Journal articles: 'Design of Experiement (DOE)'

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Goodman, John, and David C. Wyld. "The hunt for the Red X: a case study in the use of Shainin design of experiement (DOE) in an industrial honing operation." Management Research News 24, no. 8/9 (August 2001): 1–17. http://dx.doi.org/10.1108/01409170110782919.

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Laird, Trevor. "Design of Experiments (DoE)." Organic Process Research & Development 6, no. 4 (July 2002): 337. http://dx.doi.org/10.1021/op0255581.

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Muttaqin, Benazir Imam Arif. "Telaah Kajian dan Literature Review Design of Experiment (DoE)." Journal of Advances in Information and Industrial Technology 1, no. 1 (November 1, 2019): 33–40. http://dx.doi.org/10.52435/jaiit.v1i1.10.

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Ketika kita merancang suatu eksperimen, dibutuhkan suatu perencanaan yang baik agar hasil eksperimen dapat valid, efisien, dan secara statistikal dapat dipertanggungjawabkan dengan baik. Salah satu konsep kajian yang sangat banyak digunakan dan dikembangkan di dalam eksperimen adalah Design of Experiment (DoE). Di dalam DoE terdapat serangkaian tahapan/prosedur yang harus dilaksanakan sebelum peneliti melakukan suatu eksperimen. Konsep DoE pertama kali diperkenalkan oleh Sir Ronald A. Fisher pada tahun 1930. Sampai saat ini telah banyak teknik-teknik DoE yang berkembang dan digunakan untuk memecahkan berbagai macam persoalan di berbagai bidang. Artikel ini menghasilkan telaah kajian DoE mulai dari konsep, latar belakang kemunculan, dan isu-isu terkait penggunaan DoE serta literature review tentang perkembangan konsep, teknik, dan contoh penggunaan DoE selama tiga tahun terakhir.

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Cattarinussi, Massimo, Anton Leppänen, Juho Könnö, and Tero Frondelius. "Cylinder Head Design of Experiment by using the Wartsila Digital Design Platform." Rakenteiden Mekaniikka 50, no. 3 (August 22, 2017): 330–32. http://dx.doi.org/10.23998/rm.64923.

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Cylinder Head Design of Experiment (DOE) was carried out by using the Wartsila digital design platform. The overall goal has been to create a simulation process to perform a complete cylinder head thermal-structural calculation and create a DOE over that process to vary the inputs, the geometrical parameters under optimization, in a fully automatic workflow. The outputs, uploaded to the DOE activity, were used to rank the input effects on the output response, to capture response sensitivity to input changes, to identify parameter interactions and, at the end, to estimate the optimal design.

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Kowang, Tan Owee, and Choi Sang Long. "Design of Experiment – An Integration of Fisher, Taguchi and Shainin DOE Methodology." Applied Mechanics and Materials 789-790 (September 2015): 1201–6. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.1201.

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Design of experiment (DOE) is the focal collective of quality technique and is widely used in Research and Development (R&D) organization to optimize product design parameters. There are three main DOE approaches that commonly adopted by R&D companies, which are the classical Fisher’s DOE, Taguchi DOE and Shainin DOE. This paper explores how these three main DOE approaches could be integrated to optimize product design parameters in a Research and Development company. The paper starts with enlightenment of the common DOE steps, follows by discussion of issues faced by the Research and Development company during the implementation of the three DOE approaches in product design. The paper subsequently illustrates how the three DOE approaches could be integrated as a new methodology for optimization of product design parameters. The integrated DOE methodology presented in this paper offers an alternative and effective way to conduct DOE in a dynamic product design and development environment.

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Sharma, Mithun, and Shilpi Sharma. "Critical Evaluation into the practical utility of the Design of Experiments." Engineering Management in Production and Services 13, no. 3 (September 1, 2021): 50–65. http://dx.doi.org/10.2478/emj-2021-0021.

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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.

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JOHNSON, JEFF. "DOE DEFENDS "HOT" REPOSITORY DESIGN." Chemical & Engineering News Archive 82, no. 22 (May 31, 2004): 19–20. http://dx.doi.org/10.1021/cen-v082n022.p019.

