CN104462705A - Fixture assembly sequence planning method based on particle swarm optimization algorithm - Google Patents
Fixture assembly sequence planning method based on particle swarm optimization algorithm Download PDFInfo
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Abstract
Provided is a fixture assembly sequence planning method based on a particle swarm optimization algorithm. The method includes the steps that information in a connection relation attribute list in a hierarchical assembly model orientated to assembly planning is utilized to generate an initial connection relation graph of an assembly body; a connection relation graph corresponding to the whole assembly body and a connection relation graph corresponding to each sub-assembly body are constructed according to hierarchy inclusion relations among components and parts in the connection relation graph and an assembly hierarchy relation ship; tools needed in an assembling process are determined according to the assembly connection relation graph, and an assembly tool constraint matrix M is constructed; time required by each procedure in the assembling process is determined, and finally a time constraint matrix T is generated; a feasible assembly sequence plan is generated by the adoption of the particle swarm optimization algorithm; the correctness of a generated assembly sequence is judged; an overall assembly sequence plan result represented by a Gantt diagram is output. According to the method, products designed under the conditions that production technologies and use reliability and safety are satisfied, the cost is reduced as much as possible and errors are the smallest are good in performance.
Description
Technical field
The present invention relates to Fixture assembly Sequence Planning field, be specifically related to a kind of Fixture assembly Sequence Planning method based on particle swarm optimization.
Background technology
In digital product assemble planning, assembly sequences generation is guaranteed to generate feasible Assembly sequences, different Assembly sequences directly can affect the selection of assembly tool fixture, assembling process efficiency and assembly cost, for the assembling of complex product, preferred plan can be found from up to ten thousand Assembly sequences, simultaneously at Design Stage, according to assembly sequence-planning feedack, help product designer Curve guide impeller.Assembly sequences determines the complicacy of assembling process and the key factor of reliability, utilize the digital information in product design, the generation of Product Assembly sequence is carried out in computing machine, selection is applicable to the good Assembly sequences of assembly environment, for improvement product design, guarantees that the construction cycle of assembly feasibility, raising efficiency of assembling, reduction assembly cost, shortening product has very important significance.Assembly sequences generation becomes an important component part of a vital task in manufacturing automation process and CIMS research gradually.
Although particle swarm optimization is successfully applied to engineering field, but how to apply particle swarm optimization and effectively solve assembly sequence-planning problem, and in particle swarm optimization, what impact the change of parameters has all do not have document to discuss further on assembly sequence-planning.Particle swarm optimization is applied to the continuity that the main difficulty solving assembly sequence-planning problem is particle swarm optimization by present stage.The particle swarm optimization of standard can not directly be used for solving discrete assembly sequence-planning problem.But in the Discrete Particle Swarm algorithm improved on standard PSO basis, each particle can represent with a discrete Assembly sequences, determines position and the speed of particulate in discrete space.Particle Swarm Optimization is as a kind of general optimized algorithm, poor to the local search ability of particular problem, easily be absorbed in local minimizers number, this point is similar with genetic algorithm, therefore, searching method based on simulated annealing is combined with particle swarm optimization, is conducive to the global optimization quality and the efficiency that improve particle swarm optimization.Particle swarm optimization is the initial solution that simulated anneal algritym provides that a group has good quality and dispersion degree, then mechanism of Simulated Annealing is adopted to carry out local neighborhood search to these solutions, be conducive to the local improving to excellent solution, give a kind of probability kick of algorithm ability simultaneously.Therefore this paper introduces assemble planning field particle swarm optimization, by rational operational design and improvement, to provide a kind of newly, effective assemble planning method, has important Research Significance.
Summary of the invention
In order to overcome the shortcoming of above-mentioned prior art, the object of the present invention is to provide a kind of Fixture assembly Sequence Planning method based on particle swarm optimization, in the manufacturability of satisfied production, the reliability of use and security, and designed product under the condition that expense is economized most, error is minimum, is made to have good performance.
In order to achieve the above object, the technical scheme that the present invention takes is:
Based on a Fixture assembly Sequence Planning method for particle swarm optimization, comprise the following steps:
The first step, utilizes the information in the hierarchical assembly model of assemble planning in connecting relation attribute list to generate the initial connecting relation figure of assembly;
Second step, according to the level relation of inclusion in connecting relation figure and assembling level relational tree between each parts, constructs the connecting relation figure of the correspondence of whole assembly and each sub-assemblies, determines the position relationship between each part, interference relation;
3rd step, according to outfit in assembling connecting relation figure determination assembling process, structure assembly tool constraint matrix M;
4th step, utilizes assembly knowledge, determines each operation required time of assembling process, final rise time constraint matrix T;
5th step, application particle swarm optimization generates feasible assembly sequence-planning;
6th step, utilizes assembly knowledge, and the interference matrix generated by the position relationship between each part, annexation, judges the Assembly sequences correctness generated;
7th step, the overall assembly sequence-planning result that output represents with Gantt chart.
Beneficial effect of the present invention is:
Particle swarm optimization is not use evolutionary operator for individuality relative to traditional optimized algorithm, but each individuality is regarded as the particulate not having weight and volume n one of tieing up in search volume, and the flying experience of reference group and the flying experience of particle itself are flown with certain speed in search volume.Particle swarm optimization remains the global search strategy based on population as a kind of effective parallel search algorithm, do not need the characteristic information of Dependence Problem itself, adopt simple speed displacement evolution Model, avoid complicated genetic manipulation, and only have a small amount of parameter to need adjustment, its distinctive memory capability makes it dynamically can follow the tracks of current search situation in order to adjust its search strategy simultaneously, therefore there is stronger global convergence ability and robustness, be very suitable for solving complicated optimization problem.Concrete has following advantage by particle swarm optimization solution assembly sequence optimization problem:
1) insensitive for problem characteristic, do not require that objective function and constraint function are resolved, Seeking Truth continuous print or high-order can be not micro-.
