Firearm assemblability evaluation method based on qualitative and quantitative analysis self-consistent fusion

Abstract

The invention discloses a firearm assemblability evaluation method based on qualitative and quantitative analysis and self-consistent fusion, and belongs to the technical field of intelligent manufacturing. According to a product design scheme, objective quantitative calculation and subjective evaluation are firstly carried out on possible assembly process schemes through the method, then the assembly process schemes are sorted, and the closeness degree of the evaluated scheme and the optimal scheme is solved. According to the method, assemblability evaluation of objects such as a design scheme, product performance and a process scheme from a part level to a part level to a product level is realized, so that designers are assisted to make design decisions, the design quality of the product scheme is improved, and the cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent manufacturing, and in particular relates to a firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis. Background Art

[0002] Firearms products are first designed in drawings, and then physically manufactured according to the drawings. Firearms design is a crucial part of the development process, and the design stage determines how firearms products are processed and assembled. The impact of design on the entire development cycle, cost and quality will be propagated backwards in an ever-increasing manner. We should try to avoid adverse effects on subsequent related processing and assembly due to unreasonable design. Therefore, we should conduct an assemblability assessment of the design plan to eliminate hidden dangers in the assembly stage as early as possible and avoid major rework. Assemblability assessment of firearms products is of great significance to enhancing product competitiveness.

[0003] Assembling evaluation is to evaluate the characteristics of the parts or components of the product, such as ease of assembly and assembly stability. Further, an assembling evaluation method for product assembly is proposed and an overall framework of the evaluation method is established. In the assembly process of complex firearms, due to the high precision requirements of parts, a variety of shapes and complex assembly relationships, there are many factors that affect product reliability. Therefore, for high-precision products such as firearms, it is particularly important to establish a mature assembling evaluation system to ensure its assembling. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a firearm assemblability evaluation scheme based on self-consistent fusion of qualitative and quantitative analysis.

[0005] The first aspect of the present invention discloses a firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis, the method comprising:

[0006] Step 1: Construct assembly process evaluation indicators; the assembly process evaluation indicators include part-level evaluation indicators, component-level evaluation indicators, and product-level evaluation indicators:

[0007] Among them, the part-level evaluation indicators include the quality of the part, the symmetry of the part, the size of the zero level, the matching reference of the part, and the number of adjacencies of the part; the component-level evaluation indicators include the aggregation of the assembly sequence, the number of transfers of the reference components, the parallelism of the assembly, the number of assembly redirections, the assembly working hours, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; the product-level evaluation indicators include the number of dynamic interferences and the number of static interferences;

[0008] Step 2: Use the assembly process evaluation method to comprehensively evaluate the constructed assembly process evaluation indicators to obtain the optimal assembly process solution;

[0009] Among them, the assembly process evaluation method includes fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

[0010] According to the method of the first aspect, the part-level evaluation index includes the quality of the part, the symmetry of the part, the zero-level size, the matching reference of the part, and the number of adjacencies of the part; wherein:

[0011] Quality evaluation index of parts 1 It is divided into four evaluation results, each of which has a corresponding evaluation value;

[0012] Part symmetry evaluation index f 2 Described as:

[0013]

[0014] Among them, x 2 It is expressed as the number of times the part satisfies the symmetry of the plane xoy, xoz, yoz;

[0015] Zero-level size evaluation index f 3 It is divided into four evaluation results, each of which has a corresponding evaluation value;

[0016] Parts fit benchmark evaluation index f 4 Described as:

[0017]

[0018] Among them, x 4 It is expressed as the number of assembly datums. When the number of assembly datums is greater than 4, the assemblability is 0.

[0019] Part adjacency evaluation index f 5 Described as:

[0020]

[0021] Among them, x 5 Represents the number of adjacencies of a part. When the number of adjacencies of a part is greater than 4, the assemblability is 0.

[0022] According to the method of the first aspect, the component-level evaluation index includes the aggregation of assembly sequence, the number of reference component transfers, the parallelism of assembly, the number of assembly redirections, the assembly time, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; wherein:

[0023] The aggregation evaluation index f of the assembly sequence 6 Described as:

[0024]

[0025] Among them, x6 N is the average number of assembly aggregation operations; i is the number of consecutive operations completed for the i-th assembly type in the sequence; m is the number of assembly types in the entire sequence;

[0026] Evaluation index f of the number of transfer times of the benchmark component 7 Described as:

[0027]

[0028] Where n is the total number of parts; x 7 is the number of transfers of the reference component;

[0029] Assembly parallelism evaluation index f 8 Described as:

[0030]

[0031] Among them, x 8 Indicates the depth of the tree in the tree structure assembly sequence; x max Indicates the maximum depth of the assembly sequence tree;

[0032] Assembly redirection number evaluation index f 9 Described as:

[0033]

[0034] Among them, x 9 is the total redirection number of the assembly sequence; D is the theoretical maximum redirection number; d i represents the direction change index when the i-th part is assembled; δ i =δ i-1 It means that the assembly direction of the i-th part is the same as that of the previous part, and the evaluation index is 0;

[0035] Assembly time evaluation index f 10 Described as:

[0036]

[0037] Among them, N min Indicates the theoretical minimum number of parts; t a Indicates the basic assembly time of a part; t n Indicates the assembly time of the completed product;

[0038] Assembly space evaluation index f 11 It is divided into four evaluation results, each of which has a corresponding evaluation value;

[0039] The assembly stability evaluation index is described as f 12It is divided into four evaluation results, each of which has a corresponding evaluation value;

[0040] The evaluation index of assembly fastening type is described as f 13 It is divided into four evaluation results, each of which has a corresponding evaluation value;

[0041] Total assembly operation number evaluation index f 14 Described as:

[0042]

[0043] Among them, N min Represents the theoretical minimum total assembly times, N a Indicates the actual total assembly times.