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Costa, Nuno. "Design of experiments – overcome hindrances and bad practices." TQM Journal 31, no. 5 (October 10, 2019): 772–89. http://dx.doi.org/10.1108/tqm-02-2019-0035.

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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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Yarn, Kao Feng, Wen Chung Chang, and Wei Ching Chuang. "Analyses of New Diffraction Optics Element Design by Polymer Gratings." Materials Science Forum 663-665 (November 2010): 389–92. http://dx.doi.org/10.4028/www.scientific.net/msf.663-665.389.

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A new diffraction optics element (DOE) which combines semiconductor laser and polymer-based DOE processing technology into a single optoelectronic polymer device is proposed. To directly combine the VCSEL (Vertical-Cavity Surface-Emitting Laser) with DOE, DOE surface profile can be integrated on the top of VCSEL utilizing polymer gratings by soft-lithography technique. Such a new kind of polymer-based DOE-VCSEL can control spatial distribution of emitting laser energy and output angle. Through the innovative development, it can be predicted that the optical loss and traveling distance in DOE system are decreased due to the shrinking the device volume by this new design.

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Stojanović, Jevrem, Jovana Krmar, Ana Protić, Bojana Svrkota, Nevena Đajić, and Biljana Otašević. "Experimental design in HPLC separation of pharmaceuticals." Arhiv za farmaciju 71, no. 4 (2021): 279–301. http://dx.doi.org/10.5937/arhfarm71-32480.

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Design of Experiments (DoE) is an indispensable tool in contemporary drug analysis as it simultaneously balances a number of chromatographic parameters to ensure optimal separation in High Pressure Liquid Chromatography (HPLC). This manuscript briefly outlines the theoretical background of the DOE and provides step-by-step instruction for its implementation in HPLC pharmaceutical practice. It particularly discusses the classification of various design types and their possibilities to rationalize the different stages of HPLC method development workflow, such as the selection of the most influential factors, factors optimization and assessment of the method robustness. Additionally, the application of the DOE-based Analytical Quality by Design (AQbD) concept in the LC method development has been summarized. Recent achievements in the use of DOE in the development of stability-indicating LC and hyphenated LC-MS methods have also been briefly reported. Performing of Quantitative structure retention relationship (QSRR) study enhanced with DOE-based data collection was recomended as a future perspective in description of retention in HPLC system.

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Gradeci, K., and M. Sletnes. "The Hybrid-Agile Design of Experiments Methodology." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012039. http://dx.doi.org/10.1088/1742-6596/2069/1/012039.

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Abstract A DOE (Design of Experiments) is the laying out of a detailed experimental plan in advance of doing the experiment. Optimal DOEs maximize the amount of information that can be obtained for a given amount of experimental effort. The traditional DOE methodology is waterfall-type methodology implying a sequential and linear life-cycle process. The success of the experiment and usefulness of the results are highly dependent on the initial experimental setup and assumptions, and does not allow to go back and change something that was not well-documented or thought upon in the design stage. The fast-changing software development industry have made it understandable that the traditional waterfall methodology for developing systems, which follows similar patters to the traditional DOE, lacks the agility required for developing robust systems. These limitations have triggered the development of agile: a type of incremental model of software development based on principles that focuses more on flexible responses to change, instead of in-depth planning at the design stage. This paper proposes the hybrid-agile DOE methodology – a methodology that incorporates agile principles in traditional waterfall DOE methodologies – to design effective experimental layouts that allow for improvement during the experimental trial process. The methodology is applied to the natural ageing of adhesives tapes for building applications. This methodology can overcome traditional DOE, by adding agility in the whole process, especially in cases where the investigated products lack prior information and are characterised by large variability.

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Eckert, Tilman, Florian C. Klein, Piet Frieler, Oliver Thunich, and Volker Abetz. "Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)." Polymers 13, no. 18 (September 17, 2021): 3147. http://dx.doi.org/10.3390/polym13183147.

