Configuration method for vertical CNC honing machine module for carbon emission control

Abstract

The embodiment of the application provides a vertical numerical control honing machine module configuration method for carbon emission. The method comprises the following steps: obtaining product configuration information of a vertical numerical control honing machine; determining concepts, relationships between the concepts and configuration constraint information in the product configuration information; obtaining demand information of a customer; establishing a corresponding optimization configuration model according to the demand information, the concepts, the relationships between the concepts and the configuration constraint information; determining basic facts and rules in the optimization configuration model according to a preset rule inference machine; screening out a candidate instance set meeting the requirements of the customer according to the basic facts and the rules; in the candidate instance set, combining instance sets in different categories and meeting constraint conditions contained in the configuration constraint information to obtain a final configuration instance, and through the multi-layer configuration solving process of configuration knowledge modeling-candidate module reasoning-configuration optimization, the disadvantage of strong subjectivity is overcome, so that the simultaneous optimization of cost and carbon emission can be realized.

Description

Technical Field

[0001] This application relates to the field of honing machine module configuration technology, and more specifically to a method for configuring a vertical CNC honing machine module for carbon emission reduction. Background Technology

[0002] In the field of modular configuration technology for CNC honing machines, current research on modular configuration design typically only considers factors such as cost and performance when combining modules, neglecting carbon emissions. Therefore, existing configurations cannot obtain carbon emission information for each module, and cannot effectively support the acquisition of low-carbon modular configuration solutions. Products designed without sufficient carbon emission information may have high carbon emissions and fail to meet environmental protection and low-carbon requirements. Furthermore, the configuration solution process becomes increasingly complex, the solution space expands rapidly, and existing technologies require a significant time commitment to address such configuration issues. Summary of the Invention

[0003] The purpose of this application is to provide a method for configuring a vertical CNC honing machine module that reduces carbon emissions during the use of the honing machine.

[0004] To achieve the above objectives, the first aspect of this application provides a method for configuring a vertical CNC honing machine module for carbon emission control, comprising: obtaining product configuration information of the vertical CNC honing machine;

[0005] Determine the concepts, relationships between concepts, and configuration constraints in the product configuration information;

[0006] Obtain customer needs information;

[0007] Establish corresponding optimization configuration models based on demand information, concepts, relationships between concepts, and configuration constraint information;

[0008] The inference engine determines the basic facts and rules in the optimal configuration model based on preset rules.

[0009] Based on basic facts and rules, a set of candidate instances that meet the client's requirements is selected;

[0010] In the candidate instance set, instances belonging to different categories that meet the constraints contained in the configuration constraint information are selected and combined to obtain the final configuration instance.

[0011] The above technical solution first obtains product configuration information, and then establishes a corresponding optimized configuration model based on the product configuration information and customer requirements. It then determines a candidate instance set based on an inference engine and inference method, selects an instance set containing constraints from the candidate instance set, and determines the final configuration instance. This establishes a multi-layered configuration solution process of configuration knowledge modeling, candidate module inference, and configuration optimization, overcoming the drawbacks of strong subjectivity, thereby enabling simultaneous optimization of cost and carbon emissions.

[0012] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0014] Figure 1A The illustration shows a schematic flowchart of a vertical CNC honing machine module configuration method for carbon emissions according to an embodiment of this application;

[0015] Figure 1B This schematically illustrates a configuration solution framework diagram according to an embodiment of this application;

[0016] Figure 2 This illustration schematically shows a diagram illustrating concepts and their relationships in the field of product configuration according to embodiments of this application;

[0017] Figure 3 The diagram illustrates a solution system built by a rule-based inference engine according to an embodiment of this application.

[0018] Figure 4 This illustration schematically shows a method for specifically encoding chromosomes according to an embodiment of this application;

[0019] Figure 5 This illustration schematically shows a crossover process at the gene exchange location according to an embodiment of this application;

[0020] Figure 6 This illustration schematically shows a variation process at two variation points according to an embodiment of this application;

[0021] Figure 7 The schematic diagram illustrates the operation flow of the NSGA-II algorithm according to an embodiment of this application;

[0022] Figure 8 The diagram illustrates the average fitness change curve of the population during the optimization process according to an embodiment of this application;

[0023] Figure 9 The diagram illustrates the optimized Pareto front and the optimal solution according to an embodiment of this application.