[0044] According to the method of the first aspect, the product-level evaluation index includes the number of dynamic interferences and the number of static interferences; wherein:

[0045] Dynamic interference relies on UG software to establish the firearm assembly sequence and perform dynamic interference simulation, with the goal of no collision between parts and no spatial collision interference on the assembly path; during the assembly process of firearm products, the less collision interference occurs between parts, the higher the accessibility of parts on the assembly path;

[0046] Static interference refers to whether parts overlap in space in the final state after assembly. When normal interference problems such as shaft-hole interference fit and contact interference occur, the interference is confirmed and ignored, and only abnormal interference is counted. The fewer the static interferences, the higher the assemblability.

[0047] According to the method of the first aspect, a fuzzy comprehensive evaluation method is used to comprehensively evaluate the constructed assembly process evaluation index; specifically, the method includes:

[0048] Establish an evaluation index set U = {u 1 ,u 2 ,…,u n}; where U is the index set formed after selecting the influencing factors of the evaluation object and screening;

[0049] Create a review set V = {v 1 ,v 2 ,…,v 5}; The comment set V is the evaluation set of evaluation indicators, which is divided into 5 fuzzy levels.

[0050] Establish a fuzzy relationship matrix R; after determining the evaluation index set and the comment set, establish the fuzzy relationship matrix:

[0051]

[0052] Among them, r ij Denotes the evaluation index u i The corresponding j-th membership degree;

[0053] Construct a weight set W = {w 1 ,w 2 ,…,w n}; In the process of evaluating the assembly process, each evaluation index has a different degree of influence on the evaluation object. The weight is selected according to the importance of the evaluation factor and a weight set W is formed;

[0054] Construct a fuzzy comprehensive evaluation model B; wherein, the established weight set W is multiplied by the fuzzy relationship matrix R to obtain the fuzzy comprehensive evaluation model B:

[0055]

[0056] According to the method of the first aspect, the TOPSIS method is used to comprehensively evaluate the constructed assembly process evaluation index; specifically including:

[0057] Establish a decision matrix; where the evaluation index set U = {u 1 ,u 2 ,,…u n} and evaluation set V′={v 1 ,v 2 ,…,v m}Construct the decision matrix X as:

[0058]

[0059] The decision matrix is ​​dimensionless; the numerical processing in the decision matrix is ​​divided into the following two types:

[0060] For the benefit index, the larger the value, the better it is. Then:

[0061]

[0062] For cost indicators, the smaller the value, the better, then:

[0063]

[0064] For moderate indicators, the closer the value is to the middle value, the better it is. Then:

[0065]

[0066] A weighted decision matrix is ​​established; based on the dimensionless decision matrix X′, the importance of the evaluated object is weighted according to the evaluation index to construct the weighted decision Z:

[0067]

[0068] Among them, w i It is represented as the weight corresponding to the i-th evaluation index, w i ∈[0,1];

[0069] Determine the positive and negative ideal solutions; the positive ideal solution refers to the optimal solution achieved in the process of indicator convergence, and the negative ideal solution is the worst solution; it is expressed as follows:

[0070]

[0071]

[0072] Among them, Z + It is represented as a positive ideal solution, Z - It is represented as a negative ideal solution;

[0073] Calculate the distance between the evaluation object and the ideal solution; the Euclidean distance is used to represent the distance from the evaluation object to the ideal solution. The closer the distance, the better the evaluation. The Euclidean distance model is:

[0074]

[0075] Among them, D + , D j - Respectively represent the Euclidean distance from the i-th evaluation index to the positive and negative ideal solutions;

[0076] Calculate the evaluation value; in the TOPSIS evaluation method, the evaluation value indicates the closeness between the evaluation scheme and the optimal scheme, and the evaluation value e i for:

[0077]

[0078] Among them, e i ∈[0,1], and e i The larger the value, the closer the evaluation object is to the optimal solution, and the better the evaluation object is.

[0079] According to the method of the first aspect, the constructed assembly process evaluation index is comprehensively evaluated using the grey correlation method; specifically including:

[0080] Determine the ideal solution for the evaluation object X 0 and m evaluated solutions X i (i=1,2,…,m); where scheme X is represented by X=(x 1 ,x 2 ,…,x n ), x iRepresents the evaluation attribute value of the i-th evaluation index;

[0081] Solve for the correlation coefficient:

[0082]

[0083] Δ i (k) = |X 0 (k)-X i (k)|,μ∈[0,1]

[0084] Among them, η i (k) represents the correlation coefficient of evaluation scheme i at the kth evaluation index, Δ i (k) represents the difference between the reference scheme and the comparison scheme of scheme i in the k evaluation index, and μ is the resolution coefficient;

[0085] Calculate the grey relational degree:

[0086]

[0087] Among them, η i Indicates the evaluated solution X i With the ideal solution X 0 The greater the correlation value, the better the evaluated solution is.

[0088] A second aspect of the present invention discloses a combat plan evaluation system under complex confrontation conditions for simulation and deduction, the system comprising a processing unit, and the processing unit is configured to perform the following steps:

[0089] Step 1: Construct assembly process evaluation indicators; the assembly process evaluation indicators include part-level evaluation indicators, component-level evaluation indicators, and product-level evaluation indicators:

[0090] Among them, the part-level evaluation indicators include the quality of the part, the symmetry of the part, the size of the zero level, the matching reference of the part, and the number of adjacencies of the part; the component-level evaluation indicators include the aggregation of the assembly sequence, the number of transfers of the reference components, the parallelism of the assembly, the number of assembly redirections, the assembly working hours, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; the product-level evaluation indicators include the number of dynamic interferences and the number of static interferences;

[0091] Step 2: Use the assembly process evaluation method to comprehensively evaluate the constructed assembly process evaluation indicators to obtain the optimal assembly process solution;

[0092] Among them, the assembly process evaluation method includes fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

[0093] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis described in the first aspect of the present disclosure are implemented.