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Despite the great potential of design of experiments (DoE) for efficiency and plannability in academic research, it remains a method predominantly used in industrial processes. From our perspective though, DoE additionally provides greater information gain than conventional experimentation approaches, even for more complex systems such as chemical reactions. Hence, this work presents a comprehensive DoE investigation on thermally initiated reversible addition–fragmentation chain transfer (RAFT) polymerization of methacrylamide (MAAm). To facilitate the adaptation of DoE for virtually every other polymerization, this work provides a step-by-step application guide emphasizing the biggest challenges along the way. Optimization of the RAFT system was achieved via response surface methodology utilizing a face-centered central composite design (FC-CCD). Highly accurate prediction models for the responses of monomer conversion, theoretical and apparent number averaged molecular weights, and dispersity are presented. The obtained equations not only facilitate thorough understanding of the observed system but also allow selection of synthetic targets for each individual response by prediction of the respective optimal factor settings. This work successfully demonstrates the great capability of DoE in academic research and aims to encourage fellow scientists to incorporate the technique into their repertoire of experimental strategies.

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Manlig, František, and František Koblasa. "Design of Simulation Experiments Using DOE." Applied Mechanics and Materials 693 (December 2014): 219–24. http://dx.doi.org/10.4028/www.scientific.net/amm.693.219.

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Computer simulation becomes an essential tool improving the efficiency of business processes, due its ability to mimic the behavior of the simulated processes. However, Its use is not trivial. Simulation is not “only” about model design. Planning and implementation of simulation experiments are equally important. Manufacturers of the simulation software are aware of that and they provide support also in this area such as integrated heuristics algorithms. There are other options how to improve process of experimentations and one of them is methodology of DOE (Design of experiments).This article is focusing on mentioned area of planning of the simulation experiments using DOE and it shows gained experiences on particular example. This article describes design of the experiment, how to select main factors (their influence and influence of their interaction) and experimentation itself using SW Minitab. Further there are presented experiment results given by simulation models. There are briefly discussed benefits and disadvantages of this approach.

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Vakrilov, Nikolay, and Anna Stoynova. "Thermal Design of LED by DOE." Electrical and Electronics Engineering: An International Journal 4, no. 4 (November 30, 2015): 1–10. http://dx.doi.org/10.14810/elelij.2015.4401.

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Rodriguez-Granrose, Daniel, Amanda Jones, Hannah Loftus, Terry Tandeski, Will Heaton, Kevin T. Foley, and Lara Silverman. "Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement." Bioprocess and Biosystems Engineering 44, no. 6 (February 27, 2021): 1301–8. http://dx.doi.org/10.1007/s00449-021-02529-3.

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AbstractModern bioprocess development employs statistically optimized design of experiments (DOE) and regression modeling to find optimal bioprocess set points. Using modeling software, such as JMP Pro, it is possible to leverage artificial neural networks (ANNs) to improve model accuracy beyond the capabilities of regression models. Herein, we bridge the gap between a DOE skill set and a machine learning skill set by demonstrating a novel use of DOE to systematically create and evaluate ANN architecture using JMP Pro software. Additionally, we run a mammalian cell culture process at historical, one factor at a time, standard least squares regression, and ANN-derived set points. This case study demonstrates the significant differences between one factor at a time bioprocess development, DOE bioprocess development and the relative power of linear regression versus an ANN-DOE hybrid modeling approach.

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Zou, Jie Hui, and Ju Gen Nie. "Optimal Design of Safety Valve Spring." Key Engineering Materials 474-476 (April 2011): 102–7. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.102.

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In this paper, it aiming at the functional features of containerization canning safety valves adopts the method of design of experiments (DOE) to conduct fast modeling on the working conditions of the safety valve spring, and cites the means of multi-objective optimization to optimize the spring designs, minimizing the maximum inner shear stresses of its winding coils and its weights. In the process of the study, a parametric finite-element spring model with three variable parameters has been used to generate arbitrary spring shapes within the pre-set scope of spring shapes.By the V-Optimal method, it generates a DOE input matrix with 30 input points. It obtains a two-third-order polynomial regression model through analyses and calculations, which can fully reflect the regression characteristics of the shear stresses and the weights of springs. This study shows that by means of DOE and multi-objective optimization, it enables to quickly design and optimize on safety valve springs.