[0024] Figure 10 The diagram illustrates the change curve of the average fitness (cost) of the population during the solution process according to the embodiments of the application;

[0025] Figure 11 A schematic diagram illustrating the module list of configuration results according to an embodiment of this application is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Figure 1 schematically illustrates a flowchart of a vertical CNC honing machine module configuration method for carbon emissions according to an embodiment of this application. Figure 1 is also a flowchart of a file update method in one embodiment. It should be understood that although the steps in the flowchart of Figure 1 are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in Figure 1 may include multiple sub-steps or multiple stages, which are not necessarily completed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps. Figure 1A As shown in one embodiment of this application, a method for configuring a vertical CNC honing machine module for carbon emission reduction is provided, including the following steps:

[0028] Step 101: Obtain the product configuration information of the vertical CNC honing machine.

[0029] Step 102: Determine the concepts, relationships between concepts, and configuration constraints in the product configuration information.

[0030] Step 103: Obtain customer needs information.

[0031] Step 104: Establish the corresponding optimized configuration model based on the demand information, concepts, the relationships between concepts, and configuration constraint information.

[0032] Step 105: Determine the basic facts and rules in the optimized configuration model based on the preset rule inference engine.

[0033] Step 106: Select a set of candidate instances that meet the customer's requirements based on basic facts and rules.

[0034] Step 107: In the candidate instance set, select the instances that are modules in different categories and meet the constraints contained in the configuration constraint information and combine them to obtain the final configuration instance.

[0035] Figure 1B This diagram illustrates the overall framework for configuration solving according to an embodiment of this application. In configuring a vertical CNC honing machine, the first step is to model and represent the configuration knowledge, including product structure composition information, carbon emission information, design constraint information, and customer requirement information. Then, based on the vertical CNC honing machine configuration model, a certain reasoning method is used to reason and solve the modules, thereby obtaining candidate module instances that meet user requirements and design constraints. Finally, using the candidate modules and their matching relationships as input, a multi-objective intelligent optimization algorithm is used to optimize the configuration scheme. Specifically, before optimizing the configuration of a vertical CNC honing machine, the product structure, design constraints, and customer requirements involved in the configuration must first be clearly and completely modeled and described using a standardized modeling language, thereby providing a complete knowledge base for the configuration.

[0036] In one embodiment, Figure 2This diagram illustrates the concepts and their relationships in the product configuration field according to embodiments of this application. As shown, the configuration ontology is described using Web Ontology Language (OWL), and Protégé software is used as a visualization tool for ontology editing to assist in the rapid creation and reasoning of the product configuration ontology. Product configuration information includes at least one of the following: configuration schemes, modules, interfaces, attributes, resources, constraints, and customer requirements. Relationships between concepts include at least one of the following: composition relationship (hasPart), subclassing relationship (subClassOf), interface relationship (hasPort), attribute relationship (hasProperty), connection relationship (mayConnect), resource consumption and production relationship (consume and produce), and constraint relationship (hasConstraint). Specifically, a configuration scheme is a product obtained by combining modules, and its relationship with modules is represented by hasPart. Different customer requirement inputs will result in different configuration scheme instances; a module is the basic unit of product configuration, and multiple module instances can be formed based on different module attribute values; an interface is the medium connecting modules, and two modules can be combined only when interfaces can be connected. The relationship between modules and interfaces is expressed using the `hasPort` relationship, and the connection relationship between interfaces is described using the `mayConnect` relationship. Attributes: The concept of attributes describes certain characteristic parameters possessed by modules and interfaces. Examples include the quality and carbon emissions of a module, and the type and location of an interface. Attribute values ​​can take various types, such as numeric, string, and date values. The relationship between modules or interfaces and attributes is described using the `hasProperty` relationship. Resources: The concept of resources represents the services or functions that a module can produce or provide, which can be quantified by quantitative attributes. To some extent, product configuration is a resource balancing problem; the resources consumed by a module cannot exceed the resources provided by other modules. Module resource consumption and resource provision are described using `consume` and `produce` relationships, respectively. Constraints: This concept describes the restrictive conditions on the concepts themselves or the dependencies between concepts during product configuration. Constraints can be logical or mathematical. The main constraints in the configuration process include existence constraints, incompatibility constraints, necessity constraints, resource constraints, and interface constraints. Customer requirements: These describe the requirements put forward by the customer, such as the selection requirements for optional components, or the requirement that certain modules must or cannot appear in the configuration solution. Customer requirements are an important part of the configuration process and will participate in the configuration process as customer requirement constraints. isa represents the class object to which the instance belongs.

[0037] There are several ways to describe an ontology using the Web Ontology Language (OWL):

[0038] 1) Concepts such as configuration schemes, modules, interfaces, resources, and user requirements in product configuration will be expressed using "classes" in OWL;

[0039] 2) Instances of the concept will be expressed using the OWL term "Individual";

[0040] 3) The relationships between concepts are described using the "ObjectProperty" form in OWL;

[0041] 4) The attributes of a concept are expressed using "DataProperty" in OWL;

[0042] 5) Restrictive conditions and constraints on concepts (including user requirement constraints) will be expressed using "attribute constraints" in OWL and custom Semantic Web Rule Language (SWRL) rules.

