Application Of Design Of Experiments Research Articles

Application of Taguchi design of experiments in the food industry: a systematic literature review

The optimal utilization of available resources is critical for industrial experimentation. In addition, a structured approach with reliable and reproducible results and robust predictability is paramount to achieving profitability, waste reduction, and improvements in the food industry. The Taguchi design of experiments (DOE) is a practical approach that optimizes the critical characteristics using minimum time and resources – reviewing its application in the food sector assists in determining research gaps that could provide sustainable solutions to academia, industry, and policymakers. This research provides a systematic review of the literature on the Taguchi DOE application for the past two decades in the food industry. The systematic review considered articles published in peer-reviewed journals indexed in Scopus, Web of Science, and PubMed databases. The research categorized the relevant articles into three core themes and 31 sub-themes. This study identified only 31 relevant articles despite the significant advantages of applying the Taguchi DOE in the food industry. Most applications did not explore the true potential of the Taguchi DOE approach to obtain robust and scalable solutions. Also, the analysis determined that a structured approach is missing in most studies, with a lack of utilization of essential tools during the research.

Zinc-Phthalocyanine Loaded PLGA-PVA-Chitosan Nanosystem for the Enhancement of Antidiabetic Activity.

Zinc, one of the most common nutraceutical agents, proved to be effective for diabetes as it regulates the blood glucose level by inhibiting glucagon secretion. However, the hepatotoxicity of zinc creates necrosis, hepatic glycogen depletion, and apoptosis of hepatocytes at the concentration of 10 μg/kg. Phthalocyanine, a blue-colored compound, is an aromatic macrocyclic compound with good antioxidant ability owing to its heterocyclic nitrogen conjugation. The conjugation of zinc with phthalocyanine aimed to reduce the toxicity associated with zinc and enhance the antidiabetic activity at a lower dose. Hence, the present research work possessed the insights of the synthetic aspect of zinc with phthalocyanine along with its entrapment in the poly(lactic-co-glycolic acid) (PLGA)-chitosan nanosystem via oral administration in the treatment of diabetes. A nanoprecipitation technique was implemented for the synthesis of PLGA chitosan nanoparticles, and formulation was further optimized using a central composite design. Twenty trials provided by the software selected optimum concentrations of PLGA, poly(vinyl alcohol) (PVA), and chitosan in consideration with particle size up to 335.6 nm, zeta potential 27.87 mV, and entrapment efficiency of 75.67 ± 8.13%. Addition of chitosan to the nanocarrier system for controlling the release of the drug for 3 days was accompanied by the improvement in the glucose level within 28 days. The delivery of the nanoparticles showed enhancement in the cholesterol, triglyceride, alkaline phosphatase (ALP), urine parameters, and pro-inflammatory cytokines. The application of DoE (design of experiments) for the optimization of the nanoparticles established a controlled release formulation for diabetes, which displayed safety and effectiveness in streptozotocin (STZ)-induced diabetic rats.

Data-Driven Approach to Analyze Interdependencies between Electrode Manufacturing Parameters and Electrochemical Performance of the Lithium-Ion Battery Cell

The lithium-ion battery cell production is a complex process chain with a high number of process steps and manifold interdependencies. The majority of the properties determining the electrochemical performance of the battery cell are established during electrode manufacturing. An in-depth understanding of electrode manufacturing and its influence on cell performance can pave the way toward high-quality, cost-efficient cell production. Considering the complexity of the process chain and the high share of material costs in battery production, the classic one-factor-at-a-time experimentation is deemed inadequate and expensive.Machine learning (ML) approaches have been proven suitable for analyzing complex process chains. Design of experiment (DoE) methods can be used to generate a sufficient informative database for ML techniques. This study was based on the joint application of DoE and ML methods in electrode manufacturing. For this purpose, in the first step, the response surface methodology (RSM) was used to conduct experiments and analyze the effect of mass loading, drying temperature, and porosity on cell performance. The study was focused on graphite anodes produced at a pilot scale. The electrochemical performance of electrodes was assessed on coin cells at discharge C-rates from C/10 to 5C. Additionally, symmetric coin cells were used to analyze the electrochemical impedance spectra. Supervised machine learning models were adopted to predict the cell characteristics based on the electrode properties. Additionally, a factor analysis reflecting the contribution and importance of each factor on the final cell properties was conducted. The efficient experiment-based data generation using RSM combined with the applied ML techniques can support practitioners towards smart quality-oriented battery cell production.