[0094] The fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the firearm assemblability assessment method based on self-consistent fusion of qualitative and quantitative analysis described in the first aspect of the present disclosure.

[0095] In summary, in the solution of the present invention, for the product design solution, firstly, the possible assembly process solutions are objectively and quantitatively calculated and subjectively evaluated, and then these assembly process solutions are ranked by merit to find out the degree of proximity between the evaluated solutions and the optimal solution. The present invention realizes the assemblability evaluation of objects such as design solutions, product performance and process solutions from the part level to the component level and then to the product level, thereby assisting designers in making design decisions, improving the quality of product solution design and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0097] Figure 1 This is a diagram of a new assessment project interface in an embodiment of the present invention.

[0098] Figure 2 Schematic diagram of the whole gun assemblability evaluation index system in an embodiment of the present invention.

[0099] Figure 3 A schematic diagram of creating an evaluation object in an embodiment of the present invention.

[0100] Figure 4 Schematic diagram of the weights and values ​​of various indicators in an embodiment of the present invention.

[0101] Figure 5a A schematic diagram of creating evaluation indicators in an embodiment of the present invention; Figure 5b This is a schematic diagram of qualitative indicator replication in an embodiment of the present invention; Figure 5c This is a schematic diagram of editing indicator attributes in an embodiment of the present invention; Figure 5d Schematic diagram of the interface for completing the determination of indicator values ​​and weights in an embodiment of the present invention.

[0102] Figure 6 Schematic diagram of the analysis results of the fuzzy comprehensive evaluation method in an embodiment of the present invention.

[0103] Figure 7 Schematic diagram of the evaluation and analysis results of the TOPSIS method in an embodiment of the present invention.

[0104] Figure 8 Schematic diagram of the evaluation and analysis results of the grey correlation method in an embodiment of the present invention. DETAILED DESCRIPTION

[0105] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 making creative work are within the scope of protection of the present invention.

[0106] Explanation of relevant terms:

[0107] Self-consistent integration refers to the application of the concept of self-consistency to different fields or situations to ensure that the various parts or elements are logically consistent without contradictions or conflicts.

[0108] The technical characteristics mainly include whether the assembly process is reasonable, whether the assembly reliability is guaranteed, the feasibility of the assembly sequence, etc.

[0109] The cost characteristics mainly include reducing the cost of parts materials, shortening time costs, and reducing waste of assembly resources.

[0110] Social characteristics mainly refer to the impact of products on the social environment.

[0111] The fuzzy comprehensive rating method is a fuzzy comprehensive mathematical evaluation method that can quantify and comprehensively evaluate artificial qualitative indicators, and can effectively solve those difficult-to-quantify and fuzzy evaluation problems.

[0112] The TOPSIS method is used to evaluate the advantages and disadvantages of evaluation schemes under the condition of limited research objects, and to solve the degree of closeness between the evaluated scheme and the optimal scheme. It is a common and effective analysis method in multi-objective decision analysis.

[0113] Grey correlation method is a factor statistical analysis method. It is based on grey theory and performs grey correlation and quantitative analysis on the evaluation object to determine the quality of the evaluation object. Its main idea is to evaluate the trend of correlation change of evaluation indicators in the evaluation system.

[0114] The first aspect of the present invention proposes a firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis.

[0115] Step 1: Construction of assembly process evaluation indicators

[0116] In order to evaluate and optimize the assembly performance of firearms products during the product design stage, thereby reducing assembly costs and maintenance costs and improving product assembly performance, it is necessary to comprehensively consider multiple factors to evaluate the firearms assembly process. In the process of firearms assembly, the factors affecting the assemblability of the entire gun are roughly divided into three aspects: part level, assembly process, and assembly feasibility evaluation indicators. Among them, the nature of the indicators is mainly divided into qualitative indicators and quantitative indicators. For indicators that can be quantitatively calculated, mathematical models can be established and then valued, such as the number of assembly interferences, the number of assembly tool changes, etc. For indicators that cannot be quantitatively calculated, stereotyped analysis is performed, and experts manually judge the indicators based on experience, which are mainly divided into six categories: excellent, good, medium, qualified, poor, and poor. Finally, the assemblability analysis of the entire gun is performed based on the indicator weight level indicator combined with the weighted average method or fuzzy comprehensive evaluation method.

[0117] (1) Part-level evaluation indicators

[0118] Parts are the basic units in the assembly process, and part features have a great impact on the assemblability of complex products.

[0119] 1) Quality of parts

[0120] In the product assembly process, the larger or smaller the part mass, the worse the part's assemblability, and it should be assembled first in the assembly process. 1 Manual judgment is mainly performed by experts, as shown in the following table:

[0121] Table 1 Quality indicators and corresponding evaluation values ​​of parts

[0122] Evaluation results Difference middle good excellent Rating 0.25 0.5 0.75 1

[0123] 2) Symmetry of parts

[0124] The better the symmetry of a part, the more effectively it can reduce the difficulty of assembling the part during the assembly process, and the better the assemblability of the part. On the contrary, if the part structure is asymmetrical, it needs to be reoriented during assembly, and the greater the difficulty of assembly, the worse the assemblability, and it should be assembled first. Part symmetry evaluation index f 2 It can be described as:

[0125]

[0126] Where: x 2It is expressed as the number of times the part satisfies the symmetry of the planes xoy, xoz, yoz.

