ABSTRACT
Tabled sampling schemes such as MIL-STD-105D offer limited flexibility to quality control engineers in designing sampling plans to meet specific needs. We describe a closed form solution to determine the AQL indexed single sampling plan using an artificial neural network (ANN). To determine the sample size and the acceptance number, feed-forward neural networks with sigmoid neural function are trained by a back propagation algorithm for normal, tightened, and reduced inspections. From these trained ANNs, the relevant weight and bias values are obtained. The closed form solutions to determine the sampling plans are obtained using these values. Numerical examples are provided for using these closed form solutions to determine sampling plans for normal, tightened, and reduced inspections. The proposed method does not involve table look-ups or complex calculations. Sampling plan can be determined by using this method, for any required acceptable quality level and lot size. Suggestions are provided to duplicate this idea for applying to other standard sampling table schemes.
Notes
n, a–sample size, acceptance number MIL-STD-105D tables
n′, a′–sample size, acceptance number from obtained Equations
*–corrected to whole numbers
n, a–sample size, acceptance number MIL-STD-105D tables
n′, a′–sample size, acceptance number from obtained Equations
*–corrected to whole numbers
n, a–sample size, acceptance number MIL-STD-105D tables
n′, a′–sample size, acceptance number from obtained Equations
*–corrected to whole numbers