Direct Ink Writing of Alginate–Gelatin Hydrogel: An Optimization of Ink Property Design and Printing Process Efficacy

Direct Ink Writing (DIW), which is widely used for developing functional 3D scaffolds that have robust structural integrity for the growth of target tissues/cells, has emerged as an appealing method for biomedical applications. The production of 3D structures involves three separate but interconnected stages (material development, printing process, and post-printing treatment), whose effectiveness is influenced by several factors that therefore make it challenging to optimize the entire procedure. By studying the material processability and leveling the printing settings, this study proposes a three-step method to enhance the ink property design and the printer’s performance. The recommended approach is focused on the thorough study of alginate–gelatin hydrogel properties, which is a commonly used ink in biomedical applications, due to its natural origin through marine flora, as well as the development process parameters and their intercorrelations. Principal Component Analysis in comparison with K-means clustering was applied to reveal material properties that are highly correlated with additive manufacturing (AM) processability, and Taguchi’s Design of Experiments (DOE) determined the printing settings (primary and secondary) for achieving optimum printing accuracy. PCA results were affirmed by K-means clustering and showed that viscosity, m, G′ and G″ govern blends’ printing behavior while application of DOE led to 85% pore area printability.

Design of experiments in the optimization of nanoparticle-based drug delivery systems.

Design of experiment (DoE) is a powerful statistical technique used for variable screening and optimization. It is based on the simultaneous variation of multiple factors with the objective of finding the configuration of parameters that optimizes one or more outputs of interest, while using the minimal number of experimental runs required for testing, resulting very cost and time-efficient. Despite the high potential offered by this approach for innovation and process optimization, DoE is still only marginally applied in the field of nanomedicine and often its rationale application and analysis result is difficult to grasp by many. In this review, we discuss some of the latest applications of DoE in the formulation of nanovectors used for drug delivery across many different applications. First, we introduce general principles of DoE to the reader, which are indispensable to understand the works we report. Then, we give particular attention to the process variables, the specific designs, and the readouts used for process analysis and optimization for different classes of nanovectors. Finally, we try to delve into the current shortcomings of DoE application and possible future directions that could be employed to further improve the information that can be derived from this approach.

Is designed data collection still relevant in the big data era? – A discussion

Is designed data collection still relevant in the big data era? – A discussion

Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning.

Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.

Application of design of experiments for development and characterization of coprocessed excipients using Lapidium Sativum

The development of novel excipients with enhanced functionality has been explored using particle engineering by co-processing. The aim of this study was to improve the functionality of Lapidium Sativum for direct compression by co-processing with MCC pH 101 and PVP K30 in optimized proportions. Design of Experiment (DoE) was employed to optimize the composition of the co-processed excipient using the desirability function and other supporting studies as a basis for selecting the optimized formulation. The co-processed excipient was thereafter developed by the method of spraydrying. Flow and compaction studies of coprocessed excipient were carried out in comparison to its parent component and physical mixture. Tablets were prepared by direct compression (DC) containing Venlafaxine hydrochloride (100 mg) as a model for poor compressibility. Tablets produced with CPE were satisfactory and conformed to USP specifications for acceptable tablets. The application of DoE was successful in optimizing and developing a starch-based co-processed excipient that can be considered for direct compression tableting.

Assessment of the Influence of Additives on the Mechanical Properties and Machinability of Al-11%Si Cast Alloys: Application of DOE and ANOVA Methods.

In the present study, the statistical design of experiments (DOE) method was applied to study and control the properties of near-eutectic Al-11%Si alloys. In this study, we developed regression equations between response variables, including hardness, yield stress, ultimate tensile stress, elongation, total cutting force, cutting power, and tool life, and varying factors which included the percentage of the alloying element in the composition and the modification level. These equations may be analyzed quantitatively to acquire an understating of the effects of the main variables and their interactions on the mechanical behavior and the machinability of the alloy under investigation. Analysis of variance (ANOVA) was performed to verify the fit and adequacy of the developed mathematical models. The results show that increasing the levels of Cu and Fe results in an increase in hardness, yield stress and ultimate tensile strength in both modified and non-modified alloys. On the other hand, both Cu and Fe appear to affect the elongation adversely, whereas the Sr level shows a positive effect on the elongation percentage. We found that the Sr level had the most significant effect on the cutting forces and cutting power, followed by Fe and Cu contents.

Design of Experiment: A Rational and Still Unexplored Approach to Inorganic Materials’ Synthesis

This review was devoted to outlining the use and potential increasing application of the Design of Experiment (DoE) approach to the rational and planned synthesis of inorganic nanomaterials, with a particular focus on polycrystalline nanostructures (metal and alloys, oxides, chalcogenides, halogenides, etc.) produced by sustainable wet chemistry routes based on a multi-parameter experimental landscape. After having contextualised the stringent need for a rational approach to inorganic materials’ synthesis, a concise theoretical background on DoE is provided, focusing on its statistical basis, shortly describing the different sub-methodologies, and outlining the pros and cons of each. In the second part of the review, a wider section is dedicated to the application of DoE to the rational synthesis of different kinds of chemical systems, with a specific focus on inorganic materials.