[0043] In one embodiment, to reduce the solution space for configuration optimization, firstly, based on customer requirements information, the candidate modules are inferred and solved using a rule-based reasoning method based on the configuration ontology and configuration rules of the vertical CNC honing machine to obtain a set of candidate modules that meet user requirements. Then, the NSGA-II algorithm is used to solve the configuration scheme of the vertical CNC honing machine.

[0044] This application uses a rule-based inference engine to reason about candidate modules. Figure 3 This illustration schematically shows a solution system built using a rule-based inference engine according to an embodiment of this application. Before inference, instance information of concepts in the product configuration ontology, such as module instances and customer requirement instances, is first converted into basic facts for rule-based inference. Relationships, axioms, and SWRL constraints in the ontology are converted into rules in the rule base. This completes the conversion of the configuration ontology and rules. Then, the rule-based inference engine can be run based on these facts and rules to solve for candidate modules, thereby obtaining a set of all module instances that meet the specific requirements of the customer. However, since the ontology's ability to express constraints is relatively weak, this invention uses SWRL rules to express the constraint knowledge in the product configuration. The combination of ontology and SWRL rules achieves a complete expression of the configuration knowledge of the opposing CNC honing machine. The constraints in the product configuration process mainly include compatibility constraints, interface constraints, resource constraints, and customer requirement constraints. The SWRL rule expression methods for these configuration constraints include the following constraint types:

[0045] 1) Compatibility constraints

[0046] This includes incompatibility constraints and necessity constraints. Incompatibility constraints refer to the inability of modules to coexist; necessity constraints mean that the existence of one module requires the existence of another related module. For example, in a vertical CNC honing machine, if the honing head module has a dual-feed feed type, then the feed type of the connecting rod module connected to it must also be dual-feed. The SWRL rule expression for this constraint is as follows:

[0047] Honing head (? Honing head 1) ∧ Feed type (? Honing head 1, double feed) ∧ Connecting rod (? Connecting rod 1) ∧ Feed type (? Connecting rod 1, double feed) → Can be used with (? Honing head 1, ? Connecting rod 1)

[0048] 2) Interface constraints

[0049] During configuration, the two modules can only connect if their interface types match. For example, if the bed module's interface is a bearing, then the corresponding table module's interface should be an axis. The SWRL rule expression for this interface constraint is as follows:

[0050] Bed (? Bed 1) ∧ Bearing Interface (? Bearing Interface 1) ∧ Contains Interface (? Bed 1, ? Bearing Interface 1) ∧ Worktable (? Worktable 1) ∧ Axis Interface (? Axis Interface 1) ∧ Contains Interface (? Worktable 1, ? Axis Interface 1) → Can be matched with (? Bed 1, ? Worktable 1)

[0051] 3) Resource constraints

[0052] Resource constraints indicate that the amount of resources consumed by certain modules in a configuration scheme cannot exceed the total amount of resources provided by other modules; that is, resource balance must be satisfied. For example, in a CNC honing machine, the stroke of the hydraulic cylinder must be less than the total length of the column guide rail. The SWRL rule expression for this constraint is as follows:

[0053] Hydraulic cylinder (? Hydraulic cylinder 1) ∧ Cylinder stroke (? Hydraulic cylinder 1, ? S) ∧ Support module (? Support 1) ∧ Guide rail interface (? Guide rail interface 1) ∧ Includes interface (? Support 1, ? Guide rail interface 1) ∧ Interface length (? Guide rail interface 1, ? L) ∧ lessThan (? S, ? L) → Can be matched with (? Hydraulic cylinder 1, ? Support 1)

[0054] (4) Customer demand constraints

[0055] Customer requirements, as an important and unique constraint, will interact with the constraints mentioned above in the module configuration process. For example, if the customer requires a very high surface finish, the abrasive material for the corresponding honing head should be a superhard material. In Protege, this requirement constraint can be written as the following SWRL rule:

[0056] Customer requirements (? Customer requirement 1) ∧ Surface quality (? Customer requirement 1, very high) ∧ Honing head (? Honing head 1) ∧ Abrasive material (? Honing head 1, superhard material) → Candidate honing head (? Honing head 1)

[0057] It should be noted that the "?" in the above text is a rule expression and not a formatting error. Based on the SWRL rule expression method for product configuration constraints, configuration rules for the vertical CNC honing machine product are established in Protégé, thus providing support for reasoning in subsequent modules.

