Statistical Design Method Research Articles

Characterization of Cutting Parameters in the Minimum Quantity Lubricant (MQL) Machining Process of a Gearbox

This paper aims to find the key process parameters for machining different parts of an automobile gearbox, commissioned by a company that needs to replace with the MQL lubrication system their current machining process based on cutting fluids. It particularly focuses on the definition of appropriate cutting parameters for machining under the MQL condition through a statistical method of Design of Experiments (DOE). Using a combination of recommended parameters, significant improvements in the surface roughness of different machined parts are shown. Production costs are also reduced by decreasing expenses on lubricants and by optimizing the cycle time reached under the new cutting conditions, what would help the implementing company to increase its profits and adapt to a modern sustainability-demanding production industry.

Capturing Experimental Design Insights in Support of the Model-based System Engineering Approach

Model-Based Systems Engineering (MBSE) continues to mature into a popular approach as systems become more complex. Integrating MBSE architectural diagrams with external simulations that model a variety of different domains allows designers to perform sophisticated engineering design and analysis. To better understand how these external models support design decisions, we can leverage the statistical methods of design of experiments to identify insights into a complex system design problem. These insights include identifying the most important design parameters, the nature of their behaviour, their synergies between them, their diminishing or increasing rates of change, and thresholds that achieve a desired level of effectiveness. The purpose of this paper is to propose a MBSE methodology that captures the insights identified during an experimental design study within the integrated system model while applying the MBSE approach.

How does unemployment affect self-assessed health? A systematic review focusing on subgroup effects.

BackgroundAlmost all studies on the effect on health from unemployment have concluded that unemployment is bad for your health. However, only a few review articles have dealt with this relation in recent years, and none of them have focused on the analysis of subgroups such as age, gender, and marital status. The objective of our article is to review how unemployment relates to self-assessed health with a focus on its effect on subgroups.MethodsA search was performed in Web of Science to find articles that measured the effect on health from unemployment. The selection of articles was limited to those written in English, consisting of original data, and published in 2003 or later. Our definition of health was restricted to self-assessed health. Mortality- and morbidity-related measurements were therefore not included in our analysis. For the 41 articles included, information about health measurements, employment status definitions, other factors included in the statistical analysis, study design (including study population), and statistical method were collected with the aim of analysing the results on both the population and factor level.ResultsMost of the studies in our review showed a negative effect on health from unemployment on a population basis. Results at the factor levels were most common for gender (25 articles), age (11 articles), geographic location (8 articles), and education level (5 articles). The analysis showed that there was a health effect for gender, age, education level, household income, and geographic location. However, this effect differed between studies and no clear pattern on who benefits or suffers more among these groups could be determined. The result instead seemed to depend on the study context. The only clear patterns of association found were for socioeconomic status (manual workers suffer more), reason for unemployment (being unemployed due to health reasons is worse), and social network (a strong network is beneficial).ConclusionsUnemployment affects groups of individuals differently. We believe that a greater effort should be spent on specific groups of individuals, such as men or women, instead of the population as a whole when analysing the effect of unemployment on health.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-1310) contains supplementary material, which is available to authorized users.

Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

The laser brazing (LB) is widely used in the automotive industry due to the advantages of high speed, small heat affected zone, high quality of welding seam, and low heat input. Welding parameters play a significant role in determining the bead geometry and hence quality of the weld joint. This paper addresses the optimization of the seam shape in LB process with welding crimping butt of 0.8mm thickness using back propagation neural network (BPNN) and genetic algorithm (GA). A 3-factor, 5-level welding experiment is conducted by Taguchi L25 orthogonal array through the statistical design method. Then, the input parameters are considered here including welding speed, wire speed rate, and gap with 5 levels. The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead. The experiment results are embed into the BPNN network to establish relationship between the input and output variables. The predicted results of the BPNN are fed to GA algorithm that optimizes the process parameters subjected to the objectives. Then, the effects of welding speed (WS), wire feed rate (WF), and gap (GAP) on the sum values of bead geometry is discussed. Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable. On the whole, the proposed hybrid method, BPNN-GA, can be used to guide the actual work and improve the efficiency and stability of LB process.

Eco-Cement Optimization Using Statistical Mixture Design Method

The statistical mixture design (SMD) method was used to find the optimum blending proportions of economical green cement by blending portland cement with fly ash, silica fume, and calcium carbonate. Twenty blends with different combinations of materials were tested on mortar mixtures to find the optimum and to analyze each. A graphical method was suggested for examining the mixture components on the response variables that included setting time and compressive strengths at 2, 7, and 28 days. The desirability function approach was used to calculate desired optimum blend of eco-cement at a minimum cost. The results from the experimental study were compared with the real-life situation of portland cement and pozzolanic portland cement analyzing on scanning electron microscope (SEM) for the microstructure. In mixture optimization, the SMD method is a useful tool to obtain a scientific solution instead of trial-and-error method with cost-effective mixture proportions.

