Steel-aluminum mixed connection joint intelligent nail distribution system considering cost and efficiency

Abstract

The invention discloses a cost and efficiency-considered intelligent nail distribution system for a steel-aluminum mixed connection joint, which comprises the following steps: determining input targets which are divided into a fuzzy use scene, a specific strength ST, a cost PT or a preferred connection form; discussing a single connection mode, including type selection and first type selection and then optimization; a database module is established based on a multi-joint mechanical property prediction model, and a strength factor Si, a cost factor Pi, a lightweight factor Li and a sealing and NVH factor Ni are introduced. According to the method, flexible upgrading can be achieved in the target input link, namely, multiple quantifiable indexes X are added, corresponding index factors Xi are added and entered in the subsequent optimization link, and the target strength is optimized according to the number of joints, the cost, the strength, the light weight, the sealing performance and the NVH performance. Therefore, the optimal system can meet more working condition requirements.

Description

Technical Field

[0001] The present invention belongs to the field of new energy vehicles, and specifically is an intelligent nail placement system for steel-aluminum hybrid connection joints that takes cost and efficiency into consideration. Background Art

[0002] As people pay more and more attention to energy conservation and environmental protection, the government is actively encouraging the production of green and environmentally friendly cars. New energy vehicles have stricter environmental standards than traditional vehicles, focusing on energy conservation, emission reduction and extended driving range. Lightweighting of the vehicle body is an important way to achieve the above requirements. This lightweight technology involves intelligent structural design and material application. With the extensive use of various new materials on the vehicle body, the most representative of which is the application of aluminum alloy in components such as frames and suspension devices. However, because traditional welding cannot achieve efficient connection of dissimilar materials, the connection of aluminum and high-strength steel requires innovative methods. At present, technologies such as self-piercing riveting (SPR), hot-melt self-tapping (FDS), Cl i nch and bonding are gradually being used on the vehicle body. These technologies are not only efficient and cost-effective, but also more environmentally friendly, and have had a profound impact on the future of the automotive industry.

[0003] However, most of the current research only focuses on the mechanical properties and fatigue properties of a single mechanical connection joint, but there is little research on the connection performance of multiple joints, especially the research on the working conditions of multiple mechanical connections of large parts. A set of mechanical connection types that can be selected according to specific component areas, as well as the rationalization and efficiency of the connection point layout are the key to promoting cost reduction and efficiency improvement in the automotive industry.

[0004] The intelligent nail placement system for steel-aluminum hybrid connection joints can quickly respond to designers' cost and efficiency design requirements, and use machine learning technology to quickly identify nail placement solutions that match the requirements, greatly shortening the time for selecting and arranging connection points and improving the efficiency of vehicle body-in-white design. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an intelligent nail arrangement system for steel-aluminum hybrid connection joints taking cost and efficiency into consideration.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency, comprising the following operating steps:

[0007] S1: Determine the input target, which is divided into: fuzzy usage scenarios, specific intensity S T , cost P T or preferred form of connection;

[0008] S2: Discuss single connection mode, divided into selection and selection followed by optimization;

[0009] S3: Self-optimize the solution given in step S2: Establish a database module based on the multi-joint mechanical property prediction model and introduce the strength factor S i , cost factor P i , lightweight factor L i , sealing and NVH factor N i . Get the target strength and optimize the layout according to the number of joints, cost, strength, lightness, sealing and NVH performance.

[0010] As a further description of the above technical solution: in S2, based on the performance database of different types of mechanical connection joints, the database is traversed to select solutions. The number of solutions is not unique because it is a single type of connection method. The output result of the selection link is only a certain connection method.

[0011] As a further description of the above technical solution: In S2, the optimization principle of the first selection is:

[0012] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T The biggest plan;

[0013] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T And plan P 0 The smallest solution;

[0014] If the target parameter of the first step has a specific value, in S 0 ≥S T Under the premise of achieving P 0 ≥P T The output is SPR (self-filling riveting), FDS (hot melt self-tapping), Cl i nch (pressure riveting), Spot (spot welding).

[0015] As a further description of the above technical solution: the self-optimization method for step S2 is to optimize the FDS multi-connection point layout of the connection area between the vehicle sill beam and the door B-pillar.

