Multi-response multi-factorial master ranking in non-linear replicated-saturated DOE for qualimetrics

Besseris, George J.

doi:10.1016/j.chemolab.2012.04.009

Cited by 12 publications

(11 citation statements)

References 41 publications

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“…Once the number of replicates was settled, the response dataset for each characteristic was condensed using ranking operations [14][15][16][17][18]. Both monitored characteristics followed the "smaller-is-better" optimization direction based on Taguchi categorization [13].…”

Section: Discussionmentioning

confidence: 99%

“…To achieve this, the homogenized responses, rCM and rP were squared and then added to form the sum of squared rank responses, ssRS (Table 7). Re-ranking the ssRS vector formed the master response (MR) which now contained in equal proportions the contributions due to both quality characteristics and their associated repeatability levels [14][15][16][17]. The main-effects plot of ssRS vector for the data means is depicted in Fig.…”

Section: Joint Screening Of Chip Morphology and Power Consumptionmentioning

confidence: 99%

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Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Toulfatzis

Pantazopoulos²,

Besseris

et al. 2016

Int J Adv Manuf Technol

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The aim of this work was the evaluation of the machinability of leaded brass namely CuZn39Pb3 (CW614N) in comparison to three lead-free brasses alloys namely CuZn42 (CW510L), CuZn38As (CW511L), and CuZn36 (C27450). The machinability of the studied alloys was investigated, based on chip morphology and power consumption, as quality characteristics. Microstructure examination and hardness testing was employed for the characterization of the selected alloys. Design of experiments (DOE) was employed in a multi-non-parametric study in order to identify the critical-to-machinability parameters and obtain their optimum values for high performance machining. The attempted joint screening using a four-level orthogonal array revealed that the depth of cut and the alloy type were the two statistically predominant effects. The chip morphology and the power consumption in a balanced concurrent optimization effort were optimal when the depth of cut was set at 0.5 mm for the alloy type CW614N chip morphology optimization. The optimal chip morphology response was split in equal chances to produce needle or arc chips. The predicted power consumption was confined in a range of values with an upper boundary at 65 W. The findings of the statistical evaluation were experimentally confirmed, validating the DOE approach. Keywords Machinable brass . Lead-free brasses . Machinability . Design of experiments (DOE) Nomenclature ANOVA Analysis of variance ER Error contribution HV final The chip average hardness (Vickers hardness) HV initial The initial surface hardness (Vickers hardness) MANOVA Multivariate analysis of variance M CM G Grand median value of CM data M P G Grand median value of P data mCM DC o Median value of CM for the optimal setting of DC effect mCM M o Median value of CM for the optimal setting of M effect mP DC o Median value of P for the optimal setting of DC effect mP M o Median value of P for the optimal setting of M effect MR Master response (ranked values of ssRS) OA Orthogonal array R 2 Coefficient of determination rCM i (i = 1,2) Ranked values of the response of the chip Morphology replicates rP i (i = 1,2) Ranked values of the response of the power consumption replicates srCM rSM 1 + rSM 2 srP rP 1 + rP 2 rCM Ranked values of srCM rP Ranked values of srP ssRS rCM 2 + rP 2 WH

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Section: Discussionmentioning

confidence: 99%

Section: Joint Screening Of Chip Morphology and Power Consumptionmentioning

confidence: 99%

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Toulfatzis

Pantazopoulos²,

Besseris

et al. 2016

Int J Adv Manuf Technol

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“…Likewise, the multi-response FFD setup would also be confronted by collapsing multiple responses - before analyzing them - to a single master response [51]. In such case, the efficient data processor which we presented in this paper may prove to be handy.…”

Section: Discussionmentioning

confidence: 99%

A Distribution-Free Multi-Factorial Profiler for Harvesting Information from High-Density Screenings

Besseris

2013

PLoS ONE

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Data screening is an indispensable phase in initiating the scientific discovery process. Fractional factorial designs offer quick and economical options for engineering highly-dense structured datasets. Maximum information content is harvested when a selected fractional factorial scheme is driven to saturation while data gathering is suppressed to no replication. A novel multi-factorial profiler is presented that allows screening of saturated-unreplicated designs by decomposing the examined response to its constituent contributions. Partial effects are sliced off systematically from the investigated response to form individual contrasts using simple robust measures. By isolating each time the disturbance attributed solely to a single controlling factor, the Wilcoxon-Mann-Whitney rank stochastics are employed to assign significance. We demonstrate that the proposed profiler possesses its own self-checking mechanism for detecting a potential influence due to fluctuations attributed to the remaining unexplainable error. Main benefits of the method are: 1) easy to grasp, 2) well-explained test-power properties, 3) distribution-free, 4) sparsity-free, 5) calibration-free, 6) simulation-free, 7) easy to implement, and 8) expanded usability to any type and size of multi-factorial screening designs. The method is elucidated with a benchmarked profiling effort for a water filtration process.

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“…The inherent messiness of the replicated multi-response dataset is demonstrated in order to justify the necessity for the suggested hybridization of a distribution-free treatment by an intelligent processor (Milliken and Johnson, 2004;Lamrini et al, 2012). The required replicate compression will cast the problem to the menacing unreplicated-saturated condition which is convincingly resolved while overcoming reliance on the ambiguous sparsity assumption (Besseris, 2012). In a nutshell, the main objective of this work is to concurrently screen the potency of the four controlling factors -the kneading time, the lamination level, the added water quantity and the margarine temperature -against four product responses: the sensory performance, the moisture content, the package weight and baked puff-pastry height.…”

Section: Doementioning

confidence: 97%

Multi-response multi-factorial master ranking in non-linear replicated-saturated DOE for qualimetrics

Cited by 12 publications

References 41 publications

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

A Distribution-Free Multi-Factorial Profiler for Harvesting Information from High-Density Screenings

Profiling multiple static and transient puff-pastry characteristics with a robust-and-intelligent processor

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