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Monitoring process for attributes with quality deterioration and diagnosis errors

Monitoring process for attributes with quality deterioration and diagnosis errors The aim of this paper is to present an online economical quality‐control procedure for attributes in a process subject to quality deterioration after random shift and misclassification errors during inspections. The process starts in control (State I) and, in a random time, it shifts to out of control (State II). Once at State II, the non‐conforming fraction increases according to a non‐decreasing function ψ(z), where z is the number of items produced after a shift. The monitoring procedure consists of inspecting a single item at every m produced items, which is examined r times independently to decide its condition. Once an inspected item is declared non‐conforming, the process is stopped and adjusted. A direct search technique is used to find the optimum parameters which minimize the expected cost function. The proposed model is illustrated by a numerical example. Copyright © 2007 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Monitoring process for attributes with quality deterioration and diagnosis errors

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References (26)

Publisher
Wiley
Copyright
Copyright © 2007 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.675
Publisher site
See Article on Publisher Site

Abstract

The aim of this paper is to present an online economical quality‐control procedure for attributes in a process subject to quality deterioration after random shift and misclassification errors during inspections. The process starts in control (State I) and, in a random time, it shifts to out of control (State II). Once at State II, the non‐conforming fraction increases according to a non‐decreasing function ψ(z), where z is the number of items produced after a shift. The monitoring procedure consists of inspecting a single item at every m produced items, which is examined r times independently to decide its condition. Once an inspected item is declared non‐conforming, the process is stopped and adjusted. A direct search technique is used to find the optimum parameters which minimize the expected cost function. The proposed model is illustrated by a numerical example. Copyright © 2007 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and Industry – Wiley

Published: Jul 1, 2007

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