Multi-target flexible process planning system and method for ball valve assembly

Abstract

The invention belongs to the technical field of ball valve machining process routes, and particularly relates to a multi-target flexible process planning system and method for a ball valve assembly, and the system comprises a data collection module, a constraint condition construction module, a target weight determination module, a target function design module, an optimization solution module, and an optimization result analysis and display module. And setting an optimal processing sequence of each process, distributing processing equipment, comparing and analyzing optimization results of different objective functions and system performance index change conditions before and after process route optimization, and visually displaying the optimization results. The method comprises the steps of S1, performing feature representation of the ball valve assembly, S2, determining constraint conditions in the machining process, S3, determining objective functions, S4, determining weight coefficients of different objective functions based on a fuzzy analytic hierarchy process, and S5, determining a process planning decision of the ball valve assembly under multiple constraints. According to the method, rapid optimization of a complex process route is realized, and the development trend of green manufacturing is met.

Description

Technical Field

[0001] The invention belongs to the technical field of ball valve processing route, and in particular relates to a multi-objective flexible process planning system and method for a ball valve assembly. Background Art

[0002] With the acceleration of globalization and the deepening of industrialization, the contradiction between energy consumption and environmental issues is becoming increasingly acute. Process planning describes all the steps in manufacturing a product from blank to parts, including the specific operations, sequence, tools, equipment and personnel participation of each step, which greatly affects the business objectives of the product parts, such as processing efficiency, processing quality, processing cost, environmental impact and resource waste. However, the production process of ball valve components involves multiple processes and a variety of equipment combinations, and the machining process route is relatively complicated. How to optimize the process route to reduce carbon emissions, improve processing efficiency and reduce processing costs has become an important issue facing ball valve component manufacturers.

[0003] In the multi-objective optimization process, the evaluation of machining process routes is uncertain, and it is difficult for people to evaluate the process planning model. Fuzzy analytic hierarchy process is developed based on analytic hierarchy process and is used to deal with ambiguity and uncertainty in the decision-making process. It introduces the idea of ​​fuzzy sets to deal with problems that are difficult to accurately quantify in people's judgments. Fuzzy analytic hierarchy process is particularly suitable for multi-criteria decision-making problems, especially those involving complex subjective judgments. How to combine existing analysis methods to realize the evaluation of process planning models is a technical difficulty that needs to be solved urgently.

[0004] In traditional ball valve assembly process planning, decision-making generally relies on manual experience and simple heuristic rules. This method often lacks systematic and scientific optimization methods, resulting in limited process planning effects in complex and efficient production environments. With the continuous expansion of ball valve assembly production scale, the production environment and process requirements are becoming increasingly complex, and traditional optimization methods are gradually becoming incapable of meeting the needs. Especially when faced with diverse production goals, existing methods often cannot take into account all aspects of the needs at the same time, and it is difficult to achieve global optimization. This makes traditional process planning methods face greater challenges in modern manufacturing, and more advanced and intelligent optimization methods are urgently needed to meet more complex manufacturing needs. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that the existing ball valve assembly production scale is constantly expanding, the production environment and process requirements are becoming increasingly complex, and the various goals in the ball valve assembly production process cannot be achieved by relying on traditional decision-making methods. The present invention provides a multi-objective flexible process planning system and method for ball valve assemblies, which mainly uses fuzzy hierarchical analysis method and adaptive improved ant colony algorithm to establish a carbon emission model for low-carbon manufacturing from the perspective of material flow and energy flow, and establishes an optimization model with carbon emissions, completion time and processing cost as the target from the process level, realizing the rapid optimization of complex process routes, which is in line with the development trend of green manufacturing.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a multi-objective flexible process planning system for a ball valve assembly, comprising:

[0007] The data acquisition module includes a production equipment data acquisition unit, a material data acquisition unit, a production energy consumption data acquisition unit and a quality monitoring data acquisition unit.

[0008] The constraint condition construction module includes rationality constraints and optimal constraints. The rationality constraints include production task completion constraints, sequence constraints, and quality constraints. The optimal constraints are used to establish constraint conditions for ball valve assembly process route optimization in combination with actual production constraints.

[0009] The target weight determination module includes a fuzzy judgment matrix unit, a fuzzy consistency check unit, and a weight calculation and normalization unit; it is used to dynamically determine the weight coefficients of the multi-objective function based on the fuzzy hierarchical analysis method.

[0010] The objective function design module, including low-carbon objective function, high-efficiency objective function and low-cost objective function, is used to define the objective function of process route optimization.

[0011] The optimization solution module searches for the optimal solution of the objective function based on an improved multi-objective optimization algorithm. The process planning algorithm determines the optimal process route in the machining process of the ball valve component through an intelligent search mechanism based on the dynamic weight allocation of the fuzzy hierarchical analysis method.

[0012] The optimization result analysis and display module sets the optimal processing sequence for each process, allocates processing equipment, compares and analyzes the optimization results of different objective functions, the changes in system performance indicators before and after process route optimization, and visualizes the optimization results.

[0013] According to another embodiment of the invention or a flexible process planning system of any of the foregoing embodiments, the data acquisition module is used to obtain the following parameters involved in the manufacturing process of the ball valve assembly: the corresponding power of the equipment at different stages, the quality of the production unit product before and after processing, the cutting fluid replacement cycle, density, tool life, quality, the processing time of each process itself, the processing resource replacement time, the production energy consumption of the production unit product, and key quality parameters.

[0014] According to another embodiment of the invention or a flexible process planning system of any of the above embodiments, each constraint target of the constraint condition building module is as follows:

[0015] Production task completion constraints: ensure that all production tasks are completed within the specified time and no task delays are allowed.

[0016] Sequence constraint: ensure that each ball valve assembly process task is executed in the prescribed process sequence, coarse first then fine, primary first then secondary, reference first then others, surface first then hole, inside first then outside, and inside and outside crossed.

[0017] Quality constraints: Ensure that after the process route is optimized, the produced ball valve components meet the predetermined quality standards.

[0018] The optimal constraints include the minimum number of machine tool, tool and fixture replacements.

[0019] According to another embodiment of the invention or the flexible process planning system of any of the above embodiments, the units of the target weight determination module are specifically as follows:

[0020] Fuzzy judgment matrix unit: It is constructed based on the importance preference of the mechanical engineers of the enterprise for the optimization goals of minimizing total carbon emissions, minimizing total processing time, and minimizing total processing costs.

[0021] Fuzzy consistency check unit: performs consistency check on the fuzzy judgment matrix to ensure the rationality and credibility of the weights.

[0022] Weight calculation and normalization unit: The weight value of each objective function is calculated through the fuzzy hierarchical analysis method, and the indicators are normalized for weight allocation in multi-objective optimization.

[0023] According to another embodiment of the invention or the flexible process planning system of any of the above embodiments, each objective function of the objective function design module is specifically as follows:

[0024] Low-carbon objective function: The optimization goal is to minimize the total carbon emissions of the material flow carbon emissions and energy flow carbon emissions in the production process of ball valve components.

[0025] Efficient objective function: The optimization goal is to minimize the total processing time that is the sum of the processing time of the process itself and the processing resource replacement time in the production process of the ball valve assembly.

[0026] Low-cost objective function: The optimization goal is to minimize the total processing cost of machine tool processing cost and tool wear cost in the production process of ball valve components.

[0027] Accordingly, a multi-objective flexible process planning method for a ball valve assembly is disclosed, comprising the following steps:

[0028] The first step is to characterize the ball valve assembly.

[0029] According to the manufacturing features in the machining process route of the ball valve assembly, the manufacturing feature set of the ball valve assembly is obtained by stipulating and collectively defining;

[0030] According to the process step composition of each manufacturing feature of the ball valve assembly, a feature processing chain is constructed. The corresponding processing method of each manufacturing feature is determined through the feature processing chain to obtain the processing step set of the ball valve assembly. The processing resource combination method of each processing step in the processing step set is determined, and then the processing information contained in each processing step is obtained, which is defined as a process step element.