[0016] As a further description of the above technical solution: the FDS multi-connection point layout optimization of the connection area between the car sill beam and the door B-pillar includes: the cost factor is determined by the number of connection points, where k is the price coefficient, which is related to the connection method. The price coefficient has a corresponding coefficient database, which is automatically retrieved during the optimization process, and n is the number of connection points.

[0017] As a further description of the above technical solution: the cost factor is expressed as:

[0018]

[0019] Among them: there are n types of connection forms, and the price of each connection point is p 1 , p 2 ,…,p n , and the number of each connection point is q 1 ,q 2 , …, q n ,

[0020] P i =αP 0；

[0021] Where: α cost gain parameter, P 0 is the base cost,

[0022] Strength is obtained from the factor mechanical property prediction model, and the strength factor S i The optimization relationship

[0023] S i =βS 0；

[0024] Where: β is the intensity gain parameter, S 0 is the baseline strength;

[0025] As a further description of the above technical solution: Lightweight factor W i Optimizing the relationship, there are n types of connections, and the weight of each connection point is w 1 , w 2 ,…,w n , and the number of each connection point is q 1 ,q 2 , …, q n，

[0026]

[0027] Where: K is the lightweight conversion coefficient, is a constant, γ is the lightweight gain parameter, W 0 As the benchmark lightweight level,

[0028] Sealing and NVH factor N i Optimizing relationship, N i =δN 0；

[0029] Where: δ is the sealing and NVH gain parameter, N 0 It is the benchmark sealing and NVH level;

[0030] As a further description of the above technical solution: the mapping relationship between the target optimization function and each optimization parameter is:

[0031] T 0 =n 1 P 0 +n 2 S 0 +n 3 W 0 +n 4 N 0 +m 0；

[0032] T 0 is the initial factor of the scheme,

[0033] T i =φ(n 1 αP 0 +n 2 βS 0 +n 3 γW 0 +n 4 δN 0 +m 0 );

[0034] Among them, T t is the target factor of the scheme, T i is the optimized solution factor; T 0 is the initial factor of the scheme;

[0035] n 1 ,n 2 ,n 3 ,n 4 are the weight coefficients of each benchmark factor respectively;

[0036] m 0 is a constant;

[0037] α is the optimization coefficient.

[0038] As a further description of the above technical solution: After each optimization, the system enters the evaluation and threshold judgment module, and the judgment basis is: if T i ≥T t , then enter the output module and output the solution after this iteration; if T i <T t , then continue to iterate.

[0039] As a further description of the above technical solution: If the first step is to prioritize strength, then according to the working conditions, select the solution strength S i ≥S T The largest solution; just enter the target strength S T, then according to the mechanical properties prediction model and strength factor S i The optimization relationship is optimized iteratively. When S i ≥S T Stop optimization and output current data.

[0040] Due to the adoption of the above technical solution, the beneficial effects achieved by the present invention are:

[0041] (1) The present invention can achieve flexible upgrading in the input target link, that is, adding multiple quantifiable indicators X, that is, adding corresponding indicator factors X in the subsequent optimization link. i , so that the optimal system can meet the needs of more working conditions;

[0042] (2) Through an iterative optimization process, the system of the present invention will evaluate all possible scheme combinations to determine the optimal joint arrangement scheme;

[0043] (3) The present invention involves continuous iteration of the objective function T0. Each iteration adjusts the value of the objective factor Ti according to the weight coefficients n1 to n4 and the constant m0. The optimization coefficient α is used to control the convergence speed and accuracy of the iterative process to ensure that the final output solution combination achieves the best balance in terms of cost, strength, lightweight, sealing and NVH performance;

[0044] (4) Through the above-mentioned method, the system of the present invention can provide customized intelligent nailing solutions for different usage scenarios and target parameters, thereby achieving efficient, economical and reliable connection of steel-aluminum hybrid connection joints. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is an implementation flow chart of an intelligent nailing system for steel-aluminum hybrid connection joints proposed by the present invention that takes cost and efficiency into consideration; Figure 2 This is a multi-joint mechanical performance prediction model diagram of an intelligent nailing system for steel-aluminum hybrid connection joints that takes cost and efficiency into consideration, as proposed by the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0047] Reference Figure 1 The present invention provides the following embodiments to describe the present invention in detail:

[0048] Example 1

[0049] In order to solve the problems existing in the prior art, the present invention proposes an intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency, and the steps are as follows:

[0050] 1. First, enter the goal, which can be a vague usage scenario or a specific intensity S T , cost P T and preferred form of connection.

