The Application of Analytic Hierarchy Process to Determine Proportionality Constant of the Taguchi Quality Loss Function

The Taguchi robust design method traditionally deals with single-characteristic problems. Various methods have been developed for extending the Taguchi single-characteristic robust design method to the case of multi-characteristic robust design problems. However, most of those methods have shortcomings in that they do not properly consider the variance-covariance structures among performance characteristics and/or do not preserve the original properties of the Taguchi signal-to-noise ratio for single-characteristic robust design problems. To overcome these shortcomings, this paper develops a multivariate loss function approach to multi-characteristic robust design problems with an appropriately defined signal-to-noise ratio. Its performance is evaluated using simulated examples, and the results indicate that it generally outperforms existing representative methods for correlated as well as uncorrelated experimental data. IntroductionO ne of the most important goals for engineers is to design high-quality products and processes at low cost and within an affordable amount of time. The robust design (or parameter design) method proposed by Taguchi has been widely used as an efficient way of achieving this goal. The purpose of Taguchi robust design is to determine optimal settings of product or process design parameters while making performance characteristics (PCs) robust to uncontrollable noise variables. In the Taguchi robust design method, orthogonal arrays are used for experimental design, and a performance measure (PM) called the signal-tonoise (SN) ratio is calculated at each experimental run. Then, optimal settings of design parameters are determined such that the SN ratio is maximized.The Taguchi robust design method has mainly focused on the case of a single PC although the case of multiple PCs appears more commonly in practice. For example, in the literature, in order to find the optimal settings of the injection-molding parameters to minimize the imbalance of a plastic wheel cover component, two correlated characteristics of the wheel cover component were considered: total weight and the balance of the component. In another example to improve a plasma-enhanced chemical vapor deposition process in the fabrication of integrated circuits, two correlated characteristics of the deposition thickness and a refractive index were considered. Refer to Chiao and Hamada 1 for more details on these examples. A difficulty involved in multi-characteristic robust design (MCRD) is that optimal settings of design parameters may be different from characteristic to characteristic. An important issue in MCRD is how to resolve such conflicts to provide a compromised design condition.Many authors have extended the Taguchi robust design method to the case of MCRD problems. One popular approach is to combine the PMs of PCs into a single aggregated PM and, thereby, re-formulate the MCRD problem as a single-characteristic robust design (SCRD) problem to be solved. For example, the SN ratio for each PC is calculated independently of...

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“…For some other methods for determining the cost matrix, refer to Vining, Khorramshahgol and Djavanshir, and Özler et al , among others.…”

Section: Signal‐to‐noise Ratio For Multiple Performance Characteristicsmentioning

confidence: 99%

A Multivariate Loss Function Approach to Robust Design of Systems with Multiple Performance Characteristics

Soh

Kim

Yum

2016

Quality & Reliability Eng

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“…In the application, it is difficult to get an accurate value for the proportionality constant ( k i ) in equation (20). A standardized proportionality constant ( k i ) is proposed as the basis of the KCs’ identification, based on the relative size of the CCs’ degrees of influence on the KCs 14…”

Section: The Identification Of Kcsmentioning

confidence: 99%

A systematic top–down approach for the identification and decomposition of product key characteristics

Tang

Yuan

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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Methodologies for the identification of key characteristics have been widely applied in quality management through the selection of critical dimensions and the measurement of variations. However, methods for both the identification and decomposition of key characteristics have not yet been developed, and more research is still required. In answering this need, a systematic top–down decomposition approach for the identification of key characteristics is proposed. The methodology of the identification and decomposition of key characteristics can be divided into two steps: first is the construction of candidate characteristics, and second is the identification of key characteristics. These steps are based on precise mathematical definitions. Initially, the necessary information for the construction of the candidate characteristics is obtained from analysis based on assembly-oriented graph, and that information is then conveyed utilizing feature adjacency matrix. A concept for a propagation chain is then proposed, and a search algorithm for an auto-generating propagation chain is obtained through feature adjacency matrix. The degree of influence of the candidate characteristics on the key characteristics is then defined. A formula is derived that can calculate the degree of influence by utilizing a variation model. Finally, the process of the identification of the key characteristics is achieved according to the relative degrees of influence. An aircraft boarding gate is presented in order to validate the proposed methodology. Two key characteristics of the aircraft boarding gate are identified, and the results indicate the methodology’s feasibility.

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“…The AHP is based on the idea that a complex problem can be effectively examined if it is hierarchically decomposed into its components (Khorramshahgol and Djavanshir 2008). Thus, AHP provides a holistic view of the problem.…”

Section: Using Ahp In Bash Assessmentmentioning

confidence: 99%

Flying Bird Detection and Hazard Assessment for Avian Radar System

Chen

Ning

2012

J. Aerosp. Eng.

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An airport-based avian radar system is an effective technical means for bird-aircraft strike hazard (BASH) avoidance. Three existing systems are surveyed, including Merlin Avian Radar System, Accipiter Avian Radar System, and Beihang Experimental Avian Radar System. Two critical technologies for Beihang experimental system are introduced: flying bird target detecting and tracking algorithm and BASH assessment. The algorithm consists of five steps: background subtraction, clutter suppression, measurement extraction, multitarget tracking, and data overlay. The multitarget tracking algorithm is tested against simulated data. The overall approach is applied on a set of horizontal scanning plane position indicator (PPI) radar images. A BASH assessment model is established by an analytic hierarchy process (AHP), considering two factors of the current states of flying birds and aircraft. Using the BASH assessment model, an application example at Nanyang Airport is described, in detail. Finally, some development trend of critical technologies for airport-based avian radar system is given.

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The Application of Analytic Hierarchy Process to Determine Proportionality Constant of the Taguchi Quality Loss Function

Cited by 13 publications

References 14 publications

A Multivariate Loss Function Approach to Robust Design of Systems with Multiple Performance Characteristics

A Multivariate Loss Function Approach to Robust Design of Systems with Multiple Performance Characteristics

A systematic top–down approach for the identification and decomposition of product key characteristics

Flying Bird Detection and Hazard Assessment for Avian Radar System

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