Design of Experiments and machine learning for product innovation: A systematic literature review

AbstractThe recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

Critical Evaluation into the practical utility of the Design of Experiments

Abstract The research aims to emphasise the relevance of the Design of Experiments (DOE) technique as a reliable method for ensuring efficient use of statistical methods in routine industrial processes. A case study approach with a deductive strategy was used to assess the effectiveness of different DOE methods to achieve the desired objectives. Screening, mid-resolution and high-resolution DOE methods helped identify, characterise, and optimise an experimental variable against the desired output response. A general framework for effective DOE is provided as part of DOE planning, including defining DOE objectives, selection criteria, noise reduction, and application across industries. Overall, various DOE models proved successful in identifying a complicated relationship between experimental variables and output response. However, when ideal DOE models may not be feasible, reducing test run by choosing lower resolution DOE or fewer replicates can still provide important insights into the experimental variables’ impact on output responses.

A Two-Step SO3H/ICG Catalyst Synthesis for Biodiesel Production: Optimization of Sulfonation Step via Microwave Irradiation

Conventional heating, a common method used for heterogeneous solid acid catalyst synthesis unknowingly consumes massive time and energy. In this study, acid catalyst was prepared through sulfonation process of incomplete carbonized glucose (ICG) via microwave-assisted technique to shorten the heating time and energy consumption. Optimization of the sulfonation process of ICG via microwave-assisted was carried out. Four-factor-three-level central composite design (CCD) was used to develop the design of experiments (DOE). Interaction between two factors was evaluated to determine the optimum process conditions. A quadratic model was proposed for prediction of biodiesel yield (Y) from palm fatty acid distillate (PFAD) and its conversion (C). The application of DOE successfully optimized the operating conditions for the two-step SO3H/ICG catalyst synthesis to be used for the esterification process. The optimized conditions of the best performing SO3H/ICG with maximum Y and C were at 7.5 minutes of reaction time, 159.5 mL of H2SO4 used, 671 rpm of stirring rate as well as 413.64 watt of power level. At these optimum conditions the predicted yield percentage and conversion percentage were 94.01% and 91.89%, respectively, which experimentally verified the accuracy of the model. The utilization of sulfonated glucose solid acid catalyst via microwave-assisted in biodiesel production has great potential towards sustainable and green method of synthesizing catalyst for biodiesel. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Design of experiments in the service industry: results from a global survey and directions for further research

PurposeThe purpose of this study is to critically evaluate the current status of Design of experiments (DoE) in the service industry or non-manufacturing processes in the manufacturing sector. This involves the identification of critical success factors and challenges in the use of DoE in the context of the service industry as well as the key skills needed for its successful applications.Design/methodology/approachIn order to identify the critical success factors and challenges, an online survey instrument was developed and distributed across Six Sigma Black Belts (SSBB), Six Sigma Master Black Belts (SSMBB), process improvement champions, continuous improvement professionals, DoE experts and specialists as well as academics who are involved in DoE research and applications.FindingsA total of 109 participants from over 20 countries responded to the study. One of the first findings of the study is that over 40% of the process improvement professionals in the service sector do not have any hands-on experience with the application of DoE. Further, this study highlights the following critical factors for successful DoE applications: dedicating adequate resources in terms of time, budget and people for the experiment; getting a clear understanding of the problem at hand; securing a reliable and validated measurement system for measuring the response or quality characteristics of interest; and identifying input independent variables, which influence the quality characteristics during the brainstorming stage. Some respondents also mention the importance of having training or education in DoE as a critical success factor. At the same time, the lack of adequate planning to experimentation, mathematical knowledge and statistical reasoning remains as challenge to be solved.Research limitations/implicationsAs data were collected at an individual level, inter-reliability, which measures the consistency of judgements on a particular survey item, could not be gauged. Moreover, due to low sample size across some continents, an intercontinental comparative study on the extent of the application of DoE across various continents could not be performed.Originality/valueTo the best of the authors’ knowledge, this is the first global survey carried out to understand the status of DoE in the service industry. The results of the global study can be used to encourage quality, continuous improvement and process improvement professionals in the service sector to drive wider acceptance of one of the most powerful tools in the Six Sigma toolkit for problem-solving scenarios. Moreover, senior managers in the service industry can take away some of the key benefits, challenges and success factors in the implementation of DoE.