[0058] Pellet, developed by the MindSwap Labs, is a rule-based reasoning program based on descriptive logic. It can be combined with Protégé to reason about implicit knowledge within ontology and SWRL rules. This invention uses the Pellet inference engine to solve candidate modules. The solution process is as follows: First, user requirement information is obtained and entered into the configuration ontology as customer requirement instances. SWRL rules are then used to express the customer requirement information. Next, based on the product configuration ontology and configuration rules, the Pellet inference engine is invoked to reason about module instances that meet the above requirements, resulting in a set of all candidate modules that satisfy the constraints. In one embodiment, the configuration optimization of a vertical CNC honing machine module considering carbon emissions is based on and supported by the modular architecture of the vertical CNC honing machine and a module library containing carbon emission information. During the modular configuration process, the vertical CNC honing machine is divided into several functional modules with different functions and structures, and these modules are connected through interfaces. Each functional module generally has multiple module instances with different costs and carbon emissions for users to choose from. When selecting and combining modules, the constraints between modules and customer requirements need to be considered to obtain an effective configuration scheme. Based on the above analysis, the configuration optimization problem of vertical CNC honing machines considering carbon emissions can be described as follows: Based on the customer's personalized needs, and under the premise of satisfying various constraints, select module instances from different categories of modules of the vertical CNC honing machine to form a configuration scheme, achieving optimization of both cost and carbon emissions. Based on the above description of the multi-objective product configuration optimization problem, establishing a multi-objective configuration optimization model considering carbon emissions with cost and carbon emissions as objectives requires determining the optimization model and optimization variables.

[0059] The optimization variables include formula (1):

[0060]

[0061] Where N represents the number of module categories contained in the product, N i The total number of module instances representing the i-th type of module; the vector-form optimization variables for the product configuration problem include formula (2):

[0062]

[0063] The optimization objective is to minimize the carbon emissions and cost of the configuration scheme, as shown in formula (3):

[0064]

[0065] Among them, CE product (X) represents the carbon emissions of configuration scheme X; C product (X) represents the total cost of configuration scheme X. The carbon emissions of configuration scheme X are calculated using formula (4).

[0066]

[0067] Where: CE product (X) represents the carbon emissions of configuration scheme X; N represents the number of modules contained in the vertical CNC honing machine product; N i This represents the total number of module instances contained in the i-th type of module; Represents a module instance Carbon emissions.

[0068] The cost of the configuration scheme mainly consists of two parts: the cost of its constituent modules and the assembly cost. The calculation method includes formula (5):

[0069]

[0070] Among them, C product (X) represents the total cost of configuration scheme X for the vertical CNC honing machine; N represents the total number of modules contained in the vertical CNC honing machine product; N i This represents the total number of module instances contained in the i-th type of module; Represents a module instance Whether it is selected, 1 indicates selection, 0 indicates otherwise; Represents a module instance The cost; CA represents the average assembly cost between modules.

[0071] In one embodiment, the constraints include: design constraints from the product itself, mainly including selection constraints, necessity constraints, and incompatibility constraints; and customer demand constraints, mainly including price constraints and delivery time constraints.

[0072] Specifically, this includes: a) selection constraints

[0073] In the final configuration, only one module instance can be selected for each category, i.e.

[0074]

[0075] b) Necessity constraints

[0076] Assuming a module instance If a module instance is selected to appear in the configuration solution, then the module instance... If it must also be selected, then this requirement constraint can be expressed as follows:

[0077]

[0078] c) Incompatible constraints

[0079] Assuming a module instance and modules If they are incompatible, the relationship can be represented as follows:

[0080]

[0081] d) Price constraints

[0082] (1+α)C product (X)≤C max (9)

[0083] In the formula: α—the firm's profit margin; C product (X) – Total cost of the configuration plan; C max —The highest price the customer can afford.

[0084] e) Delivery time constraints

[0085] T(X)≤T max (10)

[0086] In the formula: T(X) — delivery date of the product; T max —The maximum delivery time required by the customer.

[0087] In one embodiment, the problem is either transformed into a single-objective optimization problem by weighted optimization or by first finding the Pareto optimal solution and then transforming the problem into a single-objective problem or setting a new objective to solve it. Both of these approaches suffer from the drawback of strong subjectivity. To address these shortcomings of traditional methods, this invention first uses a multi-objective optimization algorithm to obtain the Pareto optimal set of the problem. Then, the ratio of the value of a solution in the Pareto optimal set for a single objective to the optimal value of that objective in the Pareto optimal set is used to measure how close the non-dominated solution is to the optimality of that objective. The product of these ratios for each objective is used as the criterion for evaluating the non-dominated solution, as shown in Formula 11. Since the objectives of this patent are all minimization, the smaller the value of this evaluation criterion, the better. The solution with the smallest value of this criterion is considered the optimal solution in the Pareto solution set.