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Antony, J., S. Coleman, D. C. Montgomery, M. J. Anderson, and R. T. Silvestrini. "Design of experiments for non-manufacturing processes: benefits, challenges and some examples." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 225, no. 11 (September 21, 2011): 2078–87. http://dx.doi.org/10.1177/0954405410395857.

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Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments.

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Park, Young Chul, Jung Ho Kang, Dong Hwa Lee, Seung Hwan Oh, Won Deg Ko, and Kwon Hee Lee. "Shape Optimization of a Knuckle using the Design of Experiments." Key Engineering Materials 345-346 (August 2007): 905–8. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.905.

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DOE (design of experiments) was applied to the design of a knuckle as a part of a suspension system. Specifically, knuckle made of aluminum alloy was optimized considering the strength. On the other hand, design variables were set as shape variables. During structural optimization using DOE, an orthogonal array strategy was developed to determine the optimum design. The relevant discrete variables were treated as levels. Since the conventional orthogonal array did not consider the constraint, however, the characteristic function was defined to include the effect of constraint feasibility. The general DOE was expanded to include problems with constraints related to the new characteristic function.

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N. Politis, Stavros, Paolo Colombo, Gaia Colombo, and Dimitrios M. Rekkas. "Design of experiments (DoE) in pharmaceutical development." Drug Development and Industrial Pharmacy 43, no. 6 (February 23, 2017): 889–901. http://dx.doi.org/10.1080/03639045.2017.1291672.

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Kusiak, A., and Chang-Xue Feng. "Robust Tolerance Design for Quality." Journal of Engineering for Industry 118, no. 1 (February 1, 1996): 166–69. http://dx.doi.org/10.1115/1.2803639.

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Design of a product (process) includes system design, parameter design, and tolerance design. Robust design is closely applicable to parameter design and tolerance design. The current literature on robust design has focused on parameter design while the problem of tolerance design has not been adequately covered. The tolerance design literature emphasizes the use of optimization to minimize cost while little attention has been paid to minimizing the sensitivity of tolerances to the variation of manufacturing processes. This paper discusses the application of the design of experiments (DOE) approach to tolerance synthesis to minimize manufacturing variations in a probabilistic case. The DOE approach is illustrated with an example.

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Uhoraningoga, Albert, Gemma Kinsella, Gary Henehan, and Barry Ryan. "The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production." Bioengineering 5, no. 4 (October 19, 2018): 89. http://dx.doi.org/10.3390/bioengineering5040089.

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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.

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Bhemuni, V. P., and S. R. Chalamalasetti. "A Review on Hard Turning by using Design of Experiments." Journal for Manufacturing Science & Production 13, no. 3 (October 25, 2013): 209–19. http://dx.doi.org/10.1515/jmsp-2013-0007.

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AbstractHard turning is the process of machining hardened steels that are above 45HRC. In manufacturing, hard turning has emerged as a potential area because of its many advantages over grinding as a finishing operation. In the present paper, a review is done on Design of Experiment (DOE) techniques that have been employed on various hard turning processes by researchers. DOE is one of the widely used methods for experimental study of many manufacturing processes in engineering. It is a statistical approach in which a mathematical model is developed through a minimum of experimental runs. DOE predicts the possible output, based on the given input parameters in an experiment. The present study predominantly focuses on the usage of Response Surface Method, Taguchi’s method and Factorial method in hard turning.

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Rozum, Ondrej, František Zahálka, Michaela Kašparová, and Šárka Houdková. "Design of Experiments in the Branch of Thermal Spraying." Key Engineering Materials 465 (January 2011): 137–40. http://dx.doi.org/10.4028/www.scientific.net/kem.465.137.

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High quality methods of designing the experiments (DOE) seem to be a very useful exploratory tool at many points of material engineering. Designing the experiments is a natural way to find a systematic influences in the process. It is necessary to have a clear view on how the things are to have the processes under controll. Therefore the importance of DOE increases in management and industry all over the world. In this work, a general use in branch of thermal spraying is shown.