Hybrid Methodology for Efficient on the Fly (Re)Parametrization of Proton Exchange Membrane Fuel Cells Electrochemical Model for Diagnostics and Control Applications

Prevention and diminution in effects of degradation phenomena under highly dynamic operation of proton exchange membrane fuel cells (PEMFC), while retaining sufficiently high efficiency and performance of the system, are considered significant obstacles toward their wider use in transport applications. Overcoming these obstacles calls for precise on-line monitoring and control tools such as coupled observers, which enable combined performance and service life optimizations. Models used in these applications should feature low computational effort, good extrapolation capabilities and should be easy to parametrize. The first requirement, which is obvious for on-line applications, in frequently in contradiction to the last two, which are heavily intertwined. Good extrapolation capabilities of the model significantly reduce the size of experimental data sets needed for successful parametrization, thus enabling the parametrization on small data sets, while retaining higher accuracy outside of trained variation space. This rationale motivates the use of the computationally-fast reduced-dimensionality electrochemical models e.g. [1, 2], featuring a more profound mechanistic basis; thus, exhibiting better prediction capability of the model.To present significant progress in the aforementioned area, this contribution presents framework of computationally-fast electrochemical models that can be parametrized whether on experimental data from polarization curves or on data obtained by electrochemical impedance spectroscopy (EIS). This hybrid methodology consists of interchangeable thermodynamically consistent reduced-dimensionality electrochemical model for PEMFC [2] and newly developed analytical physical model for impedance spectra with the same set of calibration parameters. The latter is developed by applying electrochemical ansatzes for the cathode and anode reactions derived in [2] on the proposed modelling approach used for calculation of the cathode catalyst layer impedance in the inspiring work of Kulikovsky [3]. Profound modelling basis using the same set of calibration parameters enable enhanced identification of individual calibration parameters that are otherwise harder to be uniquely determined [4] on one hand, and enable parametrization of the model from either of the two separate measurements in time and frequency domains on the other.Enhanced identifiability of calibration parameters using proposed hybrid methodology is especially pronounced in the case of parameters describing anodic and cathodic reaction rates and their activation energies, which are extremely hard to be uniquely determined based on the experimental data consisting of polarization curves only, due to coupling of their information with other calibration parameters. Additionally, proposed hybrid methodology also enables a significant reduction in the measurement time in the case of experimental data obtained by EIS, allowing the low frequency regions, where the transport phenomena could be otherwise characterized, to be omitted due to already uniquely determined calibration parameters from polarization curve measurements. Furthermore, to reduce the necessary amount of experiments for successful parametrization of the model even further, the optimal design of experiments (DoE) is used, based on D-optimal criterion applied onto Fisher information matrix.To assess developed hybrid methodology calibration parameters, obtained by means of minimizing penalty function value with differential evolution algorithm on EIS data, are used for validation against polarization curves measured at the same RuL (remaining useful life). Furthermore, to confirm applicability of the methodology, the same procedure is also used for calibration on the measured polarization curves and validation on the EIS experimental data. Small discrepancy between the values of the root-mean-square errors show plausibility of the proposed hybrid methodology. On the other hand, Fisher information analysis is performed to assess increment in the information about individual calibration parameter when each experimental point from EIS data set is added to experimental space used for parametrization.Results of this joint analysis offer unique insight into the information that data set obtained with the EIS measurements possess about individual calibration parameter in the individual frequency interval and intuitively confirm the results obtained by application of the optimal DoE for a synthetic space of EIS measurements. The latter extremely reduces experimental workload needed for successful calibration of the model in comparison with the full factorial. Innovative hybrid methodology of the measurements in time and frequency domains can thus be used for achieving reduced costs and efforts in initial parametrization and during operation in re-parametrization procedures of the modelling framework for virtual observers.

An interdisciplinary review on calibration strategies of engine management system for diverse alternative fuels in IC engine applications

As the vehicle technology evolves and becomes more complex, the ability to monitor certain uncontrollable factors is possible through various engine management system calibration methods. Additionally, different optimization approaches assist in improving engine output characteristics. Advances in fuel production from unique feedstocks have paved a way for the introduction of innovative flex-fuel engines. These engines are made multi-fuel adaptive by utilizing appropriate calibration and optimization approaches. Since numerous researches have been carried out in internal combustion engine calibration, operating on alternative fuels, it is quite hard to maintain a record for this wealth of information. Therefore to bridge this issue, this study intends to organize and provide the reader with a retrospective view of selective studies undergone in the field of engine calibration using different tuning techniques. The objective of this study is to assist engine calibration enthusiasts on selecting an apt engine calibration technique for their application which can be flexible for multiple fuel types. Studies revealed that model-based calibration assisted by Gaussian process modelling and Design of Experiment application is adequate for calibrating an engine operating on alternative fuels. Additionally, calibration methodologies followed for certain fuel types and compositions are highlighted in detail. Furthermore, an advanced calibration methodology is proposed for flex-fuel engines in this study based on the knowledge gained through the preparation of this article. To further enhance accuracy, complex optimizers like neural networks can be used in the proposed methodology to initiate real-time calibration for ideal flex-fuel engine output responses.