[0031] The second step is to determine the constraints in the machining process.

[0032] Including rationality constraints and optimality constraints,

[0033] The rationality constraints include: rough first, fine second, primary first, secondary second, datum first, others second, surface first, hole second, inside first, outside second, internal and external intersection, and the requirements of the process characteristics themselves.

[0034] The optimal constraints include the minimum number of machine tool, tool, and fixture changes.

[0035] The optimal solution obtained must satisfy the rationality constraint while also satisfying the optimality constraint as much as possible.

[0036] The third step is to determine the objective function.

[0037] The objective functions include low-carbon objective function, high-efficiency objective function, and low-cost objective function.

[0038] The fourth step is to determine the weight coefficients of different objective functions based on the fuzzy analytic hierarchy process.

[0039] Based on the fuzzy hierarchical analysis method, carbon emissions, completion time and processing costs are selected as the first-level indicators, and carbon emissions of material flow, carbon emissions of energy flow, processing time of the process itself, processing resource replacement time, machine tool processing cost and tool wear cost are selected as the second-level indicators; according to the first-level indicators and second-level indicators, a comprehensive evaluation system for machining process routes is constructed based on fuzzy mathematics principles.

[0040] Select the factor judgment set V = (C T ,T total ,P T ), where V is the comprehensive evaluation;

[0041] According to the three primary indicators, the final evaluation results of the process route are divided into three levels, and the corresponding evaluation set is constructed, E = (E1, E2, E3), where E1, E2, and E3 represent excellent, good, and fair, respectively;

[0042] The weights are calculated based on the analytic hierarchy process, and the primary and secondary indicators are calculated in sequence, while the affiliation between the primary and secondary indicators is considered.

[0043] The specific steps are as follows:

[0044] 4.1 Establish an evaluation matrix. The establishment process mainly involves the evaluation of two types of indicators: one is the national standard, and the other is the enterprise management indicator. For indicators with clear standards, they will be compared and evaluated with relevant standards. The enterprise management indicators are evaluated by mechanical engineers.

[0045] 4.2 Establish the judgment matrix A = (a ij ) n×n , where the element a in the matrix A is ij It is the standard for comparing the relative importance of factors i and j. The relative importance of the two is represented by integers between 1 and 9, with 1 representing equal importance and 9 representing extreme importance. i with a j The difference is large, indicating that a i Relative to a j The importance of is higher, and the fuzzy consistent judgment matrix is ​​shown in Table 3.

[0046] 4.3 Normalization calculation based on the standard column average method:

[0047]

[0048] in:

[0049] n is the number of factors;

[0050] 4.4 Ensure the rationality of weight distribution through consistency check.

[0051] 4.5 The low-carbon objective function, high-efficiency objective function and low-cost objective function are normalized to obtain a dimensionless low-carbon function, a dimensionless high-efficiency objective function and a dimensionless low-cost objective function.

[0052] 4.6 The three dimensionless objective functions are weighted and summed according to the weight coefficients obtained by the fuzzy hierarchical analysis method to obtain the optimized objective function.

[0053] The fifth step is to optimize and solve the low-carbon objective function, the high-efficiency objective function and the low-cost objective function through a multi-objective optimization algorithm to determine the process planning decision of the ball valve assembly under multiple constraints, including the following steps:

[0054] 5.1 Initialize the main parameters,

[0055] The main parameters include population size, maximum number of iterations, pheromone volatilization factor, and expected heuristic factor. Each individual represents a feasible process planning decision;

[0056] 5.2 Path selection through pseudo-random state transition rules,

[0057] The population path search is mainly affected by two factors: pheromone concentration and heuristic function;

[0058] In order to solve the problem that the pheromone concentration difference between paths is small in the early stage of algorithm iteration, which reduces the algorithm search efficiency, the pseudo-random state transfer rule is used to improve the algorithm's early search efficiency;

[0059] Individual path selection: arrange M individuals to the initial node, initialize the taboo table, update the path according to the pseudo-random state transition rule, and repeat this cycle until the individuals in the population complete the route;

[0060] The low-carbon fitness value of each path in the low-carbon objective function, the high-efficiency fitness value of the high-efficiency objective function and the low-cost fitness value of the low-cost objective function are calculated as a basis for evaluating the quality of the solution and selecting the next generation population.

[0061] 5.3 Update pheromone increment,

[0062] A reward and punishment mechanism is introduced to update the pheromone increment and improve the convergence speed of the algorithm. After each iteration, the average value of the individual path length is calculated, and the optimal individual path and the path with a length less than the average value of the path length are rewarded with pheromone concentration to enhance the guiding effect of the optimal solution on subsequent iterations; for the path with a length greater than the average value of the path length, a penalty is given to reduce the misleading effect of inferior paths on subsequent iterations.

[0063] 5.4 Update pheromone based on Levi flight,

[0064] The Levy flight operator is introduced to update the pheromone volatility factor and improve the algorithm's optimization ability. In the early stage of the algorithm iteration, the pheromone volatility factor is larger, more pheromones are volatilized, and the pheromones left on the paths that individuals have passed and have not passed are similar. The path that has not been passed may also be selected as the next path, and the search range is expanded. In the later stage of the algorithm, the value of the pheromone volatility factor becomes smaller and smaller, which improves the convergence speed of the algorithm.

[0065] The improved pheromone volatilization factor is shown in the following formula:

[0066] ρ(t+1)=ρ(t)-χ⊕L(λ)

[0067] Among them, χ is the step length control value;

[0068] L(λ) is the Levy flight model

[0069] λ is a constant.

[0070] Update the pheromone increment. After a generation of individuals completes the search, abandon the population individuals that have not reached the target point. Update the pheromone increment on the path passed according to the reward and punishment mechanism, and update the pheromone volatilization factor based on the Levy flight strategy.

[0071] 5.5 Determine whether the maximum number of iterations has been reached.

[0072] If the maximum number of iterations is reached, the optimal process route in the machining process of the ball valve assembly is output; if the maximum number of iterations is not reached, all nodes in the taboo table are cleared, and the path is updated according to the pseudo-random state transfer rule to continue iteration.

[0073] 5.6 Output the optimal process route.

[0074] According to another embodiment of the invention or the flexible process planning method of any of the above embodiments, the step 4.4 specifically includes the following steps:

[0075] ① Calculate the consistency index CI, which is expressed as follows:

[0076]

[0077] ② Select the average random consistency index RI. The RI values ​​of the first-order judgment matrix and the second-order judgment matrix are both 0, and the RI value of the third-order judgment matrix is ​​0.52.

[0078] ③ Calculate the consistency ratio CR. If CR < 1, the consistency requirement is met, otherwise it is necessary to re-establish the judgment matrix and repeat the above steps. CR is expressed as follows:

[0079]

[0080] ④ Comprehensive evaluation results:

[0081] Normalize the judgment matrix of each secondary indicator to obtain matrix A ij ;

[0082] Compare it with the evaluation matrix R established based on the opinions of mechanical engineers ijMultiply them together to get the fuzzy evaluation results of each secondary indicator;

[0083] These secondary evaluation results are combined into a matrix R, which is the primary fuzzy evaluation matrix;

[0084] The first-level fuzzy evaluation matrix is ​​multiplied by the corresponding weight set to obtain the first-level fuzzy evaluation result of the ball valve assembly machining process route.

[0085] According to another embodiment of the invention or a flexible process planning method of any of the above embodiments, in step 5.2, the search efficiency of the algorithm in the early stage is improved by using a pseudo-random state transition rule, as follows:

[0086] The pseudo-random state transfer rule is shown in the following formula:

[0087]

[0088] in:

[0089] q0 is a random number in the interval (0, 1).