[0127] 3)Zero-level size

[0128] The outer contour size of the part also affects its assemblability. If the size is too large or too small, special auxiliary tools are needed for auxiliary assembly. Therefore, the higher the assembly difficulty, the worse its assemblability. Therefore, it is necessary to control the size of the part within a reasonable range. The evaluation value of this indicator f 3 Expert manual judgment is also used.

[0129] Table 2 Dimensional evaluation indexes and corresponding evaluation values ​​of parts

[0130] Evaluation results Difference middle good excellent Rating 0.25 0.5 0.75 1

[0131] 4) Parts matching datum

[0132] The matching datum of a part is the number of datums that a part uses as a reference for other parts when assembled. The more matching datums a part has, the more difficult it is to assemble, the worse its assemblability is, and the more priority it should have. 4 It can be described as:

[0133]

[0134] Where: x 4 Expressed as the number of assembly datums, when the number of assembly datums is greater than 4, the assemblability is the worst.

[0135] 5) Number of adjacencies of parts

[0136] The adjacency relationship of a part is the number of parts in an assembly that have an assembly or contact relationship with the surrounding parts. The more adjacency the part has, the more complex the assembly of the parts is, and the assembly should be prioritized, and the worse the assemblability is. 5 It can be described as:

[0137]

[0138] where x 5 Expressed as the number of adjacencies of parts. When the number is greater than 4, the worst assemblability is 0.

[0139] (2) Component-level evaluation indicators

[0140] The component-level evaluation index mainly evaluates the product assembly based on the quality of the component assembly process to ensure the simplicity, assembly quality and assembly efficiency of the product assembly process. The specific evaluation indicators are as follows.

[0141] 1) Polymerization of assembly sequences

[0142] The aggregation of assembly sequence means that the sequence should be planned in the product assembly process so that similar assembly operations can be completed as integrated as possible and the number of changes of assembly tools and fixtures can be minimized. Its evaluation value can be measured by the average number of operations of the same tool or inspection tool working continuously, that is, the aggregation index of assembly sequence f 6 It can be expressed as:

[0143]

[0144] Where: x 6 N is the average number of assembly aggregation operations; i is the number of consecutive operations completed for the i-th assembly type in the sequence; m is the number of assembly types in the entire sequence.

[0145] 2) Number of times the reference part is transferred

[0146] In the product assembly process, the more times the assembly reference parts are transferred, the greater the assembly difficulty and the worse the assemblability. 7 It can be expressed as:

[0147]

[0148] Where: n is the total number of parts; x 7 is the number of times the reference component is transferred.

[0149] 3) Parallelism of assembly

[0150] Assembly parallelism refers to the ability of an assembly task to be divided into several components and assembled simultaneously during the actual assembly process. The more parts that are assembled in parallel, the higher the parallelism, the shorter the assembly time, and the higher the assemblability. Assembly parallelism evaluation index f 8 It can be expressed as:

[0151]

[0152] x 8 Indicates the depth of the tree in the tree structure assembly sequence; x max Indicates the maximum depth of the assembly sequence tree.

[0153] 4) Assembly redirection number

[0154] The number of assembly redirection refers to the number of times the direction of the assembled parts changes during the product assembly process. The more assembly redirection times, the more complex the assembly process, the more reversal operations, and the worse the assemblability. Therefore, the number of assembly redirection should be minimized in the product sequence planning stage. The evaluation index of assembly redirection number f 9 It can be expressed as:

[0155]

[0156] Where: x 9 is the total redirection number of the assembly sequence; D is the theoretical maximum redirection number; d i represents the direction change index when the i-th part is assembled; δ i =δ i-1 It means that the assembly direction of the i-th part is the same as that of the previous part, and the evaluation index is 0.

[0157] 5) Assembly time

[0158] Assembly time is the total time after all parts of the current assembly sequence are installed. The lower the assembly time, the higher the assembly efficiency and the better the assemblability. The DFA (Design for Assembly) index is usually used to calculate the assembly time index, that is, the assembly time evaluation index f 10 It can be expressed as:

[0159]

[0160] Where: N min Indicates the theoretical minimum number of parts; t a Indicates the basic assembly time of a part; t n Indicates the assembly time of the completed product.

[0161] 6) Assembly space

[0162] During the product assembly process, parts that require assembly tools must be able to reach the assembly position quickly and easily, and must be within sight. Sufficient operating space is also required to ensure that the limbs of the assembly action do not collide with the product, thereby increasing the success rate of assembly. The evaluation indicators of assembly space are mainly determined manually by experts, as shown in the following table:

[0163] Table 3 Assembly space evaluation indexes and corresponding evaluation values

[0164] Evaluation results insufficient Moderate good adequate Rating 0.25 0.5 0.75 1

[0165] 7) Assembly stability

[0166] Assembly stability refers to the ability of the components to maintain their assembly stability under the action of gravity and assembly force after assembly. The stronger the assembly stability, the easier it is to ensure assembly accuracy and the higher the assemblability of the product. The assembly stability evaluation index is also manually determined by experts, as shown in the following table:

[0167] Table 4 Assembly stability evaluation indicators and corresponding evaluation values

[0168] Evaluation results Poor stability Generally stable More stable Good stability Rating 0.25 0.5 0.75 1

[0169] 8) Assembly fastening type

[0170] There are fastening methods such as welding, riveting, and threaded connection in product assemblies. The evaluation index of assembly fastening type is manually determined by experts, as shown in the following table:

[0171] Table 5 Evaluation indexes and evaluation values ​​of assembly fastening types

[0172] Evaluation results Riveting or welding Shaft-hole fit Threaded connection touch Rating 0.25 0.5 0.75 1

[0173] 9) Total number of assembly operations

[0174] During the product assembly process, the process can be roughly divided into operations such as delivering parts, grabbing parts, assembling parts, and positioning parts. For product assembly, the fewer the total number of operations from delivering parts to finished products, the higher the product assembly efficiency. Theoretically, the total number of assembly operations index f 14 It can be expressed as:

[0175]

[0176] Where: N min Represents the theoretical minimum total assembly times, N a Indicates the actual total assembly times.

