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Determination of closed form solution for acceptance sampling using ANN.

D Vasudevan1, V Selladurai, P Nagaraj

  • 1PSNA College of Engineering and Technology, Dindigul-624622, India. dvasudevanin@yahoo.co.in

Quality Assurance (San Diego, Calif.)
|April 9, 2005
PubMed
Summary
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This study introduces a novel artificial neural network (ANN) method for creating flexible sampling plans. This approach provides a closed-form solution for determining sample sizes and acceptance numbers, improving upon traditional tabled sampling schemes.

Area of Science:

  • Quality Control Engineering
  • Artificial Intelligence
  • Statistical Process Control

Background:

  • Traditional tabled sampling schemes, like MIL-STD-105D, lack flexibility for engineers.
  • Designing custom sampling plans often requires complex calculations or table look-ups.

Purpose of the Study:

  • To develop a flexible, closed-form solution for determining AQL-indexed single sampling plans.
  • To utilize artificial neural networks (ANNs) to overcome limitations of existing sampling methods.

Main Methods:

  • Feed-forward neural networks with sigmoid functions were trained using a backpropagation algorithm.
  • The networks were trained for normal, tightened, and reduced inspection scenarios.
  • Weight and bias values from trained ANNs were used to derive closed-form solutions for sampling plans.

Related Experiment Videos

Main Results:

  • A closed-form solution was successfully derived for determining sample size and acceptance number.
  • The method eliminates the need for table look-ups and complex calculations.
  • Numerical examples demonstrate the application for various inspection levels and lot sizes.

Conclusions:

  • The proposed ANN-based method offers a flexible and efficient way to determine sampling plans.
  • This approach can be adapted for any acceptable quality level (AQL) and lot size.
  • The methodology can be extended to other standard sampling table schemes.