[0090] N is the current iteration number,

[0091] τ ij is the pheromone concentration,

[0092] α is the expected heuristic factor,

[0093] η ij It is a pheromone-inducing factor.

[0094] β is the heuristic function,

[0095] allowed k are all the nodes that individual k may pass through when moving,

[0096] d i j is the distance between the current node i and the next node j,

[0097] η I j is the Euclidean distance from the starting node I to the node j,

[0098] is the inverse of the Euclidean distance from node j to target node T,

[0099] γ is the relative importance of the inverse of the Euclidean distance from node j to the target node T;

[0100] After each iteration, the reward and punishment mechanism calculates the average value of the individual path length, and rewards the optimal individual path and the path with a length less than the average value with pheromone concentration, so as to enhance the guiding effect of the optimal solution on subsequent iterations. For the path with a length greater than the average value, a penalty is given to reduce the misleading effect of the inferior path on subsequent iterations. The pheromone increment update includes:

[0101]

[0102] Among them, ρ is the pheromone volatilization system;

[0103] △τ ij (t) is the pheromone left between paths (i, j) in this iteration;

[0104] is the pheromone of the kth individual remaining on the path (i, j) at time t;

[0105] L k is the path length of individual k;

[0106] Q is the pheromone intensity, which is a constant.

[0107] μ1, μ2, and μ3 are constants greater than 0;

[0108] N max is the total number of iterations;

[0109] L min , L mean are respectively the optimal individual path length and the average path length taken by the individuals in this generation.

[0110] Compared with the prior art, the present invention has the following beneficial effects:

[0111] 1. The present invention provides a multi-objective flexible process planning system and method for ball valve components, which improves the efficiency of ball valve component machining process planning. By introducing the fuzzy analytic hierarchy process, the system can dynamically allocate the weights of optimization objectives, effectively avoiding subjective human intervention in complex trade-offs. Combined with advanced process planning algorithms, fast and accurate optimization is achieved. In practical applications, this means that the cycle from the initial plan to the final optimization result is significantly shortened, greatly improving planning efficiency and meeting the high-efficiency requirements of complex manufacturing scenarios.

[0112] 2. The present invention provides a multi-objective flexible process planning system and method for ball valve assemblies, which reduces the machining and manufacturing costs of ball valve assemblies. The optimization method focuses on improving the utilization of resources such as machine tools, cutting tools, and fixtures. By reducing the frequency of resource replacement, not only the direct cost in production is reduced, but also the indirect losses caused by idle resources are reduced. This optimization scheme is particularly critical for large-scale production and helps to achieve higher economy and sustainable development.

[0113] 3. The present invention provides a multi-objective flexible process planning system and method for ball valve assemblies to reduce carbon emissions during the machining process of ball valve assemblies. A refined carbon emission calculation model is used to cover carbon emissions from material flows (such as blank processing and cutting fluid consumption) and carbon emissions from energy flows (such as equipment energy consumption). By optimizing the process route, carbon emissions from each link can be minimized to achieve low-carbon goals. This not only responds to the development trend of green manufacturing, but also enhances the competitiveness of enterprises in environmental protection.

[0114] 4. The present invention provides a multi-objective flexible process planning system and method for ball valve components, which takes into account multi-objective requirements. The system can comprehensively consider the three core objectives of low carbon, high efficiency and low cost, and achieve a balance between multiple objectives through dynamic adjustment of the fuzzy hierarchical analysis method. This method avoids the limitation of optimizing one objective at the expense of another, and is particularly suitable for complex manufacturing environments with diverse requirements.

[0115] 5. The present invention provides a multi-objective flexible process planning system and method for ball valve components, which enhances the system usability. The modular design of the system separates functions such as data collection, constraint construction, target weight determination, optimization solution and result display, and users can flexibly call module functions according to their needs. At the same time, the optimization results are displayed through a visual interface, so that users can intuitively understand the improvement effects before and after optimization, which improves the usability of the system.

[0116] 6. The multi-objective flexible process planning system and method for ball valve assemblies provided by the present invention have wide applicability. The present invention is not only applicable to the process optimization of ball valve assemblies, but can also be extended to other multi-objective, multi-constrained complex processing process planning scenarios. Its optimization framework and algorithm are universal for various industrial manufacturing processes, can play a significant role in different application fields, and provide strong technical support for enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 It is a structural schematic diagram of the multi-objective flexible process planning system for ball valve components according to the present invention;

[0118] Figure 2 It is a schematic diagram of the principle of the multi-objective flexible process planning system and method for a ball valve assembly according to the present invention;

[0119] Figure 3 It is a schematic diagram of the parts processing level of the ball valve assembly of the present invention;

[0120] Figure 4 A schematic diagram of the carbon emission boundary and carbon emission source during the processing of the ball valve assembly of the present invention;

[0121] Figure 5 It is a schematic diagram of the process flow of the multi-objective flexible process planning method for a ball valve assembly according to the present invention;

[0122] Figure 6 It is a schematic diagram of the process of finding the optimal solution through a multi-objective optimization algorithm;

[0123] Figure 7 Schematic diagram of pheromone incremental update based on reward and punishment mechanism. DETAILED DESCRIPTION

[0124] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0125] like Figure 2 , 5 As shown, in the first aspect of the embodiment of the present invention,

[0126] Provide a multi-objective flexible process planning method for ball valve components based on multi-objective optimization, including:

[0127] (1) Feature Representation

[0128] According to the manufacturing features (such as holes, grooves, bosses, chamfers, threads, profiles, curved surfaces, etc.) in the machining process route of the ball valve assembly, the manufacturing feature set F of the ball valve assembly is obtained;

[0129] Manufacturing feature set F = {f1,f2,…,f i ,…,f N}, where f i (i=1, 2, ..., N) represents the i-th manufacturing feature of the ball valve assembly.

[0130] According to the process step composition of each manufacturing feature of the ball valve assembly, a feature processing chain is constructed, and the corresponding processing method of each manufacturing feature is determined through the feature processing chain to obtain the processing step set fms of the ball valve assembly;

[0131] Processing step set fms = {fms 11 ,fms 12 ,…,fm sij , …, fm s nm}, where fms ij Fabrication feature f for ball valve assembly i The jth process step in the corresponding chain; m is the manufacturing feature f n The total number of steps included in the corresponding chain.

[0132] Determine the combination of processing resources of each processing step in the processing step set during the optimization of the ball valve assembly machining process route, and then obtain the processing information contained in each step, which is defined as the step element. The part processing level is shown in Figure 3 ;

[0133] (2) Constraints

[0134] Determine the rationality constraints and optimal constraints in the machining process. The optimal solution must satisfy the rationality constraints while satisfying the optimal constraints as much as possible. The rationality constraints include: roughing first, then fine, primary first, then secondary, datum first, then others, surface first, then hole, inside first, then outside, inside and outside intersection, etc. In addition, the requirements of the process characteristics themselves need to be met. For example, some components need to be heat treated after rough grinding. The optimal constraints include the minimum number of machine tool, tool, and fixture replacements.