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Behmaneshfar, Ali, Abdolhossein Sadrnia, and Hassan Karimi-Maleh. "A Review of Different Types of DOE Methods as a Useful Platform for Improving the Performance of Nano Adsorbents in Removal Systems of Pollutants." Nanoscience & Nanotechnology-Asia 10, no. 3 (June 17, 2020): 219–27. http://dx.doi.org/10.2174/2210681209666190220130002.

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Background: In recent years, the Design of Experiments (DOE) is used for removing pollutant from wastewater by nano-adsorbent. Some methods are Taguchi, Response Surface Methodology (RSM) and factorial design. The aim of this paper is to review different used methods of DOE in removing pollutant to suggest some notations to scholars. Methods: The reviewed papers were searched in Google Scholar, Scopus, and Web of Science randomly and categorized based on DOE methods. Results: Number of factors and responses in DOE for removing pollutants from wastewater are between 2-6 and 1-4, respectively. There are several computer software programs that provide simple use of these methods, such as Qualitek, Design Expert, Minitab, R and Matlab Programming. All models have a coefficient of determination R-sq more than 0.9. Conclusion: All the mentioned methods are appropriate because of the high R-sq value. Since the largest number of runs are used in RSM, it is not suitable for the experiments which are conducted by expensive materials and process. Furthermore, Design Expert and Minitab are the most popular software used by scholars in DOE methods for the removal of pollutant.

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Umar, Sarehati, Norhisham Bakhary, and Airil Yasreen Mohd Yassin. "Comparative Study on Design of Experiment in Frequency-Based Response Surface Methodology for Damage Detection." Applied Mechanics and Materials 735 (February 2015): 168–73. http://dx.doi.org/10.4028/www.scientific.net/amm.735.168.

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This paper investigates the performance of design of experiment (DOE) in response surface methodology (RSM) for vibration-based damage detection. The ability of three major types of DOE, namely central composite design (CCD), Box-Behnken (BBD) and D-optimal (Dopt) for damage detection based on modal frequency are investigated and compared. A procedure comprising three main stages—sampling, response surface (RS) modelling and model updating—are employed for damage localisation and quantification. By considering Young’s modulus and modal frequency as respective input and output, a set of samples is generated from each DOE. Full quadratic functions are considered in RS modelling while model updating is performed for damage detection. The performances of DOE are compared based on damage detectability. A numerical simply supported beam is used as case study by considering several single damage cases. The results show that CCD provides better prediction compared to other DOEs.

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Hancu, Gabriel, Serena Orlandini, Lajos Attila Papp, Adriana Modroiu, Roberto Gotti, and Sandra Furlanetto. "Application of Experimental Design Methodologies in the Enantioseparation of Pharmaceuticals by Capillary Electrophoresis: A Review." Molecules 26, no. 15 (August 2, 2021): 4681. http://dx.doi.org/10.3390/molecules26154681.

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Chirality is one of the major issues in pharmaceutical research and industry. Capillary electrophoresis (CE) is an interesting alternative to the more frequently used chromatographic techniques in the enantioseparation of pharmaceuticals, and is used for the determination of enantiomeric ratio, enantiomeric purity, and in pharmacokinetic studies. Traditionally, optimization of CE methods is performed using a univariate one factor at a time (OFAT) approach; however, this strategy does not allow for the evaluation of interactions between experimental factors, which may result in ineffective method development and optimization. In the last two decades, Design of Experiments (DoE) has been frequently employed to better understand the multidimensional effects and interactions of the input factors on the output responses of analytical CE methods. DoE can be divided into two types: screening and optimization designs. Furthermore, using Quality by Design (QbD) methodology to develop CE-based enantioselective techniques is becoming increasingly popular. The review presents the current use of DoE methodologies in CE-based enantioresolution method development and provides an overview of DoE applications in the optimization and validation of CE enantioselective procedures in the last 25 years. Moreover, a critical perspective on how different DoE strategies can aid in the optimization of enantioseparation procedures is presented.

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Yasui, Ryota, Keisuke Sekine, and Hideki Taniguchi. "Clever Experimental Designs: Shortcuts for Better iPSC Differentiation." Cells 10, no. 12 (December 15, 2021): 3540. http://dx.doi.org/10.3390/cells10123540.

