Illustrating the Use of Statistical Experimental Design and Analysis for Multiresponse Prediction and Optimization

Izraelevitz, Adam; Anderson‐Cook, Christine M.; Hamada, Michael S.

doi:10.1080/08982112.2011.575499

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Examples of project-based introductory course on DoE, how to run a successful industrial experiment while teaching DoE, and a short course or workshop on multiresponse optimization are available in the literature. The reader is referred to Anderson-Cook (1998), Marco et al (2004) and Izraelevitz et al (2011), as examples.…”

Section: Hindrances To Doe Usagementioning

confidence: 99%

Design of experiments – overcome hindrances and bad practices

Costa

2019

TQM

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Purpose The purpose of this paper is to address misconceptions about the design of experiments (DoE) usefulness, avoid bad practices and foster processes’ efficiency and products’ quality in a timely and cost-effective manner with this tool. Design/methodology/approach To revisit and discuss the hindrances to DoE usage as well as bad practices in using this tool supported on the selective literature from Web of Science and Scopus indexed journals. Findings A set of recommendations and guidelines to mitigate DoE hindrances and avoid common errors or wrong decisions at the planning, running and data analysis phases of DoE are provided. Research limitations/implications Errors or wrong decisions in planning, running and analyzing data from statistically designed experiments are always possible so the expected results from DoE usage are not always 100 percent guaranteed. Practical implications Novice and intermediate DoE users have another perspective for developing and improving their “test and learn” capability and be successful with DoE. To appropriately plan and run statistically designed experiments not only save the user of DoE from incorrect decisions and depreciation of their technical competencies as they can optimize processes’ efficiency and products’ quality (reliability, durability, performance, robustness, etc.) in a structured, faster and cheaper way at the design and manufacturing stages. Social implications DoE usefulness will be increasingly recognized in industry and academy and, as consequence, better products can be made available for consumers, business performance can improve, and the link between industry and academy can be strengthened. Originality/value A supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented.

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Section: Hindrances To Doe Usagementioning

confidence: 99%

Design of experiments – overcome hindrances and bad practices

Costa

2019

TQM

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“…We illustrate the proposed log‐covariance modeling with data from a propeller experiment. Motivated by Izraelevitz et al, a 2 4 experiment that makes propellers from plastic cups was run in four factors (three determine the design of a propeller, number of blades [B], blade angle [A], blade length [L], and use condition, fan speed [S]) with 25 replicates per run. The three responses were force (F) in grams, rotation (R) in RPM, and stability (ST, variation of vibration captured by an accelerometer).…”

Section: A Propeller Experimentsmentioning

confidence: 99%

“…The three responses were force (F) in grams, rotation (R) in RPM, and stability (ST, variation of vibration captured by an accelerometer). The same factors were used as detailed in Izraelevitz et al, whose levels are B (4 and 8), A (10 and 30 degrees), L (half and full), and S (low and high).…”

Section: A Propeller Experimentsmentioning

confidence: 99%

“…We illustrate the proposed log-covariance modeling with data from a propeller experiment. Motivated by Izraelevitz et al 7 We use diffuse prior distributions: Normal(0, 100 2 ) for the intercepts, Normal(0, 1 2 ) for the factorial effects, and i and i for the log-covariance model. We also use a burn-in of 1,000 draws and thinning of 100 to obtain 10,000 draws from the posterior distribution.…”

Section: A Propeller Experimentsmentioning

confidence: 99%

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A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix‐logarithmic covariance model

Hamada

Jaramillo

Chiao

2019

Quality & Reliability Eng

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In the literature, analysis of multiple responses from experiments with replicates has modeled the covariance matrix directly as linear models of the transformed variances and correlations, ie, covariance modeling. This article considers models based on the matrix‐logarithm of the covariance matrix. This so‐called log‐covariance modeling is illustrated with data from actual experiments and compared with the traditional covariance modeling.

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“…Using the average values for extraction showed in Table 3, and the Kolmogorov-Smirnov statistic test with 95 % confidence, it was shown that data collected for performance follows a normal distribution with a p-value of 0,090 (Izraelevitz et al, 2011). For standard deviation, Bonferroni confidence intervals of 95 % were performed for each level.…”

Section: Response Surface Methodologymentioning

confidence: 99%

Optimization of castor seed oil extraction process using response surface methodology

Mosquera¹

2016

ing.inv.

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This work focuses on the study of the oil extraction yield from castor seed using three different seed conditions: whole, minced and bare endosperm. Taguchi design was used to determine the contribution of the following parameters: seed condition, seed load in the extractor, temperature, and pressure. It was proved that it is necessary to introduce the whole seed and that the presence of the pericarp increases the extraction yield. The contribution of the control factors has an extraction yield limit. After determining which factors contributed to the process, these were left at their optimum levels aiming to reduce the control factors to only two. The complete analysis was done using a surface response methodology giving the best parameter for temperature and pressure that allows a better yielding mechanical extraction. The oil extraction yield can be kept up to 35 % of the seed. Keywords: Experiment design, mechanical extraction, Ricinus communis L. RESUMENEste trabajo se centra en el estudio de la extracción y la producción de aceite de semillas de ricino usando tres diferentes estados de semillas: entera, partida y sin testa. Se utilizó un diseño Taguchi para determinar la contribución de los siguientes parámetros: estado de las semillas, cantidad de semilla en el extractor, temperatura y presión. Se demostró que es necesario introducir la semilla entera para aumentar el rendimiento de la extracción. La contribución de los factores de control tiene un límite de extracción en la variable respuesta (rendimiento). Después de determinar cuáles factores contribuían altamente al proceso de extracción y cuáles no, se procedió a dejarlos en los niveles de mayor contribución con la finalidad de reducir el número de factores de control a dos. Posteriormente se utilizó la metodología de superficie de respuesta para la optimización del proceso, dando como resultado los niveles óptimos para los factores de control temperatura y presión. De acuerdo con las pruebas, el rendimiento de la extracción del aceite se puede mantener en un 35 % de la semilla.

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Illustrating the Use of Statistical Experimental Design and Analysis for Multiresponse Prediction and Optimization

Cited by 7 publications

References 3 publications

Design of experiments – overcome hindrances and bad practices

Design of experiments – overcome hindrances and bad practices

A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix‐logarithmic covariance model

Optimization of castor seed oil extraction process using response surface methodology

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