[0135] (3) Objective function

[0136] Determine the carbon emissions of material flow and energy flow generated by each machining process during the machining process. The carbon emissions of material flow are equal to the sum of carbon emissions caused by blank processing, carbon emissions caused by cutting fluid consumption, and carbon emissions caused by tool wear during the machining process. The carbon emissions of energy flow are equal to the product of the carbon emission factor of electric energy and the total energy consumption. The carbon emission boundaries and carbon emission sources are shown in Figure 4 ;

[0137] Determine the total carbon emissions based on the determined carbon emissions of material flows and energy flows;

[0138] According to the operation status of the tool during the machining process of the machine tool, the total processing time of each processing process is determined, including the processing time of the process itself and the replacement time of the machine tool, tool replacement time, fixture replacement time and other processing resource replacement time;

[0139] Determine the machine tool processing cost and tool wear cost based on the determined total processing time, and then determine the total processing cost;

[0140] (4) Fuzzy analytic hierarchy process

[0141] Based on the fuzzy hierarchical analysis method, carbon emissions, completion time and processing costs are selected as the first-level indicators, and carbon emissions of material flow, carbon emissions of energy flow, processing time of the process itself, processing resource replacement time, machine tool processing cost, and tool wear cost are selected as the second-level indicators;

[0142] According to the primary and secondary indicators, a comprehensive evaluation system of machining process routes is constructed based on fuzzy mathematics principles;

[0143] A single objective function is constructed based on the weight coefficient assigned by the fuzzy analytic hierarchy process, wherein the single objective function is constructed based on the total carbon emissions, the total processing time and the total processing cost shown;

[0144] (5) Optimization algorithm

[0145] Based on the optimization algorithm, the single objective function is optimized and solved to determine the process planning strategy of the ball valve assembly production process. The specific process of optimizing the process route by the fusion method is shown in Figure 5 .

[0146] In an optional embodiment, constructing a low-carbon objective function with the goal of minimizing the total carbon emissions generated during the machining process of the ball valve assembly includes:

[0147] The low-carbon objective function is shown in the following formula:

[0148]

[0149] Among them, C i is the total carbon emissions generated by process i, n is the number of processes, C Mi , C Ei are the carbon emissions of material flow and energy flow generated by the i-th processing technology, M i 、E i are the carbon emission intensity and energy consumption of materials generated by the i-th processing technology, respectively, and f M 、f e are the carbon emission factors corresponding to material flow carbon emission and energy flow carbon emission respectively;

[0150] are the carbon emissions from blank processing, cutting fluid consumption and tool wear in process i; m i1 、m i2 are the quality of the blank before and after processing of process i; t i is the processing time of process i; T i c , T i t are the replacement cycle of the cutting fluid used in process i and the service life of the tool; ρ is the cutting fluid density; V c is the replacement volume of cutting fluid; is the quality of the tool used in process i; f m 、f c 、f cw 、f tThey are the carbon emission factors corresponding to blank processing, cutting fluid consumption, waste fluid treatment and tool wear;

[0151] C E , are the carbon emissions from the energy flow, the carbon emissions from the equipment energy consumption, and the carbon emissions from the heat treatment process, respectively. e 、E ht They are the energy consumption generated by the equipment and the energy consumption generated by the heat treatment process;

[0152] E e 、E start 、E standby 、E no-load 、E load are the total energy consumption generated by the equipment, the energy consumption generated during the equipment startup phase, standby phase, no-load phase, and load phase; P start , P standby , P no-load , P load are the powers of the corresponding stages respectively; t start ,t standby ,t no-load ,t load are the time of the corresponding stages respectively;

[0153] are the CNC system power and hydraulic system power corresponding to process i; v i (k) are the working status, operating power and operating time of the auxiliary system corresponding to process i; l is the number of auxiliary systems; is the minimum no-load power of process i; A1 and A2 are the spindle speed coefficients; n i is the spindle speed of process i; are the processing power and additional load loss power generated by the material removal part corresponding to process i; b m is the additional load loss coefficient, which is generally a constant between 0.15 and 0.25; are the cutting force and cutting speed corresponding to process i respectively;

[0154] is the energy consumption of heat treatment process j, N b It is the standard process power consumption of heat treatment, generally taken as 0.3kwh / kg; and is the correlation coefficient of heat treatment process j, which is process conversion coefficient, heating method coefficient, production method coefficient, workpiece material coefficient and loading coefficient.

[0155] In an optional embodiment, constructing an efficient objective function with the goal of minimizing the total machining time of the ball valve assembly machining process includes:

[0156] The efficient objective function is shown in the following formula:

[0157] T total =T p +T mr +T tr +T fr

[0158]

[0159] Among them, T total , T p , T mr , T tr , T fr They are total processing time, process time, machine tool change time, tool change time, and fixture change time;

[0160] n is the total number of processes, t i Process P i The processing time;

[0161] t mr is the time required for a machine tool replacement, M' i is the machine tool used in process i, M' i+1 is the machine tool used for process i+1. If two adjacent processes i and i+1 are processed on the same machine tool, then ψ(M' i+1 -M' i )=0, otherwise 1;

[0162] t tr The time required for a tool change; T' i is the tool used in process i, T' i+1 is the tool used for process i+1. If the same tool is used for two adjacent processes i and i+1, then ψ(T' i+1 -T' i )=0, otherwise 1;

[0163] t fr The time required for one fixture replacement; F' i is the fixture used in process i, F' i+1 is the fixture used for process i+1. If the same fixture is used for processing two adjacent processes i and i+1, then ψ(F' i+1 -F' i )=0, otherwise it is 1.

[0164] In an optional embodiment, constructing a low-cost objective function with the goal of minimizing the total machining cost of the ball valve assembly machining process includes:

[0165] The low-cost objective function is shown in the following formula:

[0166] P T =P j +P cl

[0167]

[0168] Among them, P T , P j , P cl They are total processing cost, machine tool processing cost and tool wear cost;

[0169] R p is the labor cost per unit time, n is the number of processes, T i total is the total processing time of process i;

[0170] T i are the cost and life of the tool used in process i, T i c is the effective processing time of process i, D is the tool diameter, v c is the cutting speed, f z is the feed per tooth, a e is the processing width, a p is the cutting depth, C v , o, k, q, u, g, and HB are tool life correlation coefficients.

[0171] In an optional implementation, the determining weight coefficients of different objective functions based on the fuzzy analytic hierarchy process includes:

[0172] The low-carbon objective function, high-efficiency objective function and low-cost objective function are used as the fundamental way to evaluate the process route. The low-carbon target is determined by the carbon emissions of material flow and energy flow. Lower carbon emissions are the key to reflecting the low-carbon optimization target.

[0173] After ensuring that the ball valve assembly process route meets the needs of the national development strategy, the high efficiency goal becomes the key to evaluation. In addition to the processing time of the processing process itself, the additional time caused by the replacement of processing resources such as machine tools, tools, and fixtures during the machining process will also affect the final maximum completion time;

[0174] At the same time, the low-cost target also has an important impact on the evaluation of the process route. The difference in the low-cost target of the process route is reflected in two aspects: machine tool processing cost and tool wear cost;

[0175] Low-carbon targets, high-efficiency targets and low-cost targets are selected as primary indicators. Carbon emissions of material flow, carbon emissions of energy flow, processing time of the process itself, replacement time of processing resources, machine tool processing cost and tool wear cost are selected as secondary indicators. A comprehensive evaluation system is constructed based on the principle of fuzzy mathematics, as shown in Table 1;

[0176] Table 1 Comprehensive evaluation index system

[0177]

[0178] Select the factor judgment set V = (C T ,T total ,P T ), where V is the comprehensive evaluation;

[0179] According to the three primary indicators, the final evaluation results of the process route are divided into three levels, and the corresponding evaluation set is constructed, E = (E1, E2, E3), where E1, E2, and E3 represent excellent, good, and fair, respectively;

[0180] Based on the analytic hierarchy process, the weights are calculated, and the first-level indicators and second-level indicators are calculated in sequence, while considering the affiliation between the first-level and second-level indicators;

[0181] The specific steps are as follows:

[0182] The evaluation matrix was established. The evaluation process mainly involved the evaluation of two types of indicators: one was the national standard, and the other was the enterprise management indicator. For indicators with clear standards, they were compared and evaluated with relevant standards. The enterprise management indicators were evaluated by inviting 30 mechanical engineers. The evaluation results of each indicator are shown in Table 2.