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For practical use of pluripotent stem cells (PSCs) for disease modelling, drug screening, and regenerative medicine, the cell differentiation process needs to be properly refined to generate end products with consistent and high quality. To construct and optimize a robust cell-induction process, a myriad of cell culture conditions should be considered. In contrast to inefficient brute-force screening, statistical design of experiments (DOE) approaches, such as factorial design, orthogonal array design, response surface methodology (RSM), definitive screening design (DSD), and mixture design, enable efficient and strategic screening of conditions in smaller experimental runs through multifactorial screening and/or quantitative modeling. Although DOE has become routinely utilized in the bioengineering and pharmaceutical fields, the imminent need of more detailed cell-lineage specification, complex organoid construction, and a stable supply of qualified cell-derived material requires expedition of DOE utilization in stem cell bioprocessing. This review summarizes DOE-based cell culture optimizations of PSCs, mesenchymal stem cells (MSCs), hematopoietic stem cells (HSCs), and Chinese hamster ovary (CHO) cells, which guide effective research and development of PSC-derived materials for academic and industrial applications.

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Thanikachalam, J., and P. Nagaraj. "Optimised design for magnetorheological brake using DOE methods." International Journal of Energy Technology and Policy 11, no. 4 (2015): 394. http://dx.doi.org/10.1504/ijetp.2015.074163.

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MH, Zakaria. "Energy Efficient Quadcopter Drone Design using Design of Experiment (DOE) Method." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.1 S I (February 15, 2020): 233–36. http://dx.doi.org/10.30534/ijatcse/2020/4191.12020.

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Li, Zheng, Liam O’Brien, He Zhang, and Rajiv Ranjan. "Applying Design of Experiments (DOE) to Performance Evaluation of Commercial Cloud Services." International Journal of Grid and High Performance Computing 5, no. 3 (July 2013): 75–93. http://dx.doi.org/10.4018/jghpc.2013070107.

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Appropriate performance evaluations of commercial Cloud services are crucial and beneficial for both customers and providers to understand the service runtime, while suitable experimental design and analysis would be vital for practical evaluation implementations. However, there seems to be a lack of effective methods for Cloud services performance evaluation. For example, in most of the existing evaluation studies, experimental factors (also called parameters or variables) were considered randomly and intuitively, experimental sample sizes were determined on the fly, and few experimental results were comprehensively analyzed. To address these issues, the authors suggest applying Design of Experiments (DOE) to Cloud services evaluation. To facilitate applying DOE techniques, this paper introduces an experimental factor framework and a set of DOE application scenarios. As such, new evaluators can explore and conveniently adapt our work to their own experiments for performance evaluation of commercial Cloud services.

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Sabila, Liya Yusrina, Teguh Prakoso, and Munawar Agus Riyadi. "Miniaturized Spiral Planar Inverted F Antenna of 2.4 GHz Using Design of Experiment Method for EEG-based Controlled Prosthetic Arm." Jurnal Elektronika dan Telekomunikasi 22, no. 1 (August 31, 2022): 23. http://dx.doi.org/10.55981/jet.445.

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This paper presents the design of a planar inverted F antenna with a miniature or tiny shape at the frequency of 2.4 GHz. The antenna uses a spiral design to reduce the dimension of the antenna with conformal shape for a suitable prosthetic arm. Usually, the antenna design uses long experimental steps, namely trial and error. It can be summarized using the DOE (design of experiment) method. The DOE is a method to streamline the experimental steps to get the best design. The DOE method uses a tuning reference at the design parameter variation of 5 % of the nominal value. Four tuning steps can get the best results from S11, bandwidth, and gain. The designed antenna works at the resonant frequency of 2.431 GHz with the value of S11 is -22.634 dB, bandwidth of 37.1 MHz, and gain of -7.596 dBi.

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Lamberti, Francesco, Chiara Mazzariol, Federico Spolaore, Riccardo Ceccato, Luigi Salmaso, and Silvia Gross. "Design of Experiment: A Rational and Still Unexplored Approach to Inorganic Materials’ Synthesis." Sustainable Chemistry 3, no. 1 (March 6, 2022): 114–30. http://dx.doi.org/10.3390/suschem3010009.

