无先验信息的小批量生产质量控制图动态优化

Dynamic Optimum Method of Control Charts for Small Batch Manufacturing

针对日益发展的小批量生产模式的质量控制,在样本数据无先验信息条件下,提出了一种基于随机加权法的质量控制图参数优化方法.采用随机加权法对样本数据进行再抽样,估计样本数据的分布参数(样本均值和方差),经假设检验,动态优化质量控制图的控制参数弥补了小样本条件下传统控制图的不足,实现了无先验信息下小批量生产过程的质量控制.实验表明,优化后的控制图的控制参数更加逼近于理论值,优于传统控制图的控制参数,适合用于小批量生产环境下的质量控制.

For quality control of the developed small batch manufacturing mode, a method based on the stochastic weighted theory is proposed to optimize control parameters of control charts. The small sample data is sampled again through the stochastic weighted method to obtain more information. The sample data＇s distribution parameters are then estimated. After the estimated distribution parameters are tested through the supposed test method, control parameters of control charts can be dynamically optimized. The method improves traditional control charts under low-volume production and realizes the quality control of small batch of manufacturing. Experiments show that these optimized control parameters approach theory true values and are better than that of the traditional control charts. Therefore, the proposed method is suitable for the quality control of small batch of manufacturing.