[0183] Table 2 Evaluation results of each indicator

[0184]

[0185] Establish a judgment matrix A = (a ij ) n×n , where the element a in the matrix A is ij It is the standard for comparing the relative importance of factors i and j. The relative importance of the two is represented by integers between 1 and 9, with 1 representing equal importance and 9 representing extreme importance. i with a j The difference is large, indicating that a i Relative to a j The importance of is higher, the fuzzy consistent judgment matrix is ​​shown in Table 3, and the comprehensive judgment matrix is ​​shown in Table 4;

[0186] Table 3 Fuzzy consistent judgment matrix

[0187]

[0188]

[0189] Table 4 Comprehensive judgment matrix

[0190]

[0191] Normalization calculation based on the canonical column average method

[0192] Where n is the number of factors;

[0193] The consistency check to ensure the rationality of weight distribution includes:

[0194] Calculate consistency index

[0195] Select the average random consistency index RI, the RI values ​​of the first-order judgment matrix and the second-order judgment matrix are both 0, and the RI value of the third-order judgment matrix is ​​0.52;

[0196] Calculate the consistency ratio If CR<1, the consistency requirement is met, otherwise the judgment matrix needs to be re-established and the above steps repeated;

[0197] The calculation of consistency index and consistency ratio shows that the corresponding consistency ratio CR values ​​of the three optimization goals of low carbon, high efficiency and low cost are all less than 0.1, which meets the consistency requirements.

[0198] The judgment matrix of each secondary index is normalized to obtain the matrix A ij , and compare it with the evaluation matrix R established based on the opinions of mechanical engineers ij Multiply them to obtain the fuzzy evaluation results of the secondary indicators;

[0199] Combining the secondary evaluation results into a matrix R to obtain a primary fuzzy evaluation matrix;

[0200] Multiplying the primary fuzzy evaluation matrix with the corresponding weight set to obtain a primary fuzzy evaluation result of the ball valve assembly machining process route;

[0201] Based on the fuzzy evaluation method B ij =A ij R ij , and obtain the secondary fuzzy evaluation result B of the ball valve assembly machining process route 21 =[0.420.40.18], B22 =[0.5250.2750.2], B 23 =[0.31670.40.2833], get the first-level fuzzy evaluation

[0202] The calculation principle of the weight set corresponding to the primary fuzzy evaluation matrix is ​​the same as the calculation principle of the weight set corresponding to the secondary fuzzy evaluation matrix. Based on the data in the comprehensive judgment matrix, the weight set A corresponding to the primary fuzzy evaluation matrix is ​​obtained as [0.525 0.334 0.141];

[0203] A consistency test is performed on the weight set corresponding to the first-level fuzzy evaluation matrix, and the consistency index CI=0.0235, the consistency ratio CR=0.0452, and the CR value is less than 0.1, which meets the consistency requirements;

[0204] First-level fuzzy evaluation results of ball valve assembly machining process route

[0205] The low-carbon objective function, the high-efficiency objective function and the low-cost objective function are normalized to obtain a dimensionless low-carbon function, a dimensionless high-efficiency objective function and a dimensionless low-cost objective function;

[0206] The three dimensionless objective functions are weighted and summed according to the weight coefficients obtained by the fuzzy hierarchical analysis method to obtain the objective function.

[0207] In an optional embodiment, the low-carbon objective function, the high-efficiency objective function and the low-cost objective function are optimized and solved by a multi-objective optimization algorithm to determine the process planning decision of the ball valve assembly under multiple constraints, including:

[0208] Initialize the main parameters, such as population size, maximum number of iterations, pheromone volatilization factor, expected heuristic factor, etc. Each individual represents a feasible process planning decision;

[0209] The population path search is mainly affected by two factors: pheromone concentration and heuristic function;

[0210] In order to solve the problem that the pheromone concentration difference between paths is small in the early stage of algorithm iteration, which reduces the algorithm search efficiency, this algorithm improves the algorithm's early search efficiency through pseudo-random state transfer rules;

[0211] Individual path selection: arrange M individuals to the initial node, initialize the taboo table, update the path according to the pseudo-random state transition rule, and repeat this cycle until the individuals in the population complete the route;

[0212] Calculate the low-carbon fitness value of each path in the low-carbon objective function, the high-efficiency fitness value of the high-efficiency objective function, and the low-cost fitness value of the low-cost objective function as a basis for evaluating the quality of the solution and selecting the next generation population;

[0213] By introducing a reward and punishment mechanism to update the pheromone increment, the convergence speed of the algorithm is improved. After each iteration, the average value of the individual path length is calculated, and the pheromone concentration reward is given to the optimal individual path and the path with a length less than the average value of the path length to enhance the guiding effect of the optimal solution on subsequent iterations; for the path with a length greater than the average value of the path length, a penalty is given to reduce the misleading effect of the inferior path on subsequent iterations. The pheromone increment update based on the reward and punishment mechanism is shown in Figure 7 ;

[0214] This algorithm introduces the Levy flight operator to update the pheromone volatility factor, improving the algorithm's optimization ability. In the early stage of the algorithm iteration, the pheromone volatility factor is larger, more pheromones are volatilized, and the pheromones left on the paths that individuals have and have not passed are similar. The path that has not been passed may also be selected as the path for the next pass, and the search range is expanded; in the later stage of the algorithm, the pheromone volatility factor value becomes smaller and smaller, which improves the convergence speed of the algorithm;

[0215] Update the pheromone increment. After a generation of individuals completes the search, discard the individuals in the population that have not reached the target point. Update the pheromone increment on the path passed by according to the reward and punishment mechanism, and update the pheromone volatility factor based on the Levy flight strategy.

[0216] Determine whether the maximum number of iterations has been reached. If yes, output the optimal process route in the machining process of the ball valve assembly. Otherwise, clear all nodes in the taboo table, update the path according to the pseudo-random state transfer rule and continue to iterate. See the implementation process of the process planning algorithm for details. Figure 6 .

[0217] In an optional embodiment, the process planning algorithm includes:

[0218] The pseudo-random state transfer rule is shown in the following formula:

[0219]

[0220] Among them, q0 is a random number in the interval (0, 1), N is the current number of iterations, τ ij is the pheromone concentration, α is the expected heuristic factor, η ij is the pheromone heuristic factor, β is the heuristic function, allowed k are all the nodes that individual k may pass through when moving, d ij is the distance between the current node i and the next node j, η Ijis the Euclidean distance from the starting node I to the node j, is the reciprocal of the Euclidean distance from node j to target node T, and γ is the relative importance of the reciprocal of the Euclidean distance from node j to target node T;

[0221] After each iteration, the reward and punishment mechanism calculates the average value of the individual path length, and rewards the optimal individual path and the path with a length less than the average value with pheromone concentration, so as to enhance the guiding effect of the optimal solution on subsequent iterations. For the path with a length greater than the average value, a penalty is given to reduce the misleading effect of the inferior path on subsequent iterations. The pheromone increment update includes:

[0222]

[0223] Among them, ρ is the pheromone volatilization system; △τ ij (t) is the pheromone left between paths (i, j) in this iteration; is the pheromone of the kth individual on the path (i, j) at time t; L k is the path length of individual k; Q is the pheromone intensity, which is generally a constant, and μ1, μ2, and μ3 are constants greater than 0; N max is the total number of iterations; L min , L mean are respectively the optimal individual path length and the average path length taken by individuals in this generation;

[0224] The Levy flight strategy introduces the Levy flight operator to update the pheromone volatility factor. The improved pheromone volatility factor is shown in the following formula:

[0225]

[0226] Where χ is the step length control variable; L(λ) is the Levy flight model

[0227] λ is a constant.