[0088]

[0089] In the formula: F(X) best —Minimum ratio coefficient; f1(X) —Value of solution X under objective 1; f1(X) min —The optimal value of objective 1 in the Pareto solution set; f2(X) —The value of solution X under objective 1; f2(X) min —The optimal value of objective 2 in the Pareto solution set.

[0090] In one embodiment, this application employs a non-dominated sorting genetic algorithm to solve a multi-objective configuration optimization problem, using the NSGA-II algorithm design to simultaneously optimize cost and carbon emissions. This algorithm achieves chromosome stratification in the population by rapidly sorting the dominance relationships between chromosomes, uses a crowding operator to sort chromosomes within the same non-dominated level, and incorporates an elite retention strategy to preserve superior individuals in the population, thereby accelerating the algorithm's convergence towards Pareto optimality. The key technologies of the algorithm are described in detail below:

[0091] (1) Chromosome coding scheme

[0092] This application uses integers to encode chromosomes. Figure 4 This diagram illustrates a specific method for encoding chromosomes according to an embodiment of this application. In the chromosome, each gene represents a module instance, and the number of genes represents the number of module categories in the product, i.e., the number of functional modules that need to be configured. The value on the gene indicates which instance of the corresponding functional module is selected for product configuration. The gene encoding value at each position does not exceed the total number of instances of the functional module represented by that position. For example, for... Figure 4 The chromosome in the image has seven genes, indicating that the product has seven functional modules that need to be configured with instances. The gene value at the third position is 'c', meaning that the module instance with sequence number 'c' in the third functional module is selected. Since the number of selectable instances for the third module is C, the value of the third gene can vary from 1 to C.

[0093] (2) Constraint handling

[0094] For chromosomes that do not meet the constraints, a penalty function method is used to process their objective function values. For the i-th objective, the penalty function... The structure is as follows:

[0095]

[0096]

[0097] In the formula: —The value of the objective function after penalty; f i(X) — the value of the i-th dimension objective function; p i (X) — Penalty value of the i-th dimension objective function; P i —Penalty constant.

[0098] (3) Non-dominated sorting operator

[0099] The non-dominated sorting operator determines the Rank value of a chromosome based on its dominance relationship within the population, thereby achieving chromosome stratification. The execution flow of the non-dominated sorting operator is as follows:

[0100] a) Initialize the current non-dominated hierarchy number R = 1;

[0101] b) Randomly select a chromosome from the current population;

[0102] c) Compare the dominance relationship between the chromosome and other chromosomes in the population. If the chromosome is not dominated by any chromosome in the current population, set its non-dominance Rank value to R.

[0103] d) Repeat steps b) and c) for the remaining chromosomes in the population until all chromosomes with Rank = R are found;

[0104] e) Remove chromosomes with Rank = R from the population to form a new population;

[0105] f) Increment the Rank value by 1. If the number of chromosomes in the new population is not 0, repeat steps b) to e) for the new population; otherwise, end the process.

[0106] (4) Crowding Calculation

[0107] Crowding degree describes the degree of clustering of chromosome target values ​​within the same non-dominated level. The crowding degree of a chromosome at a given target value is represented by the difference between the two adjacent chromosomes at that target value, and it is calculated as follows:

[0108] a) Sort chromosomes in a non-dominated hierarchy in ascending order according to a certain objective;

[0109] b) Set the crowding degree of the two chromosomes at the left and right boundaries to infinity;

[0110] c) The crowding of a chromosome at the target value is the difference between the target values ​​of the two adjacent chromosomes.

[0111] The crowding degree of a chromosome is the sum of its crowding degree across all target values.

[0112] (5) Crossover and mutation operators

[0113] The crossover operator uses a two-point crossover method, that is, randomly selecting two points and swapping the positions of the genes at these two points. Figure 5 The diagram illustrates a crossover process at gene exchange locations according to an embodiment of this application. The mutation operator selects two mutation points, that is, it randomly selects two mutation points and then changes the gene value corresponding to those points. Figure 6 The illustration shows a schematic diagram of the mutation process of two mutation points according to an embodiment of this application. Figure 7 The schematic diagram illustrates the operation flow of the NSGA-II algorithm according to an embodiment of this application.

[0114] The above configuration optimization algorithm can be used to obtain the optimal configuration result that considers multiple objectives simultaneously.

[0115] The following example, using the modular configuration design of a 2MK2263×200 vertical CNC honing machine, illustrates the configuration process and method of this invention. The product configuration optimization is carried out in two steps: first, using the customer's requirements for the honing machine as input, candidate modules and their matching relationships are deduced; then, the deduced candidate modules and their matching relationships are used as input for module configuration optimization.

