Selecting relevant effects in factorial designs

Cintas, Pere Grima; Rodero, Lourdes; Tort‐Martorell, Xavier

doi:10.1002/qre.2702

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…The following proposal is based on a more formal one presented by Grima et al (2020), briefly commented on in the section titled "A more formal alternative to step 3", and in a suggestion from Professor Alberto Luceño (Universidad de Cantabria, Spain) in a personal communication to one of the authors. It consists of three simple steps:…”

Section: Proposed Methodologymentioning

confidence: 99%

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Considering the risk of ignoring active factors in industrial experiments

Cintas

Rodero

Tort‐Martorell

2021

Quality Engineering

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Section: Proposed Methodologymentioning

confidence: 99%

“…The new critical values are those shown in the diagram. Details on their calculation can be found in Grima et al (2020).…”

Section: A More Formal Alternative To Stepmentioning

confidence: 99%

Considering the risk of ignoring active factors in industrial experiments

Cintas

Rodero

Tort‐Martorell

2021

Quality Engineering

Self Cite

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“…While reliability and process variability may be affected by many potential factors, some are more significant than others and must be identified. 27,28 As the removal of significant factor effects or the inclusion of unimportant factor effects can affect the accuracy of model predictions and optimization results, Vining and Myers 15 used a t-test to select the significant factors. Lee 29 adopted the asymptotic information criteria to select the factors, while Lin and Tu 30 determined them using stepwise regression.…”

Section: Introductionmentioning

confidence: 99%

“…The identification of significant factor effects on the location and dispersion effects is the basis for optimization. While reliability and process variability may be affected by many potential factors, some are more significant than others and must be identified 27,28 . As the removal of significant factor effects or the inclusion of unimportant factor effects can affect the accuracy of model predictions and optimization results, Vining and Myers 15 used a t ‐test to select the significant factors.…”

Section: Introductionmentioning

confidence: 99%

Framework for robust parameter design and optimization of reliability characteristics

Zhao

et al. 2022

Quality & Reliability Eng

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To achieve better product quality, the dual‐response surface has been widely used to simultaneously achieve targets for the mean and reduced variance. The general assumption is that the experimental data are normally distributed without censoring. However, reliability data are usually non‐normally distributed and censored. Consequently, the classical dual‐response surface method may not be suitable for handling reliability data. This study proposed a dual‐response surface method based on robust parameter design (RPD) for reliability data. First, a percentile lifetime and an interquartile range (IQR) were used for the location and dispersion effects, respectively. A two‐stage method was then developed to identify the effects of significant factors. During the first stage, the Boruta algorithm was employed for factor effect selection, and during the second stage, the least absolute shrinkage and selection operator (LASSO) dimension reduction method was used to determine the final significant factor effects. Finally, a modified standard particle swarm optimization (SPSO) algorithm was adopted to determine the optimal solution. The proposed method was demonstrated using an industrial thermostat experiment. Compared with other methods, the proposed method performed better over various warranty periods, particularly for the 10th percentile.

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“…Nevertheless there is a recent reference by Young, 10 based on the Faulkenberry 8 approach, who proposes a criterion in choosing

P^{'}

and δ avoiding subjectivity, in particular,

P^{'}

is determined as a function of the margin between the specification limits and the estimated mean

\overset{μ}{true}

derived from historical data; thus guiding the practitioner in achieving a sample size mostly with low values concerning Type I error. It must be said that there are no constraints to fulfill assigned power requirements (Type II error), moreover, as pointed out by Grima et al, 11 the main concern for the industrial context is precisely the Type II error.…”

Section: Introductionmentioning

confidence: 99%

Determining sample size for specification limits verification with tolerance intervals

Vasta

2021

Quality & Reliability Eng

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In the traditional industrial verification process, when the aim is the compliance to assigned specifications, it is difficult to find an affordable statistical method for the purpose. Most data tables in industrial procedures and standards deal with tolerance limits neglecting the potential needs to verify assigned specification limits. A two‐sided tolerance interval, combined with a bivariate statistical hypothesis test can be used to address this problem. The proposed risk‐based approach leads to the determination of the minimum sample size with preestablished probabilities of Type I and Type II errors, that are essential elements for estimating the safety and reliability risk. A novel method is proposed for determination of the tolerance interval testing factors. This approach calculates the testing factors based on the deviation of the mean and the variance from the null hypothesis when a specified value of Type II error is found. The deviations of the mean and variance are determined in such a way that an assigned proportion of the population falls within the specification limits. Additional studies are provided to assess the robustness of the method for nonnormal environments and to compare it with other methods.

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Selecting relevant effects in factorial designs

Cited by 3 publications

References 7 publications

Considering the risk of ignoring active factors in industrial experiments

Considering the risk of ignoring active factors in industrial experiments

Framework for robust parameter design and optimization of reliability characteristics

Determining sample size for specification limits verification with tolerance intervals

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