[0228] According to a second aspect of the embodiments of the present invention,

[0229] Provide a multi-objective flexible process planning system for ball valve components, including: data acquisition module, constraint condition construction module, target weight determination module, target function design module, optimization solution module and optimization result analysis and display module, among which:

[0230] The data acquisition module includes a production equipment data acquisition unit, a material data acquisition unit, a production energy consumption data acquisition unit and a quality monitoring data acquisition unit, which are used to obtain the corresponding power of the equipment involved in the ball valve assembly manufacturing process at different stages, the quality of the production unit product before and after processing, the cutting fluid replacement cycle, density and other related parameters, tool life, quality and other related parameters, the processing time of each process, the processing resource replacement time, the production energy consumption of the production unit product and key quality parameters;

[0231] The constraint condition building module is used to establish the constraint conditions for ball valve assembly process route optimization in combination with actual production constraints, including rationality constraints and optimal constraints, wherein the rationality constraints include production task completion constraints: ensuring that all production tasks are completed within the specified time, and no task delays are allowed; sequence constraints: ensuring that each ball valve assembly process task is executed in the specified process sequence, first rough then fine, first main then secondary, first reference then others, first surface then hole, first inside then outside, inside and outside intersection, etc.; quality constraints: ensuring that after the process route is optimized, the produced ball valve assembly meets the predetermined quality standards; the optimal constraints include the minimum number of machine tool, tool, and fixture replacements, etc.;

[0232] The target weight determination module is used to dynamically determine the weight coefficients of the multi-objective function based on the fuzzy hierarchical analysis method, including the following functions: opinion collection: construct a fuzzy judgment matrix based on the importance preference of the mechanical engineers of the enterprise for each optimization target (such as minimizing total carbon emissions, minimizing total processing time, and minimizing total processing costs); fuzzy consistency test: perform consistency test on the fuzzy judgment matrix to ensure the rationality and credibility of the weight; weight calculation and normalization: calculate the weight value of each objective function through the fuzzy hierarchical analysis method, and normalize the indicators for weight allocation of multi-objective optimization;

[0233] The objective function design module is used to define the objective function of process route optimization, including: low-carbon objective function: the optimization goal is to minimize the total carbon emissions of the material flow carbon emissions and energy flow carbon emissions in the production process of ball valve components; high-efficiency objective function: the optimization goal is to minimize the total processing time of the process itself and the processing resource replacement time in the production process of ball valve components; low-cost objective function: the optimization goal is to minimize the total processing cost of the machine tool processing cost and tool wear cost in the production process of ball valve components;

[0234] The optimization solution module searches for the optimal solution of the objective function based on an improved multi-objective optimization algorithm (such as a process planning algorithm). The process planning algorithm determines the optimal process route in the machining process of the ball valve assembly through an intelligent search mechanism based on the dynamic weight allocation of the fuzzy analytic hierarchy process.

[0235] The optimization result analysis and display module is used to visualize the optimization results, including the optimal processing sequence and processing equipment (such as machine tools, cutting tools, and fixtures) allocation of each process, comparative analysis of optimization results of different objective functions (such as carbon emissions, processing time, and processing costs), and changes in system performance indicators before and after process route optimization.

[0236] Working principle of the present invention:

[0237] The present invention constructs a multi-objective optimization model for the process route of ball valve components, and constructs a systematic evaluation and optimization framework by taking low carbon, high efficiency and low cost as optimization goals. In the evaluation stage, fuzzy mathematical methods can quantify the uncertainty of subjective judgment and improve the rationality and flexibility of target weight allocation.

[0238] The ball valve assembly process route optimization system is modularly designed, and the system modules are divided into six parts: data acquisition, constraint condition construction, target weight determination, objective function design, optimization solution, and result display. Data acquisition uses sensors and database technology to monitor process parameters in real time. Constraint conditions and objective functions establish a constraint equation group, and the objective function is optimized in combination with process goals. The modular design improves the ease of use, scalability, and intelligence of the system, ensuring that the optimization process is clear and transparent.

[0239] The implementation path of the ball valve assembly process route optimization includes the definition of the process feature set, and the extraction of key elements related to the process by analyzing the geometric and functional characteristics of the ball valve assembly. The processing steps and resource allocation optimization were carried out: the optimization algorithm was used to globally adjust each step of the process and its resource allocation to ensure the optimal path. The optimization is not limited to a certain link, but covers the entire process from design to manufacturing.

[0240] In addition, the present invention introduces tools such as genetic algorithms or particle swarm optimization algorithms to balance the conflicts between multiple optimization objectives. The environmental friendliness, production efficiency and economy of the manufacturing process are systematically taken into account to achieve comprehensive optimization. The traditional analytic hierarchy process (AHP) decomposes the problem into a hierarchical structure and determines the weight of each factor in a quantitative or qualitative way.

[0241] At the same time, in view of the data uncertainty in process optimization (such as fluctuations in carbon emission data or dynamic changes in production conditions), fuzzy set theory is introduced, and the target weight is set to a dynamic value, which is adjusted according to the specific situation. The adaptability of the model to a changing environment is improved, ensuring the scientificity and flexibility of process route optimization. The randomization mechanism is used to prevent the algorithm from falling into local optimality and improve the global search capability. Feedback optimization is performed on the pros and cons of the solution to guide the search direction and reduce invalid calculations. The introduction of a random search strategy with non-Gaussian distribution enables the algorithm to strike a balance between local search and large-scale exploration. This algorithm combination greatly improves the optimization efficiency of complex process routes, and is particularly suitable for dealing with process route optimization problems of ball valve components with multi-variable and nonlinear constraints.

[0242] The present invention establishes a carbon emission calculation model in the machining process of ball valve components, and refines the factors that may affect carbon emissions in the entire manufacturing process into specific links, including blank preparation, cutting fluid consumption, tool life management and equipment energy consumption. The carbon emissions of material flow and energy flow are subdivided, and the carbon emission contribution of each link is calculated based on the life cycle analysis method.

[0243] This invention is the first to incorporate implicit emission factors such as cutting fluid and tool wear into the assessment, supporting the optimization of low-carbon manufacturing decisions in a data-driven manner.

[0244] In summary, the present invention provides a full life cycle optimization implementation scheme for the manufacturing process of complex ball valve components.

[0245] The above are only preferred embodiments of the present invention. Any person skilled in the art may modify the present invention by using the above technical solutions or modify it into an equivalent technical solution. Therefore, any simple modification or equivalent replacement made according to the technical solution of the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A multi-objective flexible process planning system for ball valve components, characterized by: include: Data collection module, including production equipment data collection unit, material data collection unit, production energy consumption data collection unit and quality monitoring data collection unit; The constraint condition building module includes rationality constraints and optimal constraints. The rationality constraints include production task completion constraints, sequence constraints, and quality constraints. The optimal constraints are used to establish constraint conditions for ball valve assembly process route optimization in combination with actual production constraints. A target weight determination module, including a fuzzy judgment matrix unit, a fuzzy consistency verification unit, and a weight calculation and normalization unit; Used to dynamically determine the weight coefficients of multi-objective functions based on fuzzy analytic hierarchy process, Objective function design module, including low-carbon objective function, high-efficiency objective function, and low-cost objective function, which is used to define the objective function for process route optimization; The optimization solution module searches for the optimal solution of the objective function based on the improved multi-objective optimization algorithm. The process planning algorithm determines the optimal process route in the machining process of the ball valve assembly through an intelligent search mechanism based on the dynamic weight allocation of the fuzzy analytic hierarchy process; The optimization result analysis and display module sets the optimal processing sequence for each process, allocates processing equipment, compares and analyzes the optimization results of different objective functions, the changes in system performance indicators before and after process route optimization, and visualizes the optimization results.

2. A multi-objective flexible process planning system for ball valve assembly according to claim 1, characterized in that: The data acquisition module is used to obtain the following parameters involved in the ball valve assembly manufacturing process: the corresponding power of the equipment at different stages, the quality of the production unit product before and after processing, the cutting fluid replacement cycle, density, tool life, quality, the processing time of each process, the processing resource replacement time, the production energy consumption of the production unit product, and key quality parameters.