[0177] (3) Product-level evaluation indicators

[0178] Product-level evaluation indicators mainly evaluate the product from the perspective of the geometric feasibility of the assembly sequence.

[0179] The main indicators are as follows:

[0180] 1) Dynamic interference quantity

[0181] During the assembly process of firearms products, it is necessary to ensure that the parts do not collide with other parts during the assembly process. Ensuring the accessibility of parts on the assembly path is a prerequisite for smooth assembly. Dynamic interference mainly relies on UG software to establish the firearm assembly sequence and perform dynamic interference simulation. In theory, collisions between parts are not allowed, that is, spatial collisions and interferences are not allowed on the assembly path.

[0182] 2) Static interference quantity

[0183] Static interference refers to whether parts overlap in space in the final state after assembly. When normal interference problems such as shaft-hole interference fit and contact interference occur, experts are required to confirm the interference and ignore such interference, and count abnormal interference. In theory, the fewer static interferences, the higher the assemblability.

[0184] In the process of evaluating the assembly process for firearms products, it is necessary to comprehensively consider the comprehensive evaluation of various indicators, so as to achieve a more reasonable evaluation and analysis of the firearms assembly process plan. The following table contains a comprehensive evaluation indicator system at the part level, component level and product level. The detailed information is shown in the table below:

[0185] Table 6 Comprehensive evaluation index table of firearms assembly process

[0186]

[0187]

[0188] Step 2: Assembly process evaluation method

[0189] The evaluation of firearm assembly process is to determine the research object and obtain quantitative or qualitative data according to the degree of membership of the evaluation index. A certain evaluation method is used to process these data and convert them into a comprehensive evaluation value that can be evaluated. In this way, different schemes can be ranked, compared and analyzed according to the evaluation value, and the optimal assembly process scheme can be selected. This article specifically describes several common evaluation methods.

[0190] (1) Fuzzy comprehensive evaluation method

[0191] Fuzzy comprehensive rating method is a fuzzy comprehensive mathematical evaluation method, which can quantify and comprehensively evaluate the human qualitative indicators, and can effectively solve those evaluation problems that are difficult to quantify and fuzzy. The disadvantage is that the weight of the factors is not objective, and it is difficult to eliminate the correlation between the factors. The evaluation process of the fuzzy comprehensive evaluation model is as follows:

[0192] 1) Establish an evaluation index set U = {u 1 ,u 2 ,…,u n The evaluation factor set U is a set of indicators formed after selecting the influencing factors of the evaluation object and screening them.

[0193] 2) Create a review set V = {v 1 ,v 2 ,…,v 5} = {excellent, good, medium, qualified, poor}. The comment set V is the evaluation set of the evaluation indicators. This chapter divides the qualitative evaluation indicators into five fuzzy levels: excellent, good, medium, qualified, and poor.

[0194] 3) Establish the fuzzy relationship matrix R. After determining the evaluation index set and comment set, the fuzzy relationship matrix can be established:

[0195]

[0196] Where: r ij Expressed as the evaluation index ui The corresponding j-th membership.

[0197] 4) Construct a weight set W = {w 1 ,w 2 ,…,w n In the process of evaluating the assembly process, the influence of each evaluation index on the evaluation object varies. In order to make the evaluation result more reasonable, the weights are selected according to the importance of the evaluation factors and a weight set W is formed.

[0198] 5) Fuzzy comprehensive evaluation model B. Multiplying the established weight set W with the fuzzy relationship matrix R can obtain the fuzzy comprehensive evaluation model B:

[0199]

[0200] (2) TOPSIS method

[0201] The TOPSIS method is used to evaluate the advantages and disadvantages of evaluation schemes in the case of a limited number of research objects, and to find out the degree of proximity between the evaluated scheme and the optimal scheme. It is a common and effective analysis method in multi-objective decision analysis. The steps of the TOPSIS evaluation model are as follows:

[0202] 1) Establish a decision matrix. Through the evaluation index set U = {u 1 ,u 2 ,,…u n} and evaluation set V′={v 1 ,v 2 ,…,v m}Construct the decision matrix X as:

[0203]

[0204] 2) Dimensionless processing of decision matrix. Dimensionless processing is to eliminate the influence of original indicator units and their order of magnitude through mathematical transformation of the values ​​in the decision matrix. There are two main types of numerical processing in the decision matrix:

[0205] The greater the value, the better the efficiency index.

[0206]

[0207] Cost index, that is, the smaller the value, the better.

[0208]

[0209] Moderate indicators, that is, the closer the value is to the middle value, the better.

[0210]

[0211] 3) Establish a weighted decision matrix. The dimensionless decision matrix X′ is used to assign weights to the importance of the evaluated object according to the evaluation index, and the weighted decision matrix Z is constructed as follows:

[0212]

[0213] Where: w i It is represented as the weight corresponding to the i-th evaluation index and satisfies And w i ∈[0,1].

[0214] 4) Determine the positive and negative ideal solutions. The positive ideal solution is the optimal solution achieved when the indicators converge, and the negative ideal solution is the worst solution. The model is expressed as follows:

[0215]

[0216] Where: Z + It is represented as a positive ideal solution, Z - It is represented as a negative ideal solution.

