Please use this identifier to cite or link to this item: https://doi.org/10.1080/08982110601057203
Title: Optimization of multiple response surfaces with secondary constraints for improving a radiography inspection process
Authors: Ng, S.H. 
Xu, K. 
Wong, W.K.
Keywords: Design of experiments
Multiple response optimization
Nested effects modeling
Secondary response constraints
Sliding levels
X-ray radiography inspection process
Issue Date: Jan-2007
Source: Ng, S.H.,Xu, K.,Wong, W.K. (2007-01). Optimization of multiple response surfaces with secondary constraints for improving a radiography inspection process. Quality Engineering 19 (1) : 53-65. ScholarBank@NUS Repository. https://doi.org/10.1080/08982110601057203
Abstract: The quality and reliability measures of products and processes are frequently multidimensional, and the goal of various statistical studies and experiments is to find settings of design or process variables that optimize such products or processes. In many such situations, these measures of interest are further restricted by some physical secondary constraints of the process. These constraints may cause interdependencies among the factors. Moreover, the operability region imposed by the constraints is often unknown a priori, and hence, standard experimental strategy is not always feasible. In this paper, we study a radiography inspection process with dual conflicting radiograph quality measures, contrast sensitivity and spatial resolution, and an image density constraint. The paper presents a sequential approach to tackle this constrained optimization problem. We use and initial experiment to estimate the constraints, and then to account for the interdependencies between the factors more accurately, we use a sliding level design with sliding factors nested within multiple factors. We propose to use a nested effects modeling approach to analyze the experimental data and illustrate its benefits for this example. To optimize conflicting multiple responses, a goal optimization formulation based on this modeling approach is presented.
Source Title: Quality Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/63240
ISSN: 08982112
DOI: 10.1080/08982110601057203
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