[0076] like Figure 6 As shown in (a), this unit process is turning the outer circle. By analyzing its motion characteristics and cutting elements, the calculation formula for turning the outer circle can be obtained as follows: (27); Where, Indicates unit project Processing length, in mm; Represents a unit process Machining allowance, in mm; is the spindle speed, in r / min; is the feed per revolution ( Figure 6 Zhongwei , which can be used in each unit process), the unit is mm / r; is the cutting depth ( Figure 6 Zhongwei , which can be used in each unit process), the unit is mm.

[0077] Similarly, we can get Figure 6 The turning end face shown in (b) and Figure 6 The calculation formulas for the machining time of the turning groove shown in (c) are: (28); (29); Spindle speed for external turning, face turning and groove turning ( Figure 6 Zhongwei n , which can be used in each unit process) and cutting line speed ( Figure 6 Zhongwei , which can be used in each unit process) are expressed as follows: (30); (31); (32); Where, is the cutting speed, in m / min; ( Figure 6Zhongwei d , which can be used in each unit process) is a manufacturing unit process i The maximum diameter of the workpiece being processed, ( Figure 6 Zhongwei , which can be used in each unit process) is the minor diameter, in mm.

[0078] In summary, the calculation formulas for machining time for turning the outer circle, turning the end face, and turning the groove are expressed as follows: (33); (34); (35); In step S104 of this implementation, specifically, the following steps are included: S104-1: Determination of the design change type. Based on the expression method of the product design feature attribute graph proposed in step S101, the changed design features can be expressed as: (36); in, Design feature attribute diagrams for the changed parts; is the design feature node after the change; The edges between the design feature nodes after the change, that is, the relationship between the design feature nodes. The ways of changing nodes and edges are mainly divided into six types, namely replacement, insertion and deletion of nodes and edges. Figure 7 As shown in the figure, the replacement, insertion and deletion of nodes together constitute a complete change process. More specifically, starting from the design feature attribute graph in the upper left corner, the first operation is the replacement operation, which refers to replacing one of the design features with a new design feature; followed by the insertion operation, which refers to adding a new design feature based on the original design feature; finally, one of the design features is removed, and the node of the corresponding attribute graph is deleted. The above steps are the change process of the design feature. More specifically, the original design feature is 、 、 、 、 、 , which can be replaced by for Then insert , or you can delete .

[0079] Based on the attribute information of the nodes and edges in the design feature attribute graph in step S103, part design changes are defined as changes in geometric features, precision features, and topological relationships between design features. Geometric feature changes include changes in face type, face concavity, face area, edge type, and edge concavity; precision feature changes include changes in surface roughness, dimensional tolerance, and geometric tolerance; and changes in relationships between design features include changes in node distances and coupling relationships.

[0080] S104-2: A similarity matching algorithm for modified design features based on the case library. Historical cases in the case library differ from target cases in some design features, making the search process ambiguous. To improve matching accuracy, the present invention divides similarity matching into design feature node matching and inter-node topological relationship matching based on the design feature attributes of the parts.

[0081] (1) Design feature node matching.

[0082] For the similarity calculation of design feature nodes, the changed part and the part in the case library are recorded as PartT and PartC respectively, and the geometric shape pairing matrix of the two parts is constructed: (37); In the matrix, Indicates the first geometric features With Part C j geometric features Are they similar? If they are similar, the value is 1, otherwise it is 0. Traverse the matrix row by row from the left to the right. , indicating that the pair of features Matched, then delete i Row and j All elements in the column indicate that the pair of features has been matched and used. This cycle is repeated until there are no more elements with a value of 1 in the matrix. Based on the similarity calculation results of the design feature nodes, the most similar products are screened out.

[0083] (2) Matching of topological relationships between nodes.

[0084] According to the concept of compatibility class 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 a node or subgraph in a graph that satisfies a certain similarity condition. The following steps can be used to solve the maximum similarity connected topological subgraph of a topological graph: 1) Create a topology map G 1 and G 2 compatibility table C’ ; 2) Solution tableC’ The largest compatible class (i.e., the largest connected subgraph) in .