Discontinuous Modelling of Stratum Cave-in in a Longwall Coal Mine in the Arctic Area

This paper presents a discontinuous numerical approach for studying roof cave-in mechanisms and obtaining the required support capacity of longwall shields in a case study site, the Svea Nord coal mine in Svalbard. The block size in the roof strata and the mechanical parameters of the discontinuities for the numerical model were obtained through back-calculations. The back-calculations were conducted with a statistical method of design of experiment. Numerical simulations revealed that voussoir jointed beams are formed before the first cave-in occurs. The maximum deflection of a roof stratum in the study site prior to the first cave-in is about 70 % of the stratum thickness. The maximum span of the roof strata prior to the first cave-in depends upon the in situ horizontal stress state. The roof beams have a large stable span when they are subjected to high horizontal stress; but horizontal stress would increase the possibility of rock crushing in deflected roof beams. The simulations and field measurements show no periodic weighting on the longwall shields in the study site. Stiff and strong roof beams would result in large first and periodic cave-in distances. As a consequence of having large cave-in distances, the longwall shields must have high load capacity, which can be calculated by the presented numerical approach.

Uranium adsorption studies on hydrothermal carbon produced by chitosan using statistical design method

A new hydrothermal cross-linking chitosan (HCC) was prepared by hydrothermal reaction at a mild temperature (180 °C) to diminish the solubility in acid solution, and the amine groups were almost retained during the hydrothermal reaction characterized by FT-IR and the dissolution rate in pH 3 solution decreased from 89.6 to 12.6 %. The effects of initial pH, contact time, initial concentration and temperature on the sorption capacity are discussed using 24 full-factorial central composite design using response-surface methodology. The HCC showed the highest uranium sorption capacity at initial pH of 7.92 and contact time of 273.6 min with 24 full-factorial central composite design and the maximum adsorption capacity was 273 mg/g. The adsorption process could be well defined by the Langmuir isotherm and the thermodynamic parameters, ∆G°(298 K), ∆H° and ∆S°, demonstrated shown that the sorption process of U(VI) onto HCC was feasible, spontaneous and endothermic in nature.

Optimization of coal flocculation with an anionic flocculant using a box-wilson statistical design method

In this study, the Box-Wilson statistical experimental design method was employed to evaluate suspension pH, salt (CaCl2·2H2O) concentration and anionic flocculant (A-150) amount in flocculation of coal. Response function coefficients were determined by the regression analysis of experimental data and the predictions were found to be in good agreement with the experimental results. The optimum pH, salt (CaCl2·2H2O) concentration and anionic flocculant (A-150) amount were determined as 9.8, 0.0009 M and 791 g/Mg respectively, when minimum turbidity and maximum settling rate are considered.

Removal of AY99 from an Aqueous Solution Using an Emulsified Liquid Membrane. Application of Plackett-burman Design

Water contaminated with dyes presents serious environmental problems. An important quantity of dyes is released as industrial waste in nature. As part of the recovery and the regeneration of these complexes, the extraction of a cationic dye Acid Yellow 99 has been the subject of this work. To removal dyes from industrial wastewater, the technique of extraction by emulsion liquid membrane could provide an industrial success. The membrane used in this study consisted of SPAN80 as emulsifier and aliquat 336 as extractant. The stability of the emulsified liquid membrane has a very important role in the extraction. A study of the effects of different components of the membrane is necessary. The process parameters were studied using a statistical method of experimental Plackett-Burman design, This method allows us to study the effects of different factors simultaneously and determines which factors are most important parameters. The modeling was done by a mathematical model representing the extraction yield according to various factors. The most significant factors on the elimination of the acid yellow99 by the emulsion liquid membrane were then studied.

Study on Statistical Prediction and Design Method for Indoor Thermal Environment

The indoor thermal environment is affected by many heat factors such as occupants′ activities and outdoor climate changes. Because each of them varies asynchronously, the heat factors′ variation shows a nonsingle degree of freedom overall. Moreover, the heat factors′ impact on space is not uniform. Therefore, there always exist spatial distributions and temporal variations. In practice, the number of degrees of freedom of an air-conditioning system is usually limited and less than that of the object zone. The control performance of an air-conditioning system designed under a perfect mixing method cannot be satisfactory because the spatial distribution is neglected. For energy conservation and thermal comfort improvement, it is necessary to propose an air-conditioning system design method which can positively use the spatial distribution and temporal variation of the indoor thermal environment. In this study, methods for indoor temperature fluctuation analysis and optimization of air-conditioner sensor location are proposed. To represent the temperature field structure, an index called CRI is integrated. With this method, the spatial distribution and temporal variation can be calculated immediately and analyzed statistically; furthermore, the indoor temperature and outdoor climate′s correlation can be concisely visualized. A case study is conducted to verify the validity of this method.

