CN106563900A - Method for planning optimal welding track of automobile inner trims and exterior parts - Google Patents

Abstract

The invention discloses a method for optimal welding tracks of interior and exterior trim parts of automobiles, which can analyze the distribution of welding spots for different welding parts objects, and calculate and obtain corresponding welding tracks. The method includes: S1, a welding node information preprocessing step, S2, a welding spot sequence planning step based on a simulated annealing particle swarm algorithm, and S3, a step of converting the welding spot planning information into a motion trajectory of a welding robot. Automobile interior and exterior trim welding robots need to process on different curved surfaces. When performing such processing tasks, the welding points of the materials that need to be processed have a large number, the distribution of the welding points is relatively scattered, and the welded parts are scattered and relatively small in size. Many features. In the welding production line, the same welding task is performed repeatedly, and the large number of welding points leads to a large workload of the welding task. The method can effectively provide the path with the smallest moving distance in the case of multiple welding spots of different welding parts, and has better flexibility and rapidity.

Description

A method for optimal welding trajectory of automobile interior and exterior trim parts

technical field

The invention relates to the technical field of welding path planning, in particular to a method for optimal welding trajectory of interior and exterior trim parts of automobiles.

Background technique

Automobile interior and exterior trim welding robots need to process on different curved surfaces. Generally speaking, when performing such processing tasks, the material to be processed has a large number of solder joints, scattered distribution of solder joints, and scattered welded parts with a large number of sizes. In the welding production line, the same welding task is repeatedly performed, and the large number of welding points leads to a large workload of welding tasks. Therefore, it is necessary to plan reasonably the welding points in the welding tasks of automotive interior and exterior trims, which is very important for improving overall production efficiency and It has a positive effect on improving economic efficiency. The traditional method can basically complete the welding task of a small number of nodes, but for the welding task of a large number of nodes, the time consumed by the traditional method increases exponentially, and satisfactory results may not be obtained.

The essence of the basic particle swarm optimization algorithm is to use the optimal solution pbest in the iteration process of each particle in the population and the optimal solution gbest found in the whole process to calculate the position of the particle after the next iteration. The basic particle swarm optimization algorithm The convergence speed in the early stage of iteration is very fast. However, after several iterations, the particles of the population will tend to be unified and lose their diversity, which will slow down the convergence speed of the algorithm and cause premature phenomenon.

The simulated annealing algorithm is a heuristic algorithm based on the idea of the actual physical process. When a metal object is heated to a certain temperature, as the temperature gradually decreases, the molecules of the object will be in different states with a certain probability. The idea of simulated annealing was first proposed by Metropolis, and then successfully applied to combinatorial optimization problems. The main idea of the algorithm is to first generate an initial solution as the current solution, and then start jumping according to certain rules in the neighborhood of the current solution, and select a function according to a certain current temperature T as the probability to jump to the non-local optimal solution , so that the algorithm has the ability to jump out of the local optimal solution. Then continue to repeat the process with the jump to solution as the current solution. As T decreases, the probability of jumping to a non-local optimal solution also decreases. When T tends to 0, the algorithm will converge to the global optimal solution.

Contents of the invention

The purpose of the present invention is to solve the above-mentioned defects in the prior art, and provide a method for optimal welding trajectory of interior and exterior trim parts of automobiles.

The purpose of the present invention can be achieved by taking the following technical solutions:

A method for an optimal welding track of an interior and exterior trim of an automobile, the method comprising:

S1. Welding node preprocessing step, using image processing method for the required welding parts, and comparing with the preset welding point information C=(C ₁ , C ₂ ,...,C _N ) , where there are N welding nodes, which are C ₁ , C ₂ ,...,C _N ;

S2. The welding point sequence planning step. After obtaining the welding point information C of the welding task, use the simulated annealing particle swarm algorithm to calculate the welding point information C of the welding task as the input data. After the iterative operation of the algorithm, it is satisfied A welding track required;

S3. The step of transforming the welding point planning information into the motion trajectory of the welding robot. The obtained welding trajectory that meets the requirements is directly used in the welding process of the welding robot's interior and exterior trim parts, so that it can weld the nodes that need to be welded sequentially.

