基于GABP算法的车身装配分析

Body Assembly Analysis Based on GABP Algorithm

合理的车身装配公差分配可以在产品质量要求与生产成本限制之间得到平衡。文章提出了一种遗传算法(GA)BP神经网络的混合算法,用于生成和优化顺应组件的装配顺序。通过装配建模来描述其几何形状,其中包括三组零件,零件之间的关系和关节。装配序列被表示为个体,被分配评估函数,该评估函数由适应度和约束函数组成。适应度函数用于评估可行序列;此外,约束函数用于演化不可行的序列。混合算法从随机初始的染色体群开始,通过使用繁殖、交叉和变异操作进化新群体,并终止直到可接受的序列输出。描述了薄板组件的组装过程,提出混合算法以选择合适关键测量点(KMP)的最佳方案。

Reasonable body assembly tolerance allocation can be balanced between product quality requirements and production cost constraints.A hybrid algorithm based on genetic algorithm(GA)and back propagation(BP)neural network is proposed to generate and optimize the assembly sequence of compliant components.Assembly modeling is proposed to describe assembly geometry,which includes three groups of parts,relations between parts andjoints.Based on assembly modeling,assembly sequences are represented as individuals,which are assigned evaluation functions,which are composed of fitness and constraint functions.Fitness function is used to evaluate the feasible sequence;In addition,constraint functions are used to evolve infeasible sequences.The hybrid algorithm starts with a random initial population of chromosomes,evolves new populations by using breeding,crossover,and mutation operations,and terminates until an acceptable sequence is output.Firstly,the current assembly process of sheet metal components is described.Secondly,a hybrid algorithm is proposed to select the best solution for the appropriate key measurement point(KMP).