[0051] 2. This type of method first discusses the selection and optimization of a single connection method. First, based on the performance database of different types of mechanical connection joints, the database is traversed to select solutions. The number of solutions is not unique, but considering that it is a single type of connection method, the output result of the selection link is only a certain connection method.

[0052] Therefore, the selection principles are as follows:

[0053] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T The biggest plan;

[0054] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T And plan P 0 The smallest solution;

[0055] If the target parameter of the first step has a specific value, in S 0 ≥S T Under the premise of achieving P 0 ≥P T . The output is SPR (self-filling riveting), FDS (hot melt self-tapping), C linch (pressure riveting), Spot (spot welding).

[0056] 3. Select the best solution again and perform self-optimization for the solutions given in step 2. Establish a database module based on the multi-joint (joint form specific) mechanical property prediction model and introduce the strength factor S i , cost factor P i , lightweight factor L i , sealing and NVH factor N i The mechanical property prediction model here is based on a machine learning model. By inputting the position of each joint, the target strength can be obtained. However, the number of joints affects the cost, as well as the strength, lightweight, sealing and NVH performance. Therefore, the above factors are comprehensively considered to optimize the layout.

[0057] Example 2

[0058] An optimization scheme for the FDS multi-connection point arrangement of the connection area between the vehicle sill beam and the door B-pillar is proposed.

[0059] The cost factor is determined by the number of connection points, where k is the price coefficient (related to the connection method, the price coefficient has a corresponding coefficient database, which is automatically retrieved during the optimization process), and n is the number of connection points. In addition, the cost factor can also be expressed as:

[0060] P i = kn (k is the price coefficient, n is the number of connection points), P i =φ 1 P 0

[0061] φ 1 Cost-gain parameter, P 0 is the base cost,

[0062] Strength is obtained from the factor mechanical property prediction model, and the strength factor S i The optimization relationship

[0063] S i =φ 2 S 0；

[0064] Φ 2 Cost-gain parameter, S 0 is the baseline strength;

[0065] Lightweight factor L i Optimizing relationship, L i =φ 3 L 0，

[0066] Φ 3 Cost-gain parameter, L 0 Base lightweight level

[0067] Sealing and NVH factor N i Optimizing relationship, N i =φ 4 N 0，

[0068] Φ 4 Cost-benefit parameter, N 0 Benchmark sealing and NVH levels

[0069] The mapping relationship between the target optimization function and each optimization parameter is:

[0070] T 0 =n 1 P 0 +n 2 S 0 +n 3 L0 +n 4 N 0 +m 0；

[0071] T i is the target factor of the scheme, T 0 is the initial factor of the scheme,

[0072] T i =α(n 1 φ 1 P 0 +n 2 φ 2 S 0 +n 3 φ 3 L 0 +n 4 φ 4 N 0 +m 0 )

[0073] T i is the target factor of the scheme; T 0 is the initial factor of the scheme;

[0074] n 1 ,n 2 ,n 3 ,n 4 are the weight coefficients of each benchmark factor respectively;

[0075] m 0 is a constant;

[0076] α is the optimization coefficient.

[0077] Final T i Perform iterative optimization and output the solution combination corresponding to the optimal parameters.

[0078] This method can achieve flexible upgrade in the input target stage, that is, add multiple quantifiable indicators X, that is, add the corresponding indicator factor X in the subsequent optimization stage. i , thus being able to select the optimal system that meets the requirements of more working conditions.

[0079] Example 3

[0080] An intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency includes the following operation steps:

[0081] S1: Determine the input target, which is divided into: fuzzy usage scenarios, specific intensity S T , cost P T or preferred form of connection;

[0082] S2: Discuss single connection mode, divided into selection and selection followed by optimization;

[0083] S3: Self-optimize the solution given in step S2: Establish a database module based on the multi-joint mechanical property prediction model and introduce the strength factor S i , cost factor P i , lightweight factor L i , sealing and NVH factor N i . Get the target strength and optimize the layout according to the number of joints, cost, strength, lightness, sealing and NVH performance.