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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.

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Inohira, Eiichi, and Hirokazu Yokoi. "An Optimal Design Method for Artificial Neural Networks by Using the Design of Experiments." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 593–99. http://dx.doi.org/10.20965/jaciii.2007.p0593.

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This paper presents a method to optimally design artificial neural networks with many design parameters using the Design of Experiment (DOE), whose features are efficient experiments using an orthogonal array and quantitative analysis by analysis of variance. Neural networks can approximate arbitrary nonlinear functions. The accuracy of a trained neural network at a certain number of learning cycles depends on both weights and biases and its structure and learning rate. Design methods such as trial-and-error, brute-force approaches, network construction, and pruning, cannot deal with many design parameters such as the number of elements in a layer and a learning rate. Our design method realizes efficient optimization using DOE, and obtains confidence of optimal design through statistical analysis even though trained neural networks very due to randomness in initial weights. We apply our design method three-layer and five-layer feedforward neural networks in a preliminary study and show that approximation accuracy of multilayer neural networks is increased by picking up many more parameters.

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Yarici, Ismail, and Yavuz Öztürk. "Analysis of an inverted square SRR via design of experiment (DoE) approach." Journal of Electrical Engineering 72, no. 4 (August 1, 2021): 273–77. http://dx.doi.org/10.2478/jee-2021-0038.

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Abstract A design of experiment (DoE) study is presented based on an investigation of the influences of the chosen geometric parameters of an Inverted Square Split Ring Resonator on its resonance frequency. A statistical software was used to determine DoE steps and the values of chosen geometrical parameters for the experiments. The determined experiments were carried out by making simulations with electromagnetic design software. The resonator simulation outputs were analyzed by using normality tests and tools of the statistical software. By using these analyses, mainly a 2-level full factorial DoE approach, the effects of the geometrical parameters (input factors), and their interactions on the resonance frequency (response factor) were presented. In the light of our findings, this study proposes a promising path for microwave studies with several advantages such as being able to understand the dynamics of an optimized RF resonator device system, designing these kinds of devices with a few experiments, and increasing the time efficiency via reducing the number of attempts.

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Xu, Zhuohui, Jinyun Zhou, Bo Wang, and Ziming Meng. "Design of Refractive/Diffractive Hybrid Projection Lens for DMD-Based Maskless Lithography." Optics 2, no. 2 (June 8, 2021): 103–12. http://dx.doi.org/10.3390/opt2020011.

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The projection lens is the core component of DMD-based maskless lithography and its imaging quality directly affects the transferal of exposure pattern. Based on the traditional projection lens system, we have designed diffractive optical element (DOE) and aspheric surfaces to optimize the refractive/diffractive hybrid projection lens system to improve its imaging quality. We found that the best effect is obtained when DOE is very close to the front lens group before the diaphragm of the hybrid system. Compared with the traditional projection lens system, this hybrid projection lens system has lower wave aberration with the help of DOE, and higher image quality owing to the modulation transfer function (MTF) value being improved. Finally, a hybrid projection lens system with working distance of 29.07 mm, image Space NA of 0.45, and total length of 196.97 mm is designed. We found that the maximum distortion and field curvature are 1.36 × 10−5% and 0.91 μm, respectively.

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You, Gou Dong, Tie Xiong Su, Jun Feng Xu, and Kun Li. "Predictive Design for Intake Port of HPD Diesel Engine." Advanced Materials Research 308-310 (August 2011): 1641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1641.

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In an investigation to gain a deeper understanding of the relationship between port geometry and performance characteristics, a parametic intake port design tool has been developed using statistical design-of-experiment (DOE) techniques. Flow coefficient is a very important parameter, constraints are used to represent additional features in the external packaging requirements. A thorough assessment of performance was made possible by using rapid-prototype models and numerical simulation and the intake port steady flow. The in-cylinder flows were successfully validated by comparing predicted results with new test data. By analyzing the DoE data a design method was developed and used to predict optimum design configurations for a range of common diesel engine applications.