Further, the step S2 specifically includes:

S21. According to the simulated annealing particle swarm algorithm, set the size of the particle swarm to n, the initial temperature to T, the final value time to T_MIN, and the annealing rate to r, each particle randomly selects a node, and then selects the nearest node among the remaining nodes , until all nodes are traversed, the initial particle group {θ ₁ ,θ ₂ ,θ ₃ ......θ _n } is formed, and the initialized θ ₁ ,θ ₂ ,θ ₃ ......θ _{n is} used as the optimal solution pbest in the iterative process of each particle itself, and then the value with the shortest length is taken according to all current pbest _i as the optimal solution gbest found in the entire initial process;

S22. For the current path θ _i of each particle, generate a new traversal path according to the reverse method, and calculate the corresponding simulated annealing probability, repeat L times according to the setting, obtain the simulated annealing optimization path, and compare this path with pbest _i Compare and update pbest _i and gbest of each particle;

S23. Intersect the current path θ _i with the updated pbest _i and gbest respectively to obtain a new generation of current paths {θ ₁ , θ ₂ , θ ₃ ... θ _n }, and multiply the current temperature T by The annealing coefficient r is used to update the temperature, and then judge whether the exit condition is met. If the exit condition is not met, return to step S22; if the exit condition is met, return gbest and f(gbest) at this time and exit the operation, and return The best solder spot sequence θ _best calculated at this time.

Further, in the step S22, for the current path θ _i of each particle, a new traversal path is generated by reversing the middle or both ends.

Further, the exit condition is reaching a predetermined number of iterations or reaching an annealing temperature.

Further, the step S1 specifically includes:

S11. Determine whether each component in the workpiece has the correct number and position of solder joints;

S12. After identifying all the parts contained in the workpiece, the information is transmitted to the mechanical arm, and the correct welding head is called to weld the parts.

Further, in the step S11, the number and position of solder joints are detected by an image processing algorithm.

Further, in the step S3, the welding point sequence θ _best is used as a node of the welding trajectory of the welding robot, so that it automatically performs welding operations on the welding points in sequence.

Compared with the prior art, the present invention has the following advantages and effects:

1. The present invention applies an algorithm on the welding production line to calculate the welding trajectories of different components, so as to realize the flexibility of the welding production line.

2. The present invention applies an intelligent algorithm to welding path planning, which can quickly and effectively obtain an ordered path that meets requirements, and improves production efficiency.

3. The present invention adopts an automatic control method to automatically perform welding operations on the interior and exterior trim parts of automobiles, which reduces the difficulty of operation and can produce considerable economic benefits.

Description of drawings

Fig. 1 is a kind of method flow chart of the optimal welding trajectory of automobile interior and exterior parts disclosed by the present invention;

Fig. 2 is a schematic diagram of solder joints in a specific embodiment of a method for optimal welding trajectory of an automobile interior and exterior trim disclosed by the present invention;

Fig. 3 is a result diagram in a specific embodiment of a method for an optimal welding trajectory of an automobile interior and exterior trim disclosed by the present invention;

Fig. 4 is an algorithm iteration result diagram in a specific embodiment of a method for an optimal welding trajectory of an interior and exterior trim of an automobile disclosed by the present invention.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Embodiment one

This embodiment discloses a method for optimal welding trajectory of interior and exterior trim parts of automobiles. The method can analyze the distribution of welding spots for different welding parts objects, and calculate the corresponding welding trajectory. The flow chart is shown in Figure 1, and the method specifically includes the following steps:

S1. Welding node preprocessing step, using image processing method for the required welding parts, and comparing with the preset welding point information C=(C ₁ , C ₂ ,...,C _N ) , where there are N welding nodes, which are C ₁ , C ₂ ,...,C _N .

In a specific application, the step S1 specifically includes:

S11. Before the workpiece needs to be welded, it is necessary to first determine whether each component in the workpiece has the correct number and position of solder joints. Through the image processing algorithm, it can be detected whether the component has the correct solder joints;

S12. After identifying all the parts contained in the workpiece, the information can be transmitted to the robot arm, so as to call the correct welding head to weld the parts.

S2. The solder joint sequence planning step. After obtaining the solder joint information C of the welding task, the simulated annealing particle swarm algorithm is used to calculate the solder joint information C of the welding task as the input data. After the iterative operation of the algorithm, the requirements are obtained. a welding trajectory.