[0217] 5) Calculate the distance between the evaluation object and the ideal solution. Usually, the Euclidean distance is used to represent the distance between the evaluation object and the ideal solution. The closer the distance, the better. Conversely, the farther the distance, the worse the evaluation. The Euclidean distance model is expressed as:

[0218]

[0219]

[0220] Where: D + , D j - They represent the Euclidean distance from the i-th evaluation index to the positive and negative ideal solutions respectively.

[0221] 6) Calculate the evaluation value. In the TOPSIS evaluation method, the evaluation value indicates the closeness between the evaluation scheme and the optimal scheme. i It is expressed as:

[0222]

[0223] Where: e i ∈[0,1], and e i The larger the value, the closer the evaluation object is to the optimal solution, and the better the evaluation object is.

[0224] (3) Grey correlation method

[0225] Grey correlation method is a factor statistical analysis method. It is based on grey theory and performs grey correlation and quantitative analysis calculation on the evaluation object to determine the quality of the evaluation object. Its main idea is to evaluate the trend of correlation change of evaluation indicators in the evaluation system. The grey correlation evaluation method process is as follows:

[0226] 1) Determine the ideal solution X for the evaluation object 0 and m evaluated solutions X i (i=1,2,…,m). The solution X is expressed as: X=(x 1 ,x 2 ,…,x n ). where x i Represents the evaluation attribute value of the i-th evaluation indicator.

[0227] 2) Solve for the correlation coefficient.

[0228]

[0229] Δ i (k) = |X 0 (k)-X i (k)|,μ∈[0,1]

[0230] Where: η i (k) represents the correlation coefficient of evaluation scheme i at the kth evaluation index, Δ i (k) represents the difference between the reference scheme and the comparison scheme of scheme i in the k evaluation index, and μ is the resolution coefficient.

[0231] 3) Calculate the grey relational degree.

[0232]

[0233] Where: η i Indicates the evaluated solution X i With the ideal solution X 0 The greater the correlation value, the better the evaluated solution. When the correlation value is 1, it is the best solution, and when the correlation value is 0, it is the worst solution.

[0234] Specific example (software implementation of assembly process evaluation module)

[0235] The assembly process evaluation module can evaluate the design scheme, product performance, process scheme and other evaluation objects from various aspects, and derive the advantages and disadvantages and grade levels of the schemes, so as to assist designers in making design decisions. We can quickly establish a hierarchical evaluation index system based on the characteristics of the product or evaluation object by selecting or dragging the mouse. The system can use methods such as hierarchical analysis and fuzzy comprehensive evaluation to assign weights and perform fuzzy comprehensive evaluation of the index system. Each evaluation index value or weight can be associated through Excel or XML files, and the quantitative calculation of the index can be completed with the help of functions for calculation and statistical analysis. The development process is as follows:

[0236] (1) Create a comprehensive assessment project

[0237] First, create a new comprehensive evaluation project based on the software platform, store it with the file extension evp file, and select the existing comprehensive evaluation component module of the software, such as Figure 1 shown.

[0238] (2) Create an evaluation indicator system

[0239] Add the determined indicators to the project one by one. First, establish them in sequence according to the indicator relationship, and establish corresponding secondary indicators under the primary indicators. The interface of the established indicator system is as follows: Figure 2 shown.

[0240] (3) Create an evaluation object and indicator value association

[0241] The evaluation object is an indispensable key factor in the evaluation system, so it is necessary to create the evaluation object and the associated indicator value in the evaluation indicator system. Figure 3 As shown:

[0242] The evaluation object can repeat the operation to add multiple evaluated objects for further comparative analysis, and the corresponding parameters and indicator values ​​of each object should be different. To successfully evaluate the evaluation object, it is necessary to further determine the indicator weight and indicator value of each parameter. The weight of the indicator value needs to be associated with the xml data file as follows Figure 4 shown.

[0243] According to the weight table, weight association is performed in the evaluation index system. The normalized index value is mainly applicable to quantitative indicators, which means the value generated by the actual parameter after the calculation of the index evaluation function in the previous text. In the figure below, the actual value of the index can be provided to the designer for free output, and the corresponding normalized index value will also change accordingly. The qualitative index evaluation value can be selected by the expert in the input interface, and the corresponding evaluation value is also one-to-one. The process interface is shown in Figure 5.

[0244] (4) Evaluation and analysis

[0245] After the preliminary work is completed, the weight and evaluation value of the evaluated object and each indicator are determined. The next most important step is to select the evaluation method and conduct evaluation analysis. Through the evaluation module analysis and calculation, different pairs of assembly process evaluation schemes are analyzed. This chapter lists three different assembly process schemes for comparative analysis, as shown in the following table.

[0246] Table 7 Index parameters and evaluation values ​​of different assembly process solutions

[0247]

[0248]

[0249] After inputting the corresponding parameters in the evaluation interface, select different evaluation analysis methods for evaluation, and then output the analysis results on the console, such as Figure 6 As shown in the figure, the evaluation results were compared by using fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

[0250] The three evaluation schemes were benchmarked and analyzed using three different evaluation methods. Although the analysis values ​​of different evaluation methods for the same assembly process scheme are slightly different, they all show that assembly process scheme 2 is the best and assembly process scheme 1 is the worst. As shown in the following table:

[0251] Table 8 Analysis results of different analysis methods for three different assembly process solutions

[0252] Assembly solution Fuzzy comprehensive evaluation method TOPSIS method Grey Relationship Method Assembly process solution 1 76.0667 72.9329 70.7058 Assembly process solution 2 91.0289 89.7731 86.8532 Assembly process solution 3 81.26 78.1763 77.7022

[0253] It can be seen that the present invention uses the part-level, component-level, and product-level evaluation index factors that affect the assemblability of the entire gun, based on objective quantitative calculation and subjective evaluation methods, to achieve the construction of a firearm assembly process evaluation index system. The present invention uses objective quantitative and qualitative data, and uses fuzzy comprehensive evaluation method, TOPSIS method, and gray correlation method to process these data into evaluable comprehensive evaluation values, and then sorts and selects the optimal assembly process solution according to the evaluation values. (How to convert and merge into comprehensive evaluation values) Whether the three methods are used for assemblability evaluation for the first time.