[0085] First, a compatibility table is established (based on the design feature adjacency attribute graph of the product to be manufactured and the design feature adjacency attribute graph of similar products in the case library). All elements of the set of compatible node pairs of the two graphs are determined according to the following rules and used as the rows and columns of the compatibility table respectively.

[0086] Rules: For non-leaf nodes, the pairing nodes must satisfy at least two identical topological relationships, i.e., the distance and coupling relationships are identical, excluding 0. Rule: For leaf nodes, there must be at least one topological relationship between pairable nodes, except 0.

[0087] Among them, a leaf node refers to an end node without child nodes in the graph structure, and a non-leaf node refers to an intermediate node with at least one child node.

[0088] Indicates compatibility table i OK j The value of the column (only if i > j hour is defined), if If a large number of values ​​are changed to 0, the size of the compatibility table can be reduced to reduce the amount of calculation. To this end, the following conditions for reducing the size of the compatibility table are proposed: Condition: If the connection weights of two nodes are different, then ; Condition: For node pairs and ,like or , then the corresponding row and column values ​​are 0, where express Figure 1 i Nodes, others are similar.

[0089] According to the correspondence between maximum compatible classes and complete covers in set theory, a maximum compatible class in a set is the largest subset that satisfies a specific compatibility condition: all its elements meet the given condition, and no additional elements can be added. Complete covers, on the other hand, involve partitioning a set into a number of subsets (called covers), ensuring that every element in the set belongs to at least one subset, and that these subsets meet specific conditions (such as disjointness or compatibility). This paper uses a matrix method to calculate maximum compatible classes. The relevant theory and calculation process are described below.

[0090] As shown in Table 4, Indicates A i A vector of column elements, Indicates the i Columnj +1 line to n A vector composed of row elements, represented by a dot product operation ( a i ) j and a i The logical operation result is: (38); (39); (40); Table 4: Compatibility table

[0091] Assume vector Pairwise compatibility, its logical operation is: (41); In the formula, vector The elements with a median value of 1 indicate All elements in compatible.

[0092] According to the above rules, the method of scanning the compatibility table from left to right is adopted: the elements with a value of 1 in each column are calculated until the vector All elements are reset to zero, and a compatible class is obtained. Repeat this process to extract all compatible classes. Then, the maximum compatibility test is performed on the obtained compatible classes, and the compatible class with the largest number of nodes and maintained connectivity is selected as the maximum compatible 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: (42); Where, and Respectively represent the total number of nodes in their respective property graphs; The number of nodes representing the maximum similar connected topological subgraph.

[0093] On this basis, the number of units with the same geometric shape is determined and recorded as g , and calculate the geometric shape similarity: (43); In addition, even if two parts have the same geometric shape and topological relationship, differences in size specifications may still lead to different manufacturing process scenarios. Therefore, based on the Euclidean distance, the calculation formula for geometric shape size similarity is derived: (44); Where, For the i The influence weight of a geometric shape in the maximum similarity connected topological subgraph; (45); Where, l is the total number of parameters for each geometry; 、 They are X and Y No. i The first geometric shape k parameters.

[0094] Finally, the geometric feature similarity of the cases is: (46); Where, 、 、 Respectively represent the weights of the similarity of the design features between the changed part and the parts in the case library in terms of topological relationship, geometric shape and size parameters.

[0095] S104-3: Carbon footprint prediction of the manufacturing process of the changed product 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 are only partial design features in similar products. Figure 8 As shown in the figure, in order 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 to reconstruct the scenario information missing from the design features after the change. More specifically, the change node and other nodes connected to it are extracted in the design feature attribute graph, and the case node most similar 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 other nodes connected to it to complete the reconstruction of the changed design feature scenario.