In a specific application, the step S2 specifically includes:

S21. According to the simulated annealing particle swarm algorithm, set the size of the particle swarm to be n, the initial temperature to be T, the final value time to be T_MIN, and the annealing rate to be r. Each particle adopts the method of randomly selecting a node, and then selecting the nearest node among the remaining nodes until all nodes are traversed to form an initial particle group {θ ₁ ,θ ₂ ,θ ₃ ......θ _n }. Then take the initialized θ ₁ , θ ₂ , θ ₃ ... θ _n as the optimal solution pbest in the iterative process of each particle itself, and then take the value with the shortest length according to all the current pbest _i as the initial total The optimal solution gbest found in the process.

S22. For the current path θ _i of each particle, a new traversal path is generated by reversing the middle or both ends, and the corresponding simulated annealing probability is calculated, and repeated L times according to the setting, to obtain the simulated annealing optimization path, and calculate the corresponding simulated annealing probability. The path is compared with pbest _i , updating pbest _i and gbest for each particle.

S23. Intersect the current path θ _i with the updated pbest _i and gbest respectively to obtain a new generation of current paths {θ ₁ , θ ₂ , θ ₃ ... θ _n }. The temperature is updated by multiplying the current temperature T by the annealing coefficient r. Then judge whether to meet the exit condition, if not meet the exit condition, then return to step S22; if meet the exit condition, then return gbest and f(gbest) at this time and exit the calculation, and return the best solder point calculated at this time Order θ _best .

Wherein, the exit condition is reaching a predetermined number of iterations or reaching an annealing temperature.

S3. The welding point planning information is converted into the motion trajectory step of the welding robot. The obtained welding trajectory that meets the requirements is directly used in the welding process of the welding robot's internal and external trim parts, so that it can weld the nodes that need to be welded sequentially, and the completion is completed. After all the nodes that need to be welded, complete this task. The overall flow chart is shown in Figure 1.

In the specific application, this step uses the θ _best obtained by the intelligent algorithm as the node of the welding trajectory of the welding robot, so that it can automatically perform welding operations on the welding points in sequence, thereby quickly completing the process of sequentially welding the interior and exterior parts of the car.

Embodiment two

This embodiment specifically discloses a method for optimal trajectory welding of interior and exterior trim parts of automobiles, which can analyze the distribution of welding spots for different welding parts objects, and calculate the corresponding welding trajectory. The flow chart is shown in Figure 1, and the specific steps are as follows:

S1. Welding joint preprocessing step, the representation of welding joint information is obtained from the production process, and compared with the parts actually transmitted to the welding station. If the conditions are met, it means that the parts are correct and the actual welding work can be carried out. The welding node of this embodiment is shown in FIG. 2 .

S2, the welding spot sequence planning step, after obtaining the welding task welding spot information C, use the simulated annealing particle swarm algorithm to calculate C as the input data, and after the iterative calculation of the algorithm, obtain a welding trajectory that meets the requirements;

S21. According to the simulated annealing particle swarm algorithm, set the size of the particle swarm to 200, the initial temperature to 10000, the final value time to 1, and the annealing rate to 0.99. Each particle adopts the method of randomly selecting a node, and then selecting the nearest node among the remaining nodes until all nodes are traversed to form an initial particle group {θ ₁ ,θ ₂ ,θ ₃ ......θ _n }. Then take the initialized θ ₁ , θ ₂ , θ ₃ ... θ _n as the pbest _i of each particle, and then take the value with the shortest length according to all the current pbest _i as the initial gbest.

S22. For the current path θ _i of each particle, a new traversal path is generated by reversing the middle or both ends, and the corresponding simulated annealing probability is calculated, and repeated 30 times according to the setting, to obtain the simulated annealing optimization path, and calculate the corresponding simulated annealing probability. The path is compared with pbest _i , updating pbest _i and gbest for each particle.

S23. Intersect the current path θ _i with the updated pbest _i and gbest respectively to obtain a new generation of current paths {θ ₁ , θ ₂ , θ ₃ ... θ _n }. The temperature is updated by multiplying the current temperature T by the annealing coefficient r. Then judge whether the exit condition is satisfied, if the exit condition is not satisfied, then return to the second step; if the exit condition is satisfied, then return gbest and f(gbest) at this time and exit the operation, and return the best weld calculated at this time Point order θ _best . The calculated effect is shown in Figure 3, and the effect of the number of algorithm iterations is shown in Figure 4.