[0254] A second aspect of the present invention discloses a combat plan evaluation system under complex confrontation conditions for simulation and deduction, the system comprising a processing unit, and the processing unit is configured to perform the following steps:

[0255] Step 1: Construct assembly process evaluation indicators; the assembly process evaluation indicators include part-level evaluation indicators, component-level evaluation indicators, and product-level evaluation indicators:

[0256] Among them, the part-level evaluation indicators include the quality of the part, the symmetry of the part, the size of the zero level, the matching reference of the part, and the number of adjacencies of the part; the component-level evaluation indicators include the aggregation of the assembly sequence, the number of transfers of the reference components, the parallelism of the assembly, the number of assembly redirections, the assembly working hours, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; the product-level evaluation indicators include the number of dynamic interferences and the number of static interferences;

[0257] Step 2: Use the assembly process evaluation method to comprehensively evaluate the constructed assembly process evaluation indicators to obtain the optimal assembly process solution;

[0258] Among them, the assembly process evaluation method includes fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

[0259] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis described in the first aspect of the present disclosure are implemented.

[0260] The fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the firearm assemblability assessment method based on self-consistent fusion of qualitative and quantitative analysis described in the first aspect of the present disclosure.

[0261] In summary, in the solution of the present invention, for the product design solution, firstly, the possible assembly process solutions are objectively and quantitatively calculated and subjectively evaluated, and then these assembly process solutions are ranked by merit to find out the degree of proximity between the evaluated solutions and the optimal solution. The present invention realizes the assemblability evaluation of objects such as design solutions, product performance and process solutions from the part level to the component level and then to the product level, thereby assisting designers in making design decisions, improving the quality of product solution design and reducing costs.

[0262] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. A firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis, characterized in that: The method comprises: Step 1: Construct assembly process evaluation indicators; the assembly process evaluation indicators include part-level evaluation indicators, component-level evaluation indicators, and product-level evaluation indicators: Among them, the part-level evaluation indicators include the quality of the part, the symmetry of the part, the size of the zero level, the matching reference of the part, and the number of adjacencies of the part; the component-level evaluation indicators include the aggregation of the assembly sequence, the number of transfers of the reference components, the parallelism of the assembly, the number of assembly redirections, the assembly working hours, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; the product-level evaluation indicators include the number of dynamic interferences and the number of static interferences; Step 2: Use the assembly process evaluation method to comprehensively evaluate the constructed assembly process evaluation indicators to obtain the optimal assembly process solution; Among them, the assembly process evaluation method includes fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

2. A firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 1, characterized in that: The part-level evaluation indexes include the quality of the part, the symmetry of the part, the zero-level size, the matching reference of the part, and the number of adjacencies of the part; wherein: The quality evaluation index f1 of the part is divided into four evaluation results, each of which has a corresponding evaluation value; The symmetry evaluation index f2 of the part is described as: Among them, x2 represents the number of times the part satisfies the symmetry of the plane xoy, xoz, yoz; The zero-level size evaluation index f3 is divided into four evaluation results, each of which has a corresponding evaluation value; The part fit benchmark evaluation index f4 is described as: Among them, x4 represents the number of assembly bases. When the number of assembly bases is greater than 4, the assemblability is 0; The adjacency evaluation index f5 of the part is described as: Among them, x5 represents the adjacency number of the part. When the adjacency number of the part is greater than 4, the assemblability is 0.

3. The firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 2 is characterized in that: The component-level evaluation indicators include assembly sequence aggregation, reference component transfer times, assembly parallelism, assembly redirection number, assembly man-hours, assembly space, assembly stability, assembly fastening type, and total assembly operation number; among which: The aggregation evaluation index f6 of the assembly sequence is described as: Among them, x6 is the average number of assembly aggregation operations; N i is the number of consecutive operations completed for the i-th assembly type in the sequence; m is the number of assembly types in the entire sequence; The benchmark component transfer number evaluation index f7 is described as: Where n is the total number of parts; x7 is the number of times the reference part is transferred; The parallelism evaluation index f8 of the assembly is described as: Among them, x8 represents the depth of the tree in the tree structure assembly sequence; x max Indicates the maximum depth of the assembly sequence tree; The assembly redirection number evaluation index f9 is described as: Where x9 is the total number of redirections in the assembly sequence; D is the theoretical maximum number of redirections; d i represents the direction change index when the i-th part is assembled; δ i =δ i-1 It means that the assembly direction of the i-th part is the same as that of the previous part, and the evaluation index is 0; Assembly time evaluation index f 10 Described as: Among them, N min Indicates the theoretical minimum number of parts; t a Indicates the basic assembly time of a part; t n Indicates the assembly time of the completed product; Assembly space evaluation index f 11 It is divided into four evaluation results, each of which has a corresponding evaluation value; The assembly stability evaluation index is described as f 12 It is divided into four evaluation results, each of which has a corresponding evaluation value; The evaluation index of assembly fastening type is described as f 13 It is divided into four evaluation results, each of which has a corresponding evaluation value; Total assembly operation number evaluation index f 14 Described as: Among them, N min Represents the theoretical minimum total assembly times, N a Indicates the actual total assembly times.