3. A multi-objective flexible process planning system for ball valve assembly according to claim 1, characterized in that: The constraint targets of the constraint condition building module are as follows: Production task completion constraints: ensure that all production tasks are completed within the specified time, and do not allow task delays; Sequential constraints: ensure that each ball valve assembly process task is executed in the specified process sequence, first rough then fine, first primary then secondary, first reference then others, first surface then hole, first inside then outside, inside and outside crossed; Quality constraints: ensure that after the process route is optimized, the ball valve components produced meet the predetermined quality standards; The optimal constraints include the minimum number of machine tool, tool, and fixture changes; According to another embodiment of the invention or the flexible process planning system of any of the above embodiments, the units of the target weight determination module are specifically as follows: Fuzzy judgment matrix unit: constructed according to the importance preference of the mechanical engineers of the enterprise for the optimization goals of minimizing total carbon emissions, minimizing total processing time, and minimizing total processing costs; Fuzzy consistency check unit: performs consistency check on the fuzzy judgment matrix to ensure the rationality and credibility of the weights; Weight calculation and normalization unit: The weight value of each objective function is calculated through the fuzzy hierarchical analysis method, and the indicators are normalized for weight allocation in multi-objective optimization.

4. A multi-objective flexible process planning system for ball valve assembly according to claim 1, characterized in that: The specific objective functions of the objective function design module are as follows: Low-carbon objective function: The optimization goal is to minimize the total carbon emissions of the material flow carbon emissions and energy flow carbon emissions in the production process of ball valve components; Efficient objective function: The optimization goal is to minimize the total processing time of the process itself and the processing resource replacement time in the production process of the ball valve assembly; Low-cost objective function: The optimization goal is to minimize the total processing cost of machine tool processing cost and tool wear cost in the production process of ball valve components.

5. A multi-objective flexible process planning method for a ball valve assembly, characterized in that: The following steps are involved: S1. Characterize the ball valve assembly. According to the manufacturing features in the machining process route of the ball valve assembly, the manufacturing feature set of the ball valve assembly is obtained by stipulating and collectively defining; According to the process step composition of each manufacturing feature of the ball valve assembly, a feature processing chain is constructed, and the corresponding processing method of each manufacturing feature is determined through the feature processing chain to obtain the processing step set of the ball valve assembly, and the processing resource combination method of each processing step in the processing step set is determined, and then the processing information contained in each processing step is obtained, which is defined as a process step element; S2. Determine the constraints in the machining process, Including rationality constraints and optimality constraints, The rationality constraints include: rough first, then fine, primary first, then secondary, datum first, then others, surface first, then hole, inside first, then outside, inside-outside intersection, and the requirements of the process characteristics themselves. The optimal constraints include the minimum number of machine tool, tool, and fixture changes; The optimal solution obtained must satisfy the rationality constraint while also satisfying the optimal constraint as much as possible. S3. Determine the objective function, The objective functions include low-carbon objective function, high-efficiency objective function, and low-cost objective function. S3.1 takes the minimum total carbon emissions generated by the machining process of the ball valve assembly as the goal, and constructs a low-carbon objective function. The total carbon emissions generated by each processing technology is the sum of the carbon emissions of material flow and the carbon emissions of energy flow; the carbon emissions of material flow are the sum of the following carbon emissions: carbon emissions caused by blank processing, carbon emissions caused by cutting fluid consumption, and carbon emissions caused by tool wear; the carbon emissions of energy flow are equal to the product of the carbon emission factor of electric energy and the total energy consumption; The low-carbon objective function is shown in the following formula: in: C i is the total carbon emissions generated by process i, n is the number of processes, C Mi , C Ei are the carbon emissions of material flow and energy flow generated by the i-th processing technology, M i 、E i are the carbon emission intensity and energy consumption of materials generated by the i-th processing technology, f M 、f e are the carbon emission factors corresponding to material flow carbon emission and energy flow carbon emission respectively; They are the carbon emissions generated by blank processing, cutting fluid consumption and tool wear in process i respectively; m i1 、m i2 They are the quality of blank before and after processing of process i respectively; t i is the processing time of process i; are the replacement cycle of the cutting fluid and the service life of the tool used in process i respectively; ρ is the cutting fluid density; V c is the replacement volume of cutting fluid; The quality of the tool used for process i; f m 、f c 、f cw 、f t They are the carbon emission factors corresponding to blank processing, cutting fluid consumption, waste fluid treatment and tool wear; C E , are the carbon emissions from the energy flow, the carbon emissions from the equipment energy consumption, and the carbon emissions from the heat treatment process, respectively. e 、E ht They are the energy consumption generated by the equipment and the energy consumption generated by the heat treatment process; E e 、E start 、E standby 、E no-load 、E load They are the total energy consumption generated by the equipment, the energy consumption generated during the equipment startup phase, standby phase, no-load phase, and load phase; P start , P standby , P no-load , P load are the powers of the corresponding stages respectively; t start ,t standby ,t no-load ,t load are the time of the corresponding stages respectively; are the CNC system power and hydraulic system power corresponding to process i respectively; v i (k) are the working status, operating power and operating time of the auxiliary system corresponding to process i respectively; l is the number of auxiliary systems; is the minimum no-load power of process i; A1, A2 are the spindle speed coefficients; n i is the spindle speed of process i; are the machining power and additional load loss power generated by the material removal part corresponding to process i respectively; b m is the additional load loss coefficient, which is a constant between 0.15 and 0.25; are the cutting force and cutting speed corresponding to process i respectively; is the energy consumption generated by the heat treatment process j, N b The standard power consumption for heat treatment is 0.3kwh / kg; and is the correlation coefficient of heat treatment process j, which is process conversion coefficient, heating method coefficient, production method coefficient, workpiece material coefficient and loading coefficient in sequence; S3.2 takes the shortest total machining time of the ball valve assembly machining process as the goal, constructs an efficient objective function, According to the operation status of the tool during the machining process of the machine tool, the total processing time of each processing process is determined, including the processing resource replacement time: the processing time of the process itself and the machine tool replacement time, tool replacement time, and fixture replacement time; The efficient objective function is shown in the following formula: T total =T p +T mr +T tr +T fr in: T total , T p , T mr , T tr , T fr They are total processing time, process time, machine tool change time, tool change time, and fixture change time; n is the total number of processes, t i is the processing time of process Pi; t mr The time required for a machine tool replacement. M' i is the machine tool used in process i, M' i+1 is the machine tool used for process i+1. If two adjacent processes i and i+1 are processed on the same machine tool, then ψ(M' i+1 -M' i )=0, otherwise 1; t tr The time required for one tool change; T′ i is the tool used in process i, T' i+1 is the tool used for process i+1. If the same tool is used for two adjacent processes i and i+1, then ψ(T' i+1 -T' i )=0, otherwise 1; t fr The time required for one fixture replacement; F′ i The fixture used in process i, F' i+1 is the fixture used for process i+1. If the same fixture is used for processing two adjacent processes i and i+1, then ψ(F' i+1 -F' i )=0, otherwise 1; S3.3 takes the lowest total processing cost of the ball valve assembly machining process as the goal, constructs a low-cost objective function, Based on the determined total processing time, the machine tool processing cost and tool wear cost are determined, and then the total processing cost is determined. The low-cost objective function is shown in the following formula: P T =P j +P cl in: P T is the total processing cost, P j is the machine tool processing cost, P cl is the tool wear cost, R p is the labor cost per unit time, n is the number of processes, T i total is the total processing time of process i, T i is the cost of the tool used in process i, T i is the life of the tool used in process i, T i c is the effective processing time of process i, D is the tool diameter, v c is the cutting speed, f z is the feed per tooth, a e is the processing width, a p is the cutting depth, C v , o, k, q, u, g, HB are tool life correlation coefficients; S4. Based on the fuzzy hierarchical analysis method, the weight coefficients of different objective functions are determined. Based on the fuzzy hierarchical analysis method, carbon emissions, completion time and processing costs are selected as the first-level indicators, and carbon emissions of material flow, carbon emissions of energy flow, processing time of the process itself, processing resource replacement time, machine tool processing cost, and tool wear cost are selected as the second-level indicators; based on the first-level indicators and second-level indicators, a comprehensive evaluation system for machining process routes is constructed based on fuzzy mathematical principles; Select the factor judgment set V = (C T ,T total ,P T ), where V is the comprehensive evaluation; According to the three primary indicators, the final evaluation results of the process route are divided into three levels, and the corresponding evaluation set is constructed, E = (E1, E2, E3), where E1, E2, and E3 represent excellent, good, and fair, respectively; Based on the analytic hierarchy process, the weights are calculated, and the first-level indicators and second-level indicators are calculated in sequence, while considering the affiliation between the first-level and second-level indicators; The specific steps are as follows: S4.1 Establish an evaluation matrix. The establishment process mainly involves the evaluation of two types of indicators: one is the national standard, and the other is the enterprise operation indicator. For indicators with clear standards, they are compared and evaluated with relevant standards. The enterprise operation indicators are evaluated by mechanical engineers; S4.2 Establish a judgment matrix A = (a ij ) n×n , where the element a in the matrix A is ij It is the standard for comparing the relative importance of factors i and j. The relative importance of the two is represented by integers between 1 and 9, with 1 representing equal importance and 9 representing extreme importance. If a i with a j The difference is large, indicating that a i Relative to a j The importance of is higher, and the fuzzy consistent judgment matrix is ​​shown in Table 3; S4.3 Normalization calculation based on the canonical column average method in: n is the number of factors; S4.4 Ensure the rationality of weight distribution through consistency check; S4.5 normalizes the low-carbon objective function, the high-efficiency objective function and the low-cost objective function to obtain a dimensionless low-carbon function, a dimensionless high-efficiency objective function and a dimensionless low-cost objective function; S4.6 performing weighted summation on the three dimensionless objective functions according to the weight coefficients obtained by the fuzzy analytic hierarchy process to obtain an optimized objective function; S5. The low-carbon objective function, the high-efficiency objective function and the low-cost objective function are optimized and solved by a multi-objective optimization algorithm to determine the process planning decision of the ball valve assembly under multiple constraints, including the following steps: S5.1 Initialize the main parameters, The main parameters include population size, maximum number of iterations, pheromone volatilization factor, and expected heuristic factor. Each individual represents a feasible process planning decision; S5.2 selects the path through pseudo-random state transition rules. The population path search is mainly affected by two factors: pheromone concentration and heuristic function; In order to solve the problem that the pheromone concentration difference between paths is small in the early stage of algorithm iteration, which reduces the algorithm search efficiency, the pseudo-random state transfer rule is used to improve the algorithm's early search efficiency; Individual path selection: arrange M individuals to the initial node, initialize the taboo table, update the path according to the pseudo-random state transition rule, and repeat this cycle until the individuals in the population complete the route; Calculate the low-carbon fitness value of each path in the low-carbon objective function, the high-efficiency fitness value of the high-efficiency objective function, and the low-cost fitness value of the low-cost objective function as a basis for evaluating the quality of the solution and selecting the next generation population; S5.3 Update pheromone increment, A reward-penalty mechanism is introduced to update the pheromone increment and improve the convergence speed of the algorithm. After each iteration, the average value of the individual path length is calculated, and the pheromone concentration reward is given to the optimal individual path and the path with a length less than the average value, so as to enhance the guiding effect of the optimal solution on subsequent iterations; for the path with a length greater than the average value, a penalty is given to reduce the misleading effect of the inferior path on the subsequent iterations; S5.4 Pheromone update based on Levy flight, The Levy flight operator is introduced to update the pheromone volatility factor and improve the algorithm's optimization ability. In the early stage of the algorithm iteration, the pheromone volatility factor is larger, and more pheromones are volatilized. The pheromones left on the paths that individuals have passed and have not passed are similar. The paths that have not been passed may also be selected as the next path, and the search range is expanded. In the later stage of the algorithm, the pheromone volatility factor takes smaller and smaller values, which improves the convergence speed of the algorithm. The improved pheromone volatilization factor is shown in the following formula: ρ(t+1)=ρ(t)-χ⊕L(λ) Among them, χ is the step length control value; L(λ) is the Levy flight model λ is a constant; Update the pheromone increment. After a generation of individuals completes the search, discard the individuals in the population that have not reached the target point. Update the pheromone increment on the path passed by according to the reward and punishment mechanism, and update the pheromone volatility factor based on the Levy flight strategy. S5.5 determines whether the maximum number of iterations has been reached. If the maximum number of iterations is reached, the optimal process route in the machining process of the ball valve assembly is output; if the maximum number of iterations is not reached, all nodes in the taboo table are cleared, and the path is updated according to the pseudo-random state transfer rule to continue the iteration. S5.6 outputs the optimal process route.