S3. The welding point planning information is transformed into the motion trajectory step of the welding robot. For a set of welding trajectory that meets the requirements obtained, it is directly used in the welding process of the welding robot's internal and external trim parts, so that it can weld the nodes that need to be welded sequentially. After completing all the nodes that need to be welded, the task is completed.

In summary, the simulated annealing particle swarm algorithm adopted in the present invention combines the advantages of the simulated annealing algorithm jumping out of the local optimum and the advantages of the particle swarm algorithm being simple and capable of global search, and the improved simulated annealing particle swarm algorithm is applied to the welding trajectory planning.

The above-mentioned embodiment is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited by the above-mentioned embodiment, and any other changes, modifications, substitutions, combinations, Simplifications should be equivalent replacement methods, and all are included in the protection scope of the present invention.

Claims (7)

1. the method for a kind of inner and outer decorative parts of automobile optimum welding track, it is characterised in that methods described includes：

S1, welding node pre-treatment step, to the part of required welding using image procossing mode, and with preset Solder joint information C=(C₁, C₂,......,C_N) compare, wherein there is N number of welding node, it is followed successively by C₁, C₂,......,C_N；

S2, solder joint order planning step, after solder joint information C for obtaining weld task, using simulated annealing particle cluster algorithm, Calculated solder joint information C of weld task as the data of input, after the interative computation of algorithm, being met will The welding track asked；

S3, solder joint planning information are converted into the movement locus step of welding robot, for the welding that the satisfaction asked for is required Track, in being directly used in welding robot inner and outer decorative parts welding process so as to which the node to needing welding is welded successively.

2. a kind of method of inner and outer decorative parts of automobile optimum welding track according to claim 1, it is characterised in that the step Rapid S2 is specifically included：

S21, according to simulated annealing particle cluster algorithm, the size for arranging population is n, and initial temperature is T, and the final value time is T_ MIN, annealing rate is r, and each particle is using one node of random selection, then chooses the nearest node in remaining node, until Method till the whole nodes of traversal, constitutes primary group { θ₁,θ₂,θ₃......θ_n, with initialized θ₁,θ₂, θ₃......θ_nAs optimal solution pbest in each particle itself iterative process, further according to current whole pbest_iTake length Most short value is used as optimal solution gbest found in initial all processes；

S22, for the current path θ of each particle_i, new traverse path is produced according to reversal method, and calculate corresponding mould Intend annealing probability, repeat L time according to setting, obtain simulated annealing optimization path, and by this path and pbest_iIt is compared, more The pbest of new each particle_iAnd gbest；

S23, by current path θ_iRespectively with renewal after pbest_iIntersected with gbest, obtained the current path of a new generation {θ₁,θ₂,θ₃......θ_n, Current Temperatures T is multiplied by into annealing coefficient r, temperature is updated with this, then judge whether that satisfaction is exited Condition, if being unsatisfactory for exit criteria, return to step S22；If meeting exit criteria, gbest now and f is returned (gbest) and exit computing, and return the best solder joint order θ for now calculating_best。

3. the method for a kind of inner and outer decorative parts of automobile optimum welding track according to claim 2, it is characterised in that

For the current path θ of each particle in step S22_i, according to reversing, the method for middle or two ends is new to produce Traverse path.

4. the method for a kind of inner and outer decorative parts of automobile optimum welding track according to claim 2, it is characterised in that described to move back It is to reach predetermined iterationses or reach annealing temperature to go out condition.

5. a kind of method of inner and outer decorative parts of automobile optimum welding track according to claim 1, it is characterised in that the step Rapid S1 is specifically included：

S11, determine in workpiece whether all parts have correct solder joint number and location；

After S12, all parts for identifying included in workpiece, mechanical arm is transferred information to, call correct soldering tip to portion Part is welded.

6. a kind of method of inner and outer decorative parts of automobile optimum welding track according to claim 5, it is characterised in that the step Solder joint number and location are detected by image processing algorithm in rapid S11.

7. the method for a kind of inner and outer decorative parts of automobile optimum welding track according to claim 2, it is characterised in that

By the solder joint order θ in step S3_bestAs the node of the welding track of welding robot, so that its is automatic Successively butt welding point is subjected on ground.