[0084] In S2, based on the performance database of different types of mechanical connection joints, the database is traversed to select solutions. The number of solutions is not unique because it is a single type of connection method. The output result of the selection process is only a certain connection method.

[0085] In S2, the optimization principle for the first selection is:

[0086] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T The biggest plan;

[0087] If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T And plan P 0 The smallest solution;

[0088] If the target parameter of the first step has a specific value, in S 0 ≥S T Under the premise of achieving P 0 ≥P T The output is SPR (self-filling riveting), FDS (hot melt self-tapping), Cl i nch (pressure riveting), Spot (spot welding).

[0089] This step can be optimized according to the needs, which increases the flexible design of the system. The non-single-target optimization is more suitable for today's production and design needs.

[0090] The self-optimization method for step S2 is the FDS multi-connection point layout optimization of the connection area between the vehicle sill beam and the door B-pillar.

[0091] The FDS multi-connection point layout optimization of the connection area between the car sill beam and the door B-pillar includes: the cost factor is determined by the number of connection points, where k is the price coefficient, which is related to the connection method. The price coefficient has a corresponding coefficient database, which is automatically retrieved during the optimization process, and n is the number of connection points.

[0092] The cost factor is expressed as:

[0093]

[0094] Among them: there are n types of connection forms, and the price of each connection point is p 1 , p 2 ,…,p n , and the number of each connection point is q 1 ,q 2 , …, q n ,

[0095] P i =αP 0；

[0096] Where: α cost gain parameter, P 0 is the base cost,

[0097] Strength is obtained from the factor mechanical property prediction model, and the strength factor S i The optimization relationship

[0098] S i =βS 0；

[0099] Where: β is the intensity gain parameter, S 0 is the baseline strength;

[0100] Lightweight factor W i Optimizing the relationship, there are n types of connections, and the weight of each connection point is w 1 , w 2 ,…,w n , and the number of each connection point is q 1 ,q 2 , …, q n

[0101]

[0102] Where: K is the lightweight conversion coefficient (constant), γ is the lightweight gain parameter, W 0 As the benchmark lightweight level,

[0103] Sealing and NVH factor N i Optimizing relationship, N i =δN 0；

[0104] Where: δ is the sealing and NVH gain parameter, N 0 It is the benchmark sealing and NVH level;

[0105] The mapping relationship between the target optimization function and each optimization parameter is:

[0106] T 0 =n 1 P 0 +n 2 S 0 +n 3 W 0 +n 4 N 0 +m 0；

[0107] T 0 is the initial factor of the scheme,

[0108] T i =φ(n 1 αP 0 +n 2 βS 0 +n 3 γW 0 +n 4 δN 0 +m 0 );

[0109] Among them, T t is the target factor of the scheme, T i is the optimized solution factor; T 0 is the initial factor of the scheme;

[0110] n 1 ,n 2 ,n 3 ,n 4 are the weight coefficients of each benchmark factor respectively;

[0111] m 0 is a constant;

[0112] α is the optimization coefficient. After each optimization, the system enters the evaluation and threshold judgment module. The judgment basis is: if T i ≥T t , then enter the output module and output the solution after this iteration; if T i <T t , then continue to iterate. Cost, strength, lightweight level, sealing and NVH level are all related to the type, number and layout of the connection joints. Cost and lightweight level are strictly linear functions of the type and number of joints, so there is no need for a separate prediction module. The strength, sealing and NVH level of the multi-joint combination are not linearly related to the above factors, so the prediction module is introduced to predict the strength, sealing and NVH level of different schemes. The prediction results are used for evaluation and threshold judgment of each iteration.

[0113] If the first step is to prioritize strength, then select the solution strength S according to the working conditions.i ≥S T The largest solution; just enter the target strength S T , then according to the mechanical properties prediction model and strength factor S i The optimization relationship is optimized iteratively. When S i ≥S T Stop optimization and output the current solution.

[0114] Through the above embodiments and methods, the present invention realizes structural optimization of the connection area between the threshold beam and the door B-pillar, improves the safety performance and durability of the car, and by adjusting the number and layout of connection points, not only reduces the manufacturing cost, but also improves the overall performance of the vehicle. The implementation of this solution provides a new idea for automobile design, makes automobile structural design more scientific and reasonable, and meets the comprehensive requirements of the modern automobile industry for lightweight, cost control and performance improvement.

