A destructive sampling method designed for high quality production processes (DSM-HQ)

Abstract

In manufacturing and assembly, the sampling of units produced is important since in many situations not all of the units can be tested. Destructive sampling, which commonly occurs in the assembly and manufacturing industry, is a form of sampling where all units produced cannot be tested since the parts are destructively tested. In this situation, sampling techniques are used to determine if an entire lot should be accepted or rejected based on the sampling results. The traditional sampling techniques include single or classical sampling, double sampling, multiple sampling, skip-lot sampling, chain sampling and MIL-STD-105E.

However, in the modem era of high quality production, traditional sampling techniques require a high number of units tested in order to guarantee a high level of quality resulting in very high sampling costs. Therefore, to keep costs down, manufacturers and assemblers have used these techniques with lower sampling numbers in order to monitor quality.

A goal of this research is to develop a sophisticated technique that monitors quality and outperforms the existing techniques in situations where quality is high and tests are destructive. The proposed technique. Destructive Sampling Method for High Quality production processes (DSM-HQ), is based on a cost function, which balances the costs of sampling versus the costs of finding a defect on the field. DSM-HQ assumes to have a Poisson process defect pattern and uses an Empirical Bayesian analysis to allow the researcher to include prior knowledge.

The research simulation and results are separated in two stages. Stage 1 fine tunes DSM-HQ and examines its properties, while Stage 2 compares DSM-HQ to the traditional methods. The simulation results from Stage 2 show that DSM-HQ is superior to the traditional methods in most cases at the 5-sigma level. As the quality increases to 6-sigma, DSM-HQ proves to be significantly superior to all traditional methods in every cost case considered and in both random events combined with out-of-control events case and the random-event-only case.

Although DSM-HQ sampling method has some limitations, which will be explored in future research, and the case examined here is limited in scope, which will be expanded in future research, the results and comparisons to traditional methods are very encouraging.