[0116] The requirements for vertical CNC honing machines include a honing hole diameter range of Ф200~Ф630mm, a maximum honing hole depth of 2000mm, and very high surface quality requirements for the machined holes. Specific requirements are shown in Table 1. The following explanation uses the spindle module's inference as an example to illustrate the process of reasoning and solving for candidate modules.

[0117] Table 1 Requirements for Vertical CNC Honing Machines

[0118]

[0119]

[0120] The main requirements related to the spindle module's inference in the customer requirements include spindle rotation speed, honing hole diameter, surface quality, and raster angle. Based on these parameters, the minimum motor power is calculated to be 8.8kW, the minimum spindle speed to be 10r / min, and the maximum spindle speed to be 50r / min. Furthermore, the spindle head's feed mechanism must be a dual-feed system. These parameters are then input into the customer requirements instance of the ontology as inference input.

[0121] Based on the above input, the Pellet rule inference engine is run to infer the candidate modules. The main rules used in the inference process are as follows:

[0122] UserRequirement(userReq1)∧zzFeedType(userReq1,?feedtype1)∧

[0123] ZZModule(?zhuzhou)∧zzFeedType(?userReq1,?feedtype1)∧

[0124] minSpindleSpeed(UserReq1,?wmin0)∧maxSpindleSpeed(UserReq1,?wmax0)∧

[0125] zzMinSpindleSpeed(?zhuzhou,?wmin)∧lessThanOrEqual(?wmin,?wmin0)∧

[0126] zzMaxSpindleSpeed(?zhuzhou,?wmax)∧greaterThanOrEqual(?wax,?wmax0)∧

[0127] zzMotorPower(UserReq1,?P0)∧

[0128] zzMotorPower(?zhuzhou,?P)∧greaterThanOrEqual(?P,?P0)

[0129] →CandidateModule(?zhuzhou)

[0130] The above rule states that a candidate spindle module instance should simultaneously meet three conditions:

[0131] (1) The feed type of the candidate spindle module is the same as the feed type required by the user;

[0132] (2) The minimum rotational speed of the candidate spindle module is less than or equal to the minimum rotational speed required by the customer, and the maximum rotational speed of the spindle module is greater than or equal to the maximum rotational speed required by the customer.

[0133] (3) The power of the drive motor of the candidate spindle module should be greater than or equal to the spindle motor power required by the customer.

[0134] Based on the above rules, the Pellet inference engine inferred some information about the candidate spindle module instances that meet the customer's needs, as shown in Table 2:

[0135] Table 2 Information on Candidate Spindle Modules

[0136]

[0137] After inferring candidate modules, the Pellet inference engine further infers the matching relationships between modules based on the constraint rules between them, determining whether the modules satisfy constraints such as interface constraints and resource constraints. The constraint reasoning is illustrated using interface matching as an example. Two interfaces (assumed to be interface 1 and interface 2) must meet the following conditions to be matched:

[0138] (1) The target module code of interface 1 must be the same as the module category code of the module to which interface 2 belongs, and the target module code of interface 2 must be the same as the module category code of the module to which interface 1 belongs.

[0139] (2) The interface types of Interface 1 and Interface 2 must match;

[0140] (3) The interface sizes of Interface 1 and Interface 2 must be the same.

[0141] The matching rules between the pin shaft interface and the pin shaft hole interface are as follows:

[0142] PinShaftPort(?ps)∧isPortOf(?ps,?mod3)∧moduleClassCode(?mod3,?mcc3)∧

[0143] portObjectModuleType(?ps,?omt3)∧portDiameter(?ps,?d1)∧

[0144] PinShaftHolePort(?psh)∧isPortOf(?psh,?mod4)∧moduleClassCode(?mod4,?mcc4)∧

[0145] portObjectModuleType(?psh,?omt4)∧portDiameter(?psh,?d2)∧

[0146] equal(?mcc3,?omt4)∧equal(?mcc4,?omt3)∧equal(?d1,?d2)∧equal(?ft3,?ft4)

[0147] →mayConnect(?ps,?psh)

[0148] Based on the rule above, the matching relationship between the candidate honing head module and the candidate connecting rod module can be deduced, as shown in Table 3. The deduction of the matching relationship between other modules is similar and will not be repeated here. The deduced matching relationship between modules will be used as a constraint condition in the configuration optimization process for optimization.

