摘要
针对产品制造中公差与成本之间高度非线性关系的特点,提出了一种综合神经网络与遗传算法进行公差优化的方法,该方法利用遗传算法在大范围解空间内采用概率搜索策略得到全局最优解且有较强鲁棒性的特点,以及神经网络解决高度非线性问题的优越性,首先利用神经网络对公差成本进行仿真,得到具有黑箱特点的公差成本函数关系;然后在公差分配中采用遗传算法,以总成本最小为目标函数,以满足装配公差要求和符合标准公差等级为约束条件进行优化;同时基于VC和Matlab开发了公差优化系统,以飞机舱门锁钩机构为对象进行了验证,并针对不同的公差成本及分配方法进行了对比.结果表明:采用神经网络与遗传算法综合分配的结果与传统方法相比具有较大的优越性.
Taking into account the highly nonlinear relationship between the cost and the tolerance in the product manufacturing process,a method based on the neural network in conjunction with the genetic algorithm was proposed to solve the tolerance optimal issues.The method integrates the advantage of the genetic algorithm,which can obtain the optimal result in a large-scale solution space using the probability searching strategy and the strong robustness,and the superiority of the neural network that can solve the highly nonlinear problem.In the optimization process,the neural network was trained using sample data to simulate the tolerance-cost function at first and get a function relationship with the black box feature between the cost and the tolerance.And then the genetic algorithm was introduced to optimize the tolerance allocation by taking the results of the trained neural network.It takes the functional requirements and the standard tolerance grades as constraints as well as the minimum of the component cost as the objective.A tolerance optimization system based on a C＋＋ library and the Matlab was designed.Finally,an example of the latching shaft and hook mechanism component of the aircraft cargo door demonstrated the method.The analytical result proves that the new method can produce the tolerance optimization economically and accurately,and has an advantage over traditional methods.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2010年第5期518-523,共6页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家863计划资助项目(2007AA041902)
关键词
公差成本关系
公差优化
神经网络
遗传算法
tolerance-cost function
tolerance optimization
neural network
genetic algorithm