Application of DOE and ANOVA in Optimization of HVOF Spraying Parameters in the Development of New Ti Coatings

High velocity oxygen fuel (HVOF) thermal spray technique has been used to develop new Ti coatings on 1045 steel and 316L stainless steel for different applications. Optimization of the HVOF parameters requires numerous experiments to perform that can be reduced using the Taguchi Design of Experiment (DOE) methodology. By using DOE, it has been possible to identify the effects of the HVOF spraying parameters (spraying distance, number of layers, gun speed, powder feed rate, type of substrate and type of combustion) on the main characteristics of the coatings (porosity, thickness, hardness and adhesion). According to Taguchi method, the resulting orthogonal matrix corresponded to a L16 (44 × 22) matrix. Using this matrix, the number of experiments was reduced from 1024 to 16 and a first approximation of the best conditions for a real application was obtained. To evaluate the significant spraying variables, a statistical analysis of variance (ANOVA) was used. It has been determined that there is a relationship between coating characteristics and HVOF parameters. Also, the influence of the parameters on the characteristics and properties of the coatings (from high to low) is as follows: spraying distance, number of layers, gun speed, powder feed rate, type of substrate and mixture of gases used in the process.

Application of design of experiments in the development of cosmetic formulation based on natural ingredients

Development of cosmetic formulations based on natural ingredients presents a challenge in order to instability and sensory limitations of raw ingredients. Organogel which is a cold emulsifier and offers refined sensory properties, physical-chemical characteristics and stability could be solved this limitation. The aim of this study is to develop cosmetic formulations by cold process containing natural ingredients and assess through design of experiment (DOE) the influencing factors on the physical-mechanical and sensory properties of these formulations. Physico-mechanical and sensory properties of cosmetic formulations containing natural ingredients obtained by cold process were evaluated. The DOE was performed for the texture analyses. In addition, the influence of natural actives substances in the developed formulations was assessed. The different concentrations of polymers, organogel and active substances influenced the texture profile of the formulations, being the concentration of organogel the most influential factor. The formulation containing sunflower oil, a polymer from a natural source and the highest concentration of organogel influenced the firmness, consistency, cohesiveness and viscosity. The work of shear decreased in the formulation added of the polymer and increased when the formulation was supplemented with active substances. The sensory analysis showed that the formulation based on 5% of organogel, sunflower oil and polymer it was preference for the selected formulation. In conclusion, the application of DOE for the rational development of cosmetic formulations based on natural ingredients led to formulations with physico-mechanical and sensory properties suitable for the application on the skin.

Efficient EMC Parameter Analysis for the Verification of Complex Automotive Simulation Models by the Utilization of Design of Experiments

Virtual electromagnetic compatibility (EMC) analysis improves the validation processes in the automotive industry by mitigating risks in early development stages. Detailed knowledge about adjustable parameters to improve the compatibility can greatly simplify and optimize the EMC of an entire system. The application of the quality tool design of experiment (DoE) to EMC simulations combines the statistical analysis of influencing parameters with the reduction and optimization of the simulation efforts. DoE is a toolbox used to determine the effect and interactions of multiple input parameters on a desired target value by varying all input parameters simultaneously and effectively. The DoE application in virtual EMC validation processes reduces the simulation efforts of full factorial optimization problems and leads to a clear understanding of the most influencing parameters, parameter strengths, and parameter interactions. The application of DoE is presented for complex screens of high-voltage cables as well as for high-voltage subsystems.

The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production.

The production of high yields of soluble recombinant protein is one of the main objectives of protein biotechnology. Several factors, such as expression system, vector, host, media composition and induction conditions can influence recombinant protein yield. Identifying the most important factors for optimum protein expression may involve significant investment of time and considerable cost. To address this problem, statistical models such as Design of Experiments (DoE) have been used to optimise recombinant protein production. This review examines the application of DoE in the production of recombinant proteins in prokaryotic expression systems with specific emphasis on media composition and culture conditions. The review examines the most commonly used DoE screening and optimisation designs. It provides examples of DoE applied to optimisation of media and culture conditions.