Optimal levels of process parameters for products with multiple characteristics

Elsayed, Elsayed A.; Chen, Argon

doi:10.1080/00207549308956778

Cited by 162 publications

(71 citation statements)

References 5 publications

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“…The method of quality loss functions in achieving robust consumer products has been outlined by Clausing and Taguchi [4], Taguchi [5] and Ross [6]. Product multicharacteristic selection has shown to be solvable by fundamental Taguchi methods [7]. Vining and Myers [8] have attempted to bring together the best features of two very popular methods, response surface methodology and Taguchi's robust design in order to produce a superior optimizer hybrid.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the management team at this point would prefer to observe comparative behavior among all engaging factors at some reasonable process and product parameter extremities that nevertheless are not foreign to tested recipes in previous patent work. Hence, it was thought that a two-level L 8 (2 7 ) orthogonal array should be utilized in selecting the appropriate trial run combinations. Resorting to OAs makes sense from an economical perspective in this situation because each workpiece resulting to final product may pile up a cost of the order of $100 000 requiring a combined cycle time of one to two months to produce it.…”

Section: A Case Studymentioning

confidence: 99%

“…To enable the use of Wilcoxon-Mann-Whitney test for data collected by a 2-level orthogonal array, such as the L 8 (2 7 ) highlighted in the case study, response entries accrued in the master superrank column of Figure 7 should be separated in two groups according to the factor level they reflect. The case study that follows this discussion will assist in comprehending this type of grouping.…”

Section: The Multiresponse Nested Super-ranking Approach In Unreplicamentioning

confidence: 99%

“…Additional advantages discussed for the proposed technique is the convenience in mixing up variable and categorical response data alike. The theory developed here is targeting orthogonal array families members of L 4 (2 3 ), L 8 (2 7 ), L 12 (2 11 ), and L 16 (2 15 ) because of their inherent reliance on low sampling count [3]. A case study drawn from the heavy industry aids in demonstrating the possible usefulness of the tool developed herein.…”

Section: Introductionmentioning

confidence: 99%

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Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Besseris

2008

International Journal of Quality, Statistics, and Reliability

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Recommended by Fugee TsungModern production methods demand the synchronous multicharacteristic optimization of goods. There is a need to diversify a basic product to the importance placed on its individual quality components by a wide spectrum of concerned customers. This work shows how the super-ranking concept may be utilized taking into account relative weights among the implicated responses. The theoretical development is focused on the difficult situation where the optimization is attempted through unreplicated and saturated fractional factorial designs. A nested super-ranking scheme is devised to accommodate a dual weight assignment, first by setting up a single consolidated response per implicated customer and then, in a subsequent step, by incorporating a customer importance rating thus rendering an overall single master response. A demonstration of the proposed method on a pragmatic problem arising in aluminum milling involves optimization due to seven controlling factors concurrently influencing nine product responses modulated by six preference ratings set by a given customer base, respectively. Key benefits of this method are the offered ease of intermixing numerical and categorical data in mainstream multiresponse optimization problems, and keeping customer preferences in perspective through economical, short-cycle screening while relaxing stringent data normality and possible multidistributional effects among the implicated quality characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

Section: A Case Studymentioning

confidence: 99%

Section: The Multiresponse Nested Super-ranking Approach In Unreplicamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Besseris

2008

International Journal of Quality, Statistics, and Reliability

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“…It is very important for the proper selection of linear motion guide process parameters to improve noise level, push force value and precision. The Taguchi method [3][4][5][6][7][8] is a systematic application of design and analysis of experiments for the purpose of designing and improving product quality.…”

Section: Introductionmentioning

confidence: 99%

The use of grey-based Taguchi methods to determine Process Parameter of linear motion guide with Multiple Performance Characteristics

Hsiao¹,

Kung²,

Tarng³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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By the grey-based Taguchi methods, this study attempts to analyze the process parameters pertinent to the multiple performance characteristics of linear motion guides and obtain the multiple performance characteristics of the noise level, push force value and horizontal combination precision of the linear motion guide with multiple qualities. Optimal process parameters are determined by using the parameter design proposed by the Taguchi method. Experimental results have shown that optimal linear motion guide process parameters can be determined effectively so as to improve multiple qualities through this new approach.

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A Prediction Region‐based Approach to Model Uncertainty for Multi‐response Optimization

Ouyang

Byun

et al. 2015

Quality & Reliability Eng

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Multi‐response optimization methods rely on empirical process models based on the estimates of model parameters that relate response variables to a set of design variables. However, in determining the optimal conditions for the design variables, model uncertainty is typically neglected, resulting in an unstable optimal solution. This paper proposes a new optimization strategy that takes model uncertainty into account via the prediction region for multiple responses. To avoid obtaining an overly conservative design, the location and dispersion performances are constructed based on the best‐case strategy and the worst‐case strategy of expected loss. We reveal that the traditional loss function and the minimax/maximin strategy are both special cases of the proposed approach. An example is illustrated to present the procedure and the effectiveness of the proposed loss function. The results show that the proposed approach can give reasonable results when both the location and dispersion performances are important issues. Copyright © 2015 John Wiley & Sons, Ltd.

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Optimal levels of process parameters for products with multiple characteristics

Cited by 162 publications

References 5 publications

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

The use of grey-based Taguchi methods to determine Process Parameter of linear motion guide with Multiple Performance Characteristics

A Prediction Region‐based Approach to Model Uncertainty for Multi‐response Optimization

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