Impact of Heating Rate During Exposure of Laser Molten Parts on the Processing Window of PA12 Powder

Abstract The additive component manufacturing by selective beam melting of thermoplastic polymer powders can be divided essentially into the following sub-processes: Powder coating, exposure and material consolidation. The mechanical and geometrical properties of a part produced by the selective melting of polymer powders depend toa large extent on these sub-processes. To increase process repeatability basic knowledge about the mutual interactions within the sub-process is of major interest. In the following article the exposure process is focused. Therefore the time dependent energy input into the powder bed is analyzed in its impact on the usable processing window of PA12powder. Thereby parameters like surface temperature, density and strength of molten layers as well as complex body specimens are quantified for varying exposure heating rates. Therefore methods of statistical design of experiments are used. Due to these investigations the derivation of new, the time dependent material behavior of polymers fitting processing strategies is possible.

Optimization of Silver Removal from Anode Slime by Microwave Irradiation in Ammonium Thiosulfate Solutions

We investigate the optimization of removal of silver from anode slime in ammonium thiosulfate solutions by microwave using statistical design methods. Ammonium thiosulfate concentration, leaching temperature, solid-to-liquid ratio and leaching time were selected as variables. A model was obtained among relevant parameters by means of variance analysis by using the Matlab Computer Software Programme. The optimum conversion conditions for process were found to be ammonium thiosulfate (ATS) concentration of 0.4 M, leaching temperature of 313 K, solid-to-liquid ratio of 1/10, leaching time of 90 min, and stirring speed of 500 rpm. Under optimal conditions, the removal of silver is obtained as 88.76%. It is observed that the model obtained has nearly fitted the full second-order model, and the correlation coefficient calculated at the 95% confidence level has a value of 0.97.

Multi-objective genetic algorithm for economic statistical design of [formula omitted] control chart

Control charts are widely used for monitoring the quality of a product or a process. Their implementation cost motivates researchers to design them with the lowest cost and most desirable statistical properties. Usually, the cost function is optimized subject to statistical properties. However, the cost function also depends on statistical properties, and minimizing it as the only objective is not an efficient method of economic statistical design of control charts. In this paper, cost function, as well as statistical properties, including probability of Type I error, power of X¯ control chart, and Average Time to Signal (ATS), are considered as objectives; the corresponding constraints are also used. Then, a Multi-Objective Genetic Algorithm for Economic Statistical Design (MOGAESD) is proposed for identifying the Pareto optimal solutions of control chart design. The preferred solution is selected by the designer. The performance of the proposed method is compared through some numerical examples reported in the literature. The results show that the proposed approach is effective.

Behavioural Modelling of Corundum under the Effect of Porosity Rate

Corundum, which is a form of Alumina, is used to manufacture various components and mechanical, electrical and electronics parts. Known for its relatively high hardness, its other characteristics depend on the porosity rate that varies depending on the manufacturing process. One of the characteristics to consider is the tensile stress value changes in a standard test to failure as a function of applied force and porosity rate. In this study, for a better understanding of the effect of the two parameters, we used the statistical method of design of experiments that makes it possible to establish a mathematical model of the response, by which explains the effect of the two parameters in isolation from each other, then their effect simultaneously. Consequently, we can explain the mechanical behaviour of the porous corundum, with curves and graphs, and optimize the final result.

Optimization of filter designs with dependent and asymmetrically distributed parameters

This paper presents a new statistical design method for maximizing the manufacturing yield of engineering systems for which the realizations of design parameters are assumed to be dependent random variables. Like in many practical situations, the method assumes that the joint distribution of design parameters is unknown and their marginal distributions can be estimated but are not necessarily symmetrical. This is a difficult problem to which little research has been devoted, other than using some brute force search methods. We use a Frank copula to construct the joint distribution of correlated random variables. Kumaraswamy density function is used to approximate their marginal distributions because of its flexibility and simplicity. The proposed method is based on the approximation of the yield integral over the largest rectangular hypercube (n-box) that is contained in the feasible region. It tries to maximize the yield by relocating and rescaling the box so that higher portion of the manufacturing yield is captured by this box. No integration is necessary since the yield of any given design is approximated by evaluating the cumulative distribution of the copula at the endpoints of the associated n-box. Finally, the actual yield of the optimal design is tested using Monte-Carlo simulation. This requires generating correlated random samples from the chosen copula distribution. One tutorial example and two practical design problems demonstrate the applications of the proposed method. Computational results show that the optimal designs significantly increase the manufacturing yield and this observation is verified using Monte-Carlo simulation.