[0115] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency, characterized in that: The steps include: S1: Determine the input target, which is divided into: fuzzy usage scenarios, specific intensity S T , cost P T or preferred form of connection; S2: Discuss single connection mode, divided into selection and selection followed by optimization; S3: Self-optimize the solution given in step S2: Establish a database module based on the multi-joint mechanical property prediction model and introduce the strength factor S i , cost factor P i , lightweight factor L i , sealing and NVH factor N i . Get the target strength and optimize the layout according to the number of joints, cost, strength, lightness, sealing and NVH performance.

2. According to claim 1, a cost-effective and efficient steel-aluminum hybrid connection joint intelligent nailing system is characterized in that: In S2, based on the performance database of different types of mechanical connection joints, the database is traversed to select solutions. The number of solutions is not unique because it is a single type of connection method. The output result of the selection process is only a certain connection method.

3. According to claim 2, a cost- and efficiency-considered intelligent nailing system for steel-aluminum hybrid connection joints is characterized in that: In S2, the optimization principle for the first selection is: If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T The biggest plan; If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T And the solution with the smallest P0; If the target parameter of the first step has a specific value, then S0≥S T Under the premise of achieving P0≥P T The output is SPR (self-filling riveting), FDS (hot melt self-tapping), Cl inch (pressure riveting), Spot (spot welding).

4. According to claim 1, a cost-effective and efficient steel-aluminum hybrid connection joint intelligent nailing system is characterized in that: The self-optimization method for step S2 is the FDS multi-connection point layout optimization of the connection area between the vehicle sill beam and the door B-pillar.

5. According to claim 4, a cost-effective and efficient steel-aluminum hybrid connection joint intelligent nailing system is characterized in that: The FDS multi-connection point layout optimization of the connection area between the car sill beam and the door B-pillar includes: the cost factor is determined by the number of connection points, where k is the price coefficient, which is related to the connection method. The price coefficient has a corresponding coefficient database, which is automatically retrieved during the optimization process, and n is the number of connection points.

6. According to claim 5, a cost- and efficiency-considered intelligent nailing system for steel-aluminum hybrid connection joints is characterized in that: The cost factor is expressed as: Among them: there are n types of connection forms, and the price of each connection point is p1, p2, ..., p n , and the number of each connection point is q1, q2, ..., q n , P i =αP 0； Among them: α is the cost gain parameter, P0 is the base cost, Strength is obtained from the factor mechanical property prediction model, and the strength factor S i The optimization relationship S i =βS 0； Where: β is the intensity gain parameter, S0 is the reference intensity.

7. According to claim 6, a cost-effective and efficient steel-aluminum hybrid connection joint intelligent nailing system is characterized in that: Lightweight factor W i Optimize the relationship. There are n types of connections. The weight of each connection point is w1, w2, ..., w n , and the number of each connection point is q1, q2, ..., q n， Where: K is the lightweight conversion coefficient, is a constant, γ is the lightweight gain parameter, W0 is the benchmark lightweight level, Sealing and NVH factor N i Optimizing relationship, N i =δN 0； Where: δ is the sealing and NVH gain parameter, and N0 is the reference sealing and NVH level.

8. The intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency according to claim 7 is characterized in that: The mapping relationship between the target optimization function and each optimization parameter is: T0=n1P0+n2S0+n3W0+n4N0+m 0； T0 is the initial factor of the scheme, T i =φ(n1αP0+n2βS0+n3γW0+n4δN0+m0); Among them, T t is the target factor of the scheme, T i is the optimized scheme factor; T0 is the initial scheme factor; n1, n2, n3, n4 are the weight coefficients of each benchmark factor; m0 is a constant; α is the optimization coefficient.

9. The intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency according to claim 8 is characterized in that: After each optimization, the system enters the evaluation and threshold judgment module. The judgment basis is: if T i ≥T t , then enter the output module and output the solution after this iteration; if T i <T t , then continue to iterate.

10. The intelligent nailing system for steel-aluminum hybrid connection joints considering cost and efficiency according to claim 9 is characterized in that: If the first step is to prioritize strength, then select the solution strength S according to the working conditions. i ≥S T The largest solution; just enter the target intensity S T , then according to the mechanical properties prediction model and strength factor S i The optimization relationship is optimized iteratively. When S i ≥S T Stop optimization and output current data.

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