[0149] Table 3. Interface matching relationship between candidate honing head module and candidate connecting rod module

[0150] Matchable Honing head 11 Honing head 14 Honing head 17 Honing head 20 Honing head 23 Link 11 yes yes yes yes yes Link 12 yes yes yes yes yes Link 13 yes yes yes yes yes Link 14 yes yes yes yes yes Linkage 15 no yes yes yes no

[0151] The reasoning process for other modules is similar to that described above. Table 4 shows all the candidate modules that ultimately meet the customer's needs. These candidate modules will serve as input for configuration optimization, and the optimal configuration scheme that meets the customer's requirements will be determined by combining these modules.

[0152] Table 4. Examples of Candidate Modules

[0153]

[0154]

[0155] Using the candidate module set obtained above as input, and aiming to minimize cost and carbon emissions, the NSGA-II algorithm was employed to optimize the configuration scheme of the vertical CNC honing machine. Multiple sets of algorithm parameter experiments were conducted, and it was found that the following parameters yielded better results: initial population size of 100, maximum number of iterations of 120, crossover probability of 0.8, mutation percentage of 0.2, and mutation probability of 0.1. The encoding, cost, and carbon emission information of the candidate modules are shown in Table 4.

[0156] Based on the above information, the NSGA-II multi-objective genetic algorithm was written using MATLAB R2014b to optimize the configuration scheme. Figure 8 The diagram illustrates the change curve of the average fitness of the population during the optimization process according to an embodiment of this application. Figure 9 The diagram illustrates the Pareto front and optimal solution obtained after optimization according to the embodiments of this application. In the diagram, "dots" represent the objective function values ​​of the Pareto front during the iteration process, "star points" represent the objective function values ​​of the final Pareto optimal solution set, and "circles" mark the solutions as the optimal configuration schemes. The components of the optimal configuration scheme module are shown in Table 5, with a carbon emission of 303,979.52 kg CO2 and a cost of 443,000 yuan.

[0157] Table 5 Optimal Configuration Scheme Information

[0158]

[0159] Using the same set of candidate modules as input, the single-objective genetic algorithm (GA) is employed to perform single-objective optimization with the goal of minimizing cost. Figure 10The diagram illustrates the population average fitness (cost) change curve during the solution process according to the application embodiment. The lowest cost configuration scheme is 43,600 yuan, with a corresponding carbon emission of 332,049.17 kg CO2. The modules it contains are shown in Table 6.

[0160] Table 6. Module Composition of the Single-Objective (Cost) Optimal Configuration Scheme

[0161]

[0162]

[0163] To facilitate the comparison between the optimal configuration scheme that considers both cost and carbon emissions and the optimal configuration scheme that only considers cost, the information of the two schemes is listed in Table 7. The comparison shows that the cost of the multi-objective optimal configuration scheme is 1.58% higher than that of the single-objective optimal configuration scheme that only considers cost, which is a very small increase. Its carbon emissions are reduced by 9.23% compared with the single-objective optimal scheme. This shows that the multi-objective configuration optimization scheme can simultaneously take into account both cost and carbon emissions, and achieve a balance between cost and carbon emissions.

[0164] Through the above multi-objective configuration optimization process, the optimal configuration result that simultaneously considers cost and carbon emissions was obtained. Figure 11 A schematic diagram illustrating the module list of configuration results according to an embodiment of this application is shown.

[0165] Table 7. Module Composition of the Single-Objective (Cost) Optimal Configuration Scheme

[0166] project Cost / yuan Carbon emissions / kgCO2 Multi-objective optimal configuration scheme 443000 303979.52 Cost-optimal (low-cost) configuration 436000 332049.17

[0167] Based on the analysis of the module configuration process of vertical CNC honing machine, this application establishes a multi-layer configuration solution process of configuration knowledge modeling, candidate module reasoning, and configuration optimization, and constructs a multi-objective configuration optimization method for vertical CNC honing machine that considers carbon emissions, which can simultaneously take into account cost and carbon emissions.

[0168] First, an ontology- and rule-based approach to modeling and representing product configuration knowledge is adopted. The ontology-based approach uses terminology and its relationships to represent product configuration knowledge in a unified way, improving the sharing and reusability of configuration knowledge. On this basis, rules are combined to supplement configuration constraint knowledge, thereby establishing a complete product configuration knowledge model.

[0169] Secondly, to reduce the solution space for configuration optimization, based on customer requirements, the Pellet inference engine is used to reason and solve for candidate modules based on the configuration ontology and configuration rules of the vertical CNC honing machine. This enables reasoning about implicit knowledge and obtains a set of candidate modules that meet user requirements.