6. A multi-objective flexible process planning method for a ball valve assembly according to claim 5, characterized in that: The step S4.4 specifically includes the following steps: T1. Calculate the consistency index CI, which is expressed as follows: T2. Select the average random consistency index RI, The RI values ​​of the first-order judgment matrix and the second-order judgment matrix are both 0, and the RI value of the third-order judgment matrix is ​​0.52; T3. Calculate the consistency ratio CR, If CR<1, the consistency requirement is met, otherwise it is necessary to re-establish the judgment matrix and repeat the above steps; CR is expressed as follows: T4. Comprehensive evaluation results: Normalize the judgment matrix of each secondary indicator to obtain matrix A ij ; Compare it with the evaluation matrix R established based on the opinions of mechanical engineers ij Multiply them together to get the fuzzy evaluation results of each secondary indicator; These secondary evaluation results are combined into a matrix R, which is the primary fuzzy evaluation matrix; The first-level fuzzy evaluation matrix is ​​multiplied by the corresponding weight set to obtain the first-level fuzzy evaluation result of the ball valve assembly machining process route.

7. A multi-objective flexible process planning method for a ball valve assembly according to claim 5, characterized in that: In step S5.2, the search efficiency of the algorithm in the early stage is improved by using pseudo-random state transition rules, as follows: The pseudo-random state transfer rule is shown in the following formula: in: q0 is a random number in the interval (0, 1). N is the current iteration number, τ ij is the pheromone concentration, α is the expected heuristic factor, η ij It is a pheromone-inducing factor. β is the heuristic function, allowed k are all the nodes that individual k may pass through when moving, d ij is the distance between the current node i and the next node j, η I j is the Euclidean distance from the starting node I to the node j, is the inverse of the Euclidean distance from node j to target node T, γ is the relative importance of the inverse of the Euclidean distance from node j to the target node T; After each iteration, the reward and punishment mechanism calculates the average value of the individual path length, and rewards the optimal individual path and the path with a length less than the average value with pheromone concentration, so as to enhance the guiding effect of the optimal solution on subsequent iterations. For the path with a length greater than the average value, a penalty is given to reduce the misleading effect of the inferior path on subsequent iterations. The pheromone increment update includes: Among them, ρ is the pheromone volatilization system; △τ ij (t) is the pheromone left between paths (i, j) in this iteration; is the pheromone of the kth individual remaining on the path (i, j) at time t; L k is the path length of individual k; Q is the pheromone intensity, which is a constant. μ1, μ2, and μ3 are constants greater than 0; N max is the total number of iterations; L min , L mean are respectively the optimal individual path length and the average path length taken by the individuals in this generation.

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