Optimization of culture conditions for production of lipase by a newly isolated bacterium Stenotrophomonas maltophilia

The production of an extracellular lipase from the newly isolated Stenotrophomonas maltophilia was optimized via the statistical design method. During a screening program, the microorganism was isolated from soil samples, according to lipolytic activity on nutrient agar medium supplemented with Rhodamine B. It was identified using morphological and biochemical properties as well as 16S rDNA sequencing analysis. After the initial screening of 10 factors of the medium components, using Plackett–Burman design, results showed that four of them, included olive oil, peptone, yeast extract, and ferrous sulfate, exhibited significant effects on the lipase production by S. maltophilia. The level of each factor was subjected to optimization by using the Box–Behnken technique, and a 9.1-fold enhancement of lipase productivity (from 500U/ml to 4559U/ml) was achieved overall in the presence of optimum levels of the effective factors. Forty-three percent of capsaicin (initial concentration of 500mg/L) was converted to vanillylamine after treatment with the optimized medium (lipase activity 4559U/ml) for 18h.

Optimization of Microwave-Assisted Removal of Lead from Anode Slime in Triethanolamine Solutions

The aim of this work is to investigate the optimization of removal of lead from anode slime in triethanolamine solutions by the microwave effect using statistical design methods. Triethanolamine concentration, leaching temperature, solid-to-liquid ratio, and leaching time has been selected as variables. A model has been obtained among relevant parameters by means of variance analysis by using the Matlab Computer Software Programme. The optimum conversion conditions for process are found to be a triethanolamine concentration of 3.5 M, leaching temperature of 313 K, solid-to-liquid ratio of 1/10, and leaching time of 150 min. Under the optimal conditions, the removal of lead is obtained as 87.67%. It is observed that the model obtained has nearly fitted the full second-order model, and the correlation coefficient calculated at 95% confidence level has a value of 0.97.

An empirical study of a gravitational air classifier

An empirical study was performed on a gravitational air classifier that is used in a Swedish quarry. The classifier works in two steps and is used to classify a VSI-crushed 0–2mm fraction into three different products for use in the concrete and asphalt industry. The study was performed using the statistical method of design of experiments (DOE), with which the influence of three different variable control parameters was investigated. The influence of these parameters on the separation cut size and air flow into the machine has thus been obtained. The results show that different parameters as well as control strategies should be applied depending on the quality and capacity requirements of the final products.

Influence of temperature, light and nutrients on the growth rates of the macroalga Gracilaria domingensis in synthetic seawater using experimental design

In the present study, the daily relative growth rates (DRGR, in percent per day) of the red macroalga Gracilaria domingensis in synthetic seawater was investigated for the combined influence of five factors, i.e., light (L), temperature (T), nitrate (N), phosphate (P), and molybdate (M), using a statistical design method. The ranges of the experimental cultivation conditions were T, 18–26°C; L, 74–162 μmol photons m−2 s−1; N, 40–80 μmol L−1; P, 8–16 μmol L−1; and M, 1–5 nmol L−1. The optimal conditions, which resulted in a maximum growth rate of ≥6.4% d−1 from 7 to 10 days of cultivation, were determined by analysis of variance (ANOVA) multivariate factorial analysis (with a 25 full factorial design) to be L, 74 μmol photons m−2 s−1; T, 26°C; N, 80 μmol L−1; P, 8 μmol L−1; and M, 1 nmol L−1. In additional, these growth rate values are close to the growth rate values in natural medium (von Stosch medium), i.e., 6.5–7.0% d−1. The results analyzed by the ANOVA indicate that the factors N and T are highly significant linear terms, X L, (α = 0.05). On the other hand, the only significant quadratic term (X Q) was that for L. Statistically significant interactions between two different factors were found between T vs. L and N vs. T. Finally, a two-way (linear/quadratic interaction) model provided a quite reasonable correlation between the experimental and predicted DRGR values (R 2adjusted = 0.9540).

Comparison of effective parameters for copper powder production via electrorefining and electrowinning cells and improvement using DOE methods

The influences of cupric ion concentration (5–35 g/L), current density (500–2000 A/m2), circulation rate of the electrolyte solution (15–120 mL/min), and temperature (25–60°C) on the physical and chemical properties of copper powders obtained in electrolysis cells were investigated. Two industrial processes, electrorefining (ER) cells with a synthetic electrolyte and electrowinning (EW) cells with an original solution of copper mineral leaching, were utilized to produce copper powders. Finally, the statistical full factorial method of design of experiments (DOE) was employed to investigate the interaction or the main effects of processes. The results show that increasing the copper concentration and temperature can increase the grain size, apparent density, and electrical energy consumption. On the other hand, increasing the current density and circulation rate of the electrolyte can decrease them. This production process is optimized via DOE to control the interactive and main effects to produce copper powders with favorable properties.