4. The firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 3 is characterized in that: The product-level evaluation index includes the number of dynamic interferences and the number of static interferences; wherein: Dynamic interference relies on UG software to establish the firearm assembly sequence and perform dynamic interference simulation, with the goal of no collision between parts and no spatial collision interference on the assembly path; during the assembly process of firearm products, the less collision interference occurs between parts, the higher the accessibility of parts on the assembly path; Static interference refers to whether parts overlap in space in the final state after assembly. When normal interference problems such as shaft-hole interference fit and contact interference occur, the interference is confirmed and ignored, and only abnormal interference is counted. The fewer the static interferences, the higher the assemblability.

5. The firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 4, characterized in that: The fuzzy comprehensive evaluation method is used to comprehensively evaluate the constructed assembly process evaluation indicators; specifically including: Establish an evaluation index set U = {u1,u2,…,u n }; where U is the index set formed after selecting the influencing factors of the evaluation object and screening; Create a comment set V = {v1, v2, ..., v5}; where the comment set V is an evaluation set of evaluation indicators, divided into 5 fuzzy levels. Establish a fuzzy relationship matrix R; after determining the evaluation index set and the comment set, establish the fuzzy relationship matrix: Among them, r ij Represents the evaluation index u i The corresponding j-th membership degree; Construct a weight set W = {w1,w2,…,w n }; In the process of evaluating the assembly process, each evaluation index has a different degree of influence on the evaluation object. The weight is selected according to the importance of the evaluation factor and a weight set W is formed; Construct a fuzzy comprehensive evaluation model B; wherein, the established weight set W is multiplied by the fuzzy relationship matrix R to obtain the fuzzy comprehensive evaluation model B:

6. The firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 5, characterized in that: The TOPSIS method is used to comprehensively evaluate the constructed assembly process evaluation indicators; specifically including: Establish a decision matrix; in which, through the evaluation index set U = {u1,u2,,…u n } and evaluation set V′={v1,v2,…,v m }Construct the decision matrix X as: The decision matrix is ​​dimensionless; the numerical processing in the decision matrix is ​​divided into the following two types: For the benefit index, the larger the value, the better it is. Then: For cost indicators, the smaller the value, the better, then: For moderate indicators, the closer the value is to the middle value, the better it is. Then: A weighted decision matrix is ​​established; based on the dimensionless decision matrix X′, the importance of the evaluated object is weighted according to the evaluation index to construct the weighted decision Z: Among them, w i It is represented as the weight corresponding to the i-th evaluation index, w i ∈[0,1]; Determine the positive and negative ideal solutions; the positive ideal solution refers to the optimal solution achieved in the process of indicator convergence, and the negative ideal solution is the worst solution; it is expressed as follows: Among them, Z + It is represented as a positive ideal solution, Z - It is represented as a negative ideal solution; Calculate the distance between the evaluation object and the ideal solution; the Euclidean distance is used to represent the distance from the evaluation object to the ideal solution. The closer the distance, the better the evaluation. The Euclidean distance model is: Among them, D + , D j - Respectively represent the Euclidean distance from the i-th evaluation index to the positive and negative ideal solutions; Calculate the evaluation value; in the TOPSIS evaluation method, the evaluation value indicates the closeness between the evaluation scheme and the optimal scheme, and the evaluation value e i for: Among them, e i ∈[0,1], and e i The larger the value, the closer the evaluation object is to the optimal solution, and the better the evaluation object is.

7. The firearm assemblability evaluation method based on self-consistent fusion of qualitative and quantitative analysis according to claim 6, characterized in that: The grey correlation method is used to comprehensively evaluate the constructed assembly process evaluation indicators; specifically including: Determine the ideal solution X0 of the evaluation object and m evaluated solutions X i (i=1,2,…,m); where scheme X is represented by X=(x1,x2,…,x n ), x i Represents the evaluation attribute value of the i-th evaluation index; Solve for the correlation coefficient: Among them, η i (k) represents the correlation coefficient of evaluation scheme i at the kth evaluation index, Δ i (k) represents the difference between the reference scheme and the comparison scheme of scheme i in the k evaluation index, and μ is the resolution coefficient; Calculate the grey relational degree: Among them, η i Indicates the evaluated solution X i The correlation with the ideal solution X0. The larger the correlation value, the better the evaluated solution.

8. A firearm assemblability evaluation system based on self-consistent fusion of qualitative and quantitative analysis, characterized in that: The system comprises a processing unit configured to perform the following steps: Step 1: Construct assembly process evaluation indicators; the assembly process evaluation indicators include part-level evaluation indicators, component-level evaluation indicators, and product-level evaluation indicators: Among them, the part-level evaluation indicators include the quality of the part, the symmetry of the part, the size of the zero level, the matching reference of the part, and the number of adjacencies of the part; the component-level evaluation indicators include the aggregation of the assembly sequence, the number of transfers of the reference components, the parallelism of the assembly, the number of assembly redirections, the assembly working hours, the assembly space, the assembly stability, the assembly fastening type, and the total number of assembly operations; the product-level evaluation indicators include the number of dynamic interferences and the number of static interferences; Step 2: Use the assembly process evaluation method to comprehensively evaluate the constructed assembly process evaluation indicators to obtain the optimal assembly process solution; Among them, the assembly process evaluation method includes fuzzy comprehensive evaluation method, TOPSIS method and grey correlation method.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the firearm assemblability assessment method based on self-consistent fusion of qualitative and quantitative analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a firearm assemblability assessment method based on self-consistent fusion of qualitative and quantitative analysis as described in any one of claims 1 to 7 are implemented.

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