[0170] Finally, a multi-objective non-dominated sorting genetic algorithm is used to solve the multi-objective configuration optimization problem of this patent. The Pareto optimal set is obtained, and the ratio of the value of a solution in the optimal set on a single objective to the optimal value of that objective in the Pareto optimal set is used to measure the degree to which the non-dominated solution is close to the optimality of that objective. The product of the ratios of the non-dominated solution under each objective is used as the standard for evaluating the non-dominated solution. This overcomes the drawback of strong subjectivity and thus enables simultaneous optimization of cost and carbon emissions.

[0171] This application provides a processor for running a program, wherein the program executes the above-described configuration method for a vertical CNC honing machine module for carbon emission reduction.

[0172] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-described method for configuring a vertical CNC honing machine module for carbon emission reduction.

[0173] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0174] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0175] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0176] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0177] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0178] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0179] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0180] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0181] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for configuring a vertical CNC honing machine module for carbon emission reduction, characterized in that, The method includes: Obtain the product configuration information of the vertical CNC honing machine; Determine the concepts, relationships between concepts, and configuration constraints in the product configuration information; Obtain customer needs information; Establish a corresponding optimization configuration model based on the requirement information, the concepts, the relationships between the concepts, and the configuration constraint information. The establishment of the corresponding optimization configuration model based on the requirement information, the concepts, the relationships between the concepts, and the configuration constraint information includes determining optimization variables and determining optimization objectives. The basic facts and rules in the optimized configuration model are determined by a pre-defined rule inference engine. Based on the stated basic facts and the stated rules, a set of candidate instances that meet the stated requirements is selected. In the candidate instance set, instances belonging to different categories that satisfy the constraints contained in the configuration constraint information are selected and combined to obtain the final configuration instance. The determination of the optimization objective includes calculations obtained through formula (3): Official (3); in, This represents the minimum value of carbon emissions and total cost. Indicates the carbon emissions of configuration scheme X; Let X represent the total cost of configuration scheme X; the carbon emissions of configuration scheme X are calculated using formula (4): （4）； in, This indicates the number of modules contained in the vertical CNC honing machine product; This represents the total number of module instances contained in the i-th type of module; Represents a module instance carbon emissions; The total cost of configuration scheme X includes the cost of the constituent modules of configuration scheme X and the assembly cost, and the total cost is calculated by formula (5): （5）； in, This represents the total cost of configuration scheme X for the vertical CNC honing machine. This indicates the total number of modules contained in a vertical CNC honing machine product; This represents the total number of module instances contained in the i-th type of module; Represents a module instance Whether it is selected, 1 indicates selection, 0 indicates otherwise; Represents a module instance The cost; This represents the average assembly cost between modules.

2. The method according to claim 1, characterized in that, The product configuration information includes at least one of the following: configuration scheme, module, interface, attribute, resource, constraint, and customer requirement; the relationships between the concepts include at least one of the following: composition relationship, subclassing relationship, interface relationship, attribute relationship, connection relationship, resource consumption and provision relationship, and constraint relationship.

3. The method according to claim 1, characterized in that, The process of determining the basic facts and rules in the optimized configuration model based on the preset rule inference engine includes: Transform the instance information in the product configuration into basic facts for rule-based reasoning; Transform the ontology information in the ontology into rules in the preset rule base; The ontology information includes at least one of the following: relationships between ontology, axioms, and semantic web rule language constraints.

4. The method according to claim 3, characterized in that, The semantic web rule language constraints include at least one of the following: compatibility constraints, interface constraints, resource constraints, and client requirement constraints.

5. The method according to claim 3, characterized in that, The inference engine includes an ontology inference engine, and the inference methods include: Obtain user requirement information and input the requirement information as a requirement instance into the configuration ontology; The customer's demand information is constrained by the semantic web rule language; The ontology reasoning engine determines a set of candidate modules that satisfy the constraints.

6. The method according to claim 1, characterized in that, The determination of optimization variables includes: Determine Boolean variables To optimize the variables used to represent module instances The selection of the Boolean variable; the Boolean variable is calculated using formula (1): Official (1); in, This represents the number of module categories contained in the product. The total number of module instances representing the i-th type of module; the optimization variable X in vector form for the product configuration problem is calculated using formula (2): Official (2); in, It represents an optimization variable with N module categories and N total module instances for the Nth category.

7. The method according to claim 1, characterized in that, The method includes: The Pareto optimal set is determined by a multi-objective optimization algorithm, and the optimal solution in the Pareto optimal set is determined. A multi-objective configuration optimization scheme is determined using a non-dominated sorting genetic algorithm. The optimal configuration model is determined based on the optimal solution and the optimization scheme.

8. The method according to claim 1, characterized in that, The determination of constraints on the product configuration includes: determining at least one of the following: selection constraints, necessity constraints, incompatibility constraints, price constraints, and delivery date constraints.

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