[0096] More specifically, in the target case scenario, before the change , after changing to At this time, the processing feature node is changed, similarity matching is performed, and similar manufacturing scenarios in the case library are extracted. In the case library, the information expression matrix of the first manufacturing process scenario includes 、 、 ··· , among which 、 、 The processing feature points after the change are similar in the processing attribute feature diagram; the information expression matrix of the second manufacturing process scenario includes 、 、 ··· , among which ··· The processing feature points after the change are similar in the processing attribute feature map; the information expression matrix of the third manufacturing process scenario includes 、 、 ··· , among which and The processing feature points are similar to the changed processing feature points in the processing attribute feature diagram; The information expression matrix of a manufacturing process scenario includes 、 、 ··· , among which and The processing feature points after the change are most similar in the processing attribute feature map; of and Replace with and , and get the final changed (i.e. the changed manufacturing process scenario information expression matrix).

[0097] Based on the reconstructed scene data and the design feature data, the carbon footprint prediction of the product manufacturing process after the design change is achieved according to the carbon footprint quantification model in step S103.

[0098] Figure 9 A carbon footprint assessment system for a product manufacturing process based on design features is shown, including: A design feature extraction unit is configured to: extract design features of the product to be manufactured, wherein the design features include design feature type, geometric features, and precision features, and construct a design feature adjacency attribute graph based on the design features; A manufacturing scenario determination unit is configured to: determine a plurality of manufacturing unit process scenarios under a manufacturing process scenario according to scenario attributes of a manufacturing process of a product to be manufactured; A carbon footprint assessment unit is configured to: determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured 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 are changed, determine the changed design feature adjacency attribute graph, match similar products from the case library based on the design feature adjacency attribute graph, extract the manufacturing unit process scenarios corresponding to similar design features of the similar products in the case library to update the manufacturing scenarios, and predict the carbon footprint of the changed product to be manufactured based on the updated manufacturing scenarios and the changed design features.

[0099] It is understandable that each of the above-mentioned units can be separately or completely combined into one or several other units to form a unit, or one (or some) of the units can be further divided into multiple functionally smaller units to form a unit, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In actual applications, these functions can also be implemented with the assistance of other units and can be implemented by the collaboration of multiple units.

[0100] According to another embodiment of the present application, the system described in this embodiment can be constructed by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 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, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0101] Those skilled in the art will appreciate that the units and algorithmic steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may 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.

[0102] The above embodiments can be implemented in whole or in part using software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. 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 a computer-readable storage medium or transmitted via 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, optical fiber, digital line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can include magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0103] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for carbon footprint assessment of product manufacturing process based on design features, characterized in that: The following processes are included: Extracting design features of the product to be manufactured, the design features including design feature type, geometric features, and precision features, and constructing a design feature adjacency attribute graph based on the design features; Determine multiple manufacturing unit process scenarios under the manufacturing process scenario based on scenario attributes of the manufacturing process of the product to be manufactured; Determining the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured based on the design features and the carbon emission data generated by each manufacturing unit process scenario; When the design features of the product to be manufactured are changed, an 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, and manufacturing unit process scenarios corresponding to similar design features of the similar products in the case library are extracted to update the manufacturing scenarios. Based on the updated manufacturing scenarios and the changed design features, a carbon footprint prediction is performed on the changed product to be manufactured.

2. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 1, wherein: The expression of the design feature adjacency attribute graph is: , Represents design features, , Indicates a product design feature nodes; ,in 、 and Respectively represent The type, geometry and precision of each design feature Indicates the The pattern interaction information of the design features, is the total number of design features, Represents the design feature attributes; Indicates the The geometric features of each design feature are structurally expressed through the adjacency attribute graph, which covers the area of ​​the surface, the concavity of the surface and the edge. ,in, 、 and Respectively represent The roughness information, dimensional tolerance information and geometric tolerance information of each design feature.

3. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 2, wherein: The attributes of faces and edges in geometric features are structurally expressed through the adjacency attribute graph. The construction methods of the adjacency attribute graph of geometric features include: Create a face node based on the feature base surface, and then traverse all feature faces and edges of the entire design feature; Whenever a face is traversed, a face node is created and the face attribute information is extracted. The face type, face area, face convexity and number of adjacent faces are stored in the face node. When traversing the common intersection line of two adjacent faces, an edge is created between the two nodes, and the number of edges and concavity information are extracted and stored in the edge. When the common edge is concave, it is marked as 0 on the edge, and when the common edge is convex, it is marked as 1; When all faces and edges are traversed, the nodes and edges are connected according to the topological relationship to obtain the adjacency attribute graph of the geometric features.

4. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 1, wherein: According to the scenario attributes of the manufacturing process of the product to be manufactured, multiple manufacturing unit process scenarios under the manufacturing process scenario are determined, including: The manufacturing process of a product consists of multiple manufacturing unit process scenarios with a time sequence relationship. The unit process scenarios contained in the manufacturing process scenario are arranged and combined according to the processing sequence to form a manufacturing process scenario information expression matrix.

5. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 1, wherein: Determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured based on the design features and the carbon emission data generated by each manufacturing unit process scenario, including: The carbon footprint of the entire manufacturing scenario is the sum of the carbon emissions generated by each manufacturing unit process scenario. The carbon emissions generated by any manufacturing unit process scenario is the sum of energy carbon emissions, material carbon emissions, and logistics and transportation carbon emissions. The energy carbon emission data is the sum of the energy carbon emissions in the standby stage, the energy carbon emissions in the no-load stage, and the energy carbon emissions in the cutting processing stage; the material carbon emission data is the sum of the carbon emissions from tool wear and the carbon emissions from cutting fluid loss. The working time of the energy carbon emissions in the cutting processing stage is determined based on the design features.

6. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 5, wherein: Based on the design features, the working time of energy carbon emissions in the cutting process is determined, and the outer circle, end face, and groove of the product to be manufactured are determined. The calculation formulas for the processing time of turning the outer circle, turning the end face, and turning the groove are expressed as follows: ； ； ； in, is the cutting speed, Manufacturing unit process The maximum diameter of the workpiece being processed, For small diameter, Indicates unit project Processing length; Represents a unit process Machining allowance; is the feed per revolution; is the cutting depth, is the machining time for turning the outer circle, is the machining time for turning the end face, is the machining time for turning the groove.

7. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 1, wherein: The design features of the product to be manufactured are changed, including: geometric feature changes, precision feature changes, and topological relationship changes between design features; Geometric feature changes include: face type changes, face concavity changes, face area changes, edge type changes, and edge concavity changes; precision feature changes include: surface roughness changes, dimensional tolerance changes, and geometric tolerance changes; topological relationship changes between design features include: node distance changes and coupling relationship changes; According to the modified design feature adjacency attribute graph and the products in the case library, matching based on design feature nodes and matching based on the topological relationship between nodes is performed to determine similar products.

8. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 7, wherein: Matching based on design feature nodes, including: The changed product to be manufactured is matched with any product to be matched in the case library to construct a geometric shape pairing matrix. In the geometric shape pairing matrix, Indicates the product to be manufactured The geometric shape feature 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 the left to the right. When , it indicates the characteristics and features Matchable, delete Row and All elements of the column, representing features and features Already matched and used, the calculation is repeated until there are no more elements with a value of 1 in the matrix.

9. The method for evaluating the carbon footprint of a product manufacturing process based on design features according to claim 8, wherein: The matching results of the design feature nodes are used to determine the geometric feature similarity, which is a weighted sum of the topological relationship similarity, the geometric shape similarity, and the geometric shape size similarity.

10. A carbon footprint assessment system for product manufacturing process based on design features, characterized in that: include: A design feature extraction unit is configured to: extract design features of the product to be manufactured, wherein the design features include design feature type, geometric features, and precision features, and construct a design feature adjacency attribute graph based on the design features; A manufacturing scenario determination unit is configured to: determine a plurality of manufacturing unit process scenarios under a manufacturing process scenario according to scenario attributes of a manufacturing process of a product to be manufactured; A carbon footprint assessment unit is configured to: determine the carbon footprint of the entire manufacturing scenario corresponding to the product to be manufactured 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 are changed, determine the changed design feature adjacency attribute graph, match similar products from the case library based on the design feature adjacency attribute graph, extract the manufacturing unit process scenarios corresponding to similar design features of the similar products in the case library to update the manufacturing scenarios, and predict the carbon footprint of the changed product to be manufactured based on the updated manufacturing scenarios and the changed design features.

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