2) be a kind of random iterative algorithm.
3) searching process is the iterative process of Evolution of Population process instead of a point, and a population comprises multiple individuality, and this makes algorithm can find globally optimal solution with larger probability.
4) be easy to perform and use.
5) insensitive to choosing of initial point.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the entirety of assembly sequences generation.
Fig. 2 is hierarchical assembly model hum pattern.
Fig. 3 is particle swarm optimization process flow diagram.
Fig. 4 is the Gantt chart that application particle swarm optimization carries out one of them optimum solution of assembly sequence-planning.
Embodiment
Below in conjunction with accompanying drawing and example, the present invention is described in detail.
Instantiation is as follows, supposes that a certain clamp body completes time needed for each assembling process and required assembly tool, and this program is optimized this problem for optimization aim so that the completion date of all machines is the shortest, and its Optimized model is
In formula: M
jfor optimum Assembly sequences scheme; T
efor the process finishing time of the every platform machine of each assembling scheme.
With reference to Fig. 1, a kind of Fixture assembly Sequence Planning method based on particle swarm optimization, comprises the following steps: comprise the following steps:
The first step, restriction relation inherent, implicit between each part in the individual assembly comprised in utilizing hierarchical assembly model hum pattern as shown in Figure 2 to represent, adopts a kind of hierarchical assembly model towards assemble planning to carry out assembly information required in complete expression assembly sequences generation.Assembly information is divided into two-layer storage by the hierarchical assembly model towards assemble planning, and low layer stores concrete part geometry shape and positional information, and abstract contact and connecting relation information between high-rise storage part, overall framework as shown in Figure 2.Utilize the information in hierarchical assembly model in connecting relation attribute list to generate the initial connecting relation figure of assembly;
Second step, according to the level relation of inclusion in connecting relation figure and assembling level relational tree between each parts, constructs the connecting relation figure of the correspondence of whole assembly and each sub-assemblies, determines the position relationship between each part, interference relation;
3rd step, according to outfit in assembling connecting relation figure determination assembling process, structure assembly tool constraint matrix
;
4th step, utilizes assembly knowledge, determines each operation required time of assembling process, final rise time constraint matrix
;
5th step, as shown in Figure 3, application particle swarm optimization generates assembly sequence-planning;
6th step, utilizes assembly knowledge, and the interference matrix generated by the position relationship between each part, annexation, judges the Assembly sequences correctness generated, as clamp body in this example is made up of 6 parts, then can generate the interference matrix of a group 6 × 6;
As shown in Figure 4, the overall assembly sequence-planning result that output represents with Gantt chart, wherein horizontal ordinate represents required time, and ordinate represents service machine.Assembling required time is as seen from Figure 4 55s, draws the shortest time this time needed for assembling thus.Owing to there is 0 to 1 random function be evenly distributed in computing formula, each run acquired results can not be identical, obtain reliable data, repeatedly need run and get minimum value.
Claims (1)
1., based on a Fixture assembly Sequence Planning method for particle swarm optimization, comprise the following steps:
The first step, utilizes the information in the hierarchical assembly model of assemble planning in connecting relation attribute list to generate the initial connecting relation figure of assembly;
Second step, according to the level relation of inclusion in connecting relation figure and assembling level relational tree between each parts, constructs the connecting relation figure of the correspondence of whole assembly and each sub-assemblies, determines the position relationship between each part, interference relation;
3rd step, according to outfit in assembling connecting relation figure determination assembling process, structure assembly tool constraint matrix M;
4th step, utilizes assembly knowledge, determines each operation required time of assembling process, final rise time constraint matrix T;
5th step, application particle swarm optimization generates feasible assembly sequence-planning;
6th step, utilizes assembly knowledge, and the interference matrix generated by the position relationship between each part, annexation, judges the Assembly sequences correctness generated;
7th step, the overall assembly sequence-planning result that output represents with Gantt chart.
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| CN108255141A (en) * | 2018-01-17 | 2018-07-06 | 北京理工大学 | A kind of assembling schedule information generating method and system |
| CN109784263A (en) * | 2019-01-09 | 2019-05-21 | 大连理工大学 | A kind of sub-assemblies extracting method based on interference with connection relationship |
| CN111625996A (en) * | 2020-05-26 | 2020-09-04 | 武汉理工大学 | Hierarchical parallel multi-station assembly sequence planning method |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105947232A (en) * | 2016-06-06 | 2016-09-21 | 电子科技大学 | Aircraft fuselage component assemblability evaluation method taking assembling resource influences into consideration |
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| CN108255141A (en) * | 2018-01-17 | 2018-07-06 | 北京理工大学 | A kind of assembling schedule information generating method and system |
| CN108255141B (en) * | 2018-01-17 | 2019-11-26 | 北京理工大学 | A kind of assembling schedule information generating method and system |
| CN109784263A (en) * | 2019-01-09 | 2019-05-21 | 大连理工大学 | A kind of sub-assemblies extracting method based on interference with connection relationship |
| CN109784263B (en) * | 2019-01-09 | 2020-09-11 | 大连理工大学 | Sub-assembly body extraction method based on interference and connection relation |
| CN111625996A (en) * | 2020-05-26 | 2020-09-04 | 武汉理工大学 | Hierarchical parallel multi-station assembly sequence planning method |
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