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Islam, M. N., and A. Pramanik. "Comparison of Design of Experiments via Traditional and Taguchi Method." Journal of Advanced Manufacturing Systems 15, no. 03 (July 26, 2016): 151–60. http://dx.doi.org/10.1142/s0219686716500116.

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This paper presents a case study on comparison of Design of Experiments (DOE) via traditional and Taguchi methods in terms of efficiency. First, a three-level, four-parameter, full factorial DOE was conducted for finding the effects of machining parameters on the surface roughness (arithmetic average) of parts produced by turning operation. The results were analyzed applying average response, Taguchi’s [Formula: see text]/[Formula: see text] ratio, and Pareto ANOVA. Subsequently, the same data was analyzed applying Taguchi’s L9 orthogonal array. The comparison of two results revealed that despite an 88.9% savings of experimental runs with the Taguchi method, both methods produced similar results.

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Seok, Woochan, Gwan Hoon Kim, Jeonghwa Seo, and Shin Hyung Rhee. "Application of the Design of Experiments and Computational Fluid Dynamics to Bow Design Improvement." Journal of Marine Science and Engineering 7, no. 7 (July 17, 2019): 226. http://dx.doi.org/10.3390/jmse7070226.

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Techniques of the design of experiments (DOE) and computational fluid dynamics (CFD) were applied for improving the bow shape of a tanker hull. Through this, a hull that could reduce the added resistance in waves was derived. The key design elements of the bow shape were selected as parameters for design optimization and added resistance in the short-wavelength region was interpreted through CFD considering the operational condition of the full scale ship. For design parameter changes, the number of analyses was minimized by applying DOE. The regression equation for calculating added resistance was derived using bow-shape design parameters by applying the response surface method and regression analysis to obtain the optimal hull with minimal added resistance was derived. The methodology was applied to an Aframax tanker hull form, and the derived added resistance regression equation and the added resistance value obtained through CFD analysis showed a difference of approximately 1%. The model test results of the improved hull form showed that the added resistance was reduced by 52% in comparison to that obtained for the original hull form.

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You, Yong-Min. "Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling." Energies 12, no. 24 (December 9, 2019): 4673. http://dx.doi.org/10.3390/en12244673.

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To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

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Aldio, Rieza Zulrian, and Zainol Mustafa. "DRILL BIT SELECTION USING DESIGN OF EXPERIMENTS (DoE) METHOD." Journal of Renewable Energy & Mechanics (REM) 3, no. 01 (February 28, 2020): 39–43. http://dx.doi.org/10.25299/rem.2020.vol3.no01.4597.

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Drilling process is one of the most common machning process in industrial sector. More than half of the metal-cutting processes are conducted by the drilling process. Drill bit has influenced the results of the drilling process. Therefore, selection of the suitable drill bit becomes a critical factor in the drilling process. This is because the use of the suitable drill bit could fulfill the determined specification value of the hole. Six Sigma and Failure Mode Effect Analysis (FMEA) methods are used to identify factors that have influenced the results of the drilling process. Then by using the Design of Experiment, selection of the best drill bit could be done. In this study, 2 factors that influenced the result are the drill bit type and the drill point angle. Significance test using nested design through MINITAB 14 application has shown that both factors have significant influence over the hole diameter size.. Then by using the plot from the MINITAB 14 application, HPMT 1 became the best drill bit because it could fulfill the specification value. As for the best point angle in this study is 139.72º. Process capability calculation of HPMT 1 has shown that the process is in control. The conclusion is that drill bit HPMT 1 with point angle 139.72º became the best option in this study.

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Yang, Zhuo Yi, Yan Ma, and Yan Xue Chen. "Integrated Optimal Design of Ducted Propeller Based on DOE." Advanced Materials Research 291-294 (July 2011): 1698–703. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.1698.

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Ducted propeller is a normal thrust used widely in ship field, and the traditional design method could be improved by advanced computer technology of integrated design. Surface panel method predicting hydrodynamic performance of propeller and CFD were both used here, to ensure the results from surface panel method were believable. Surface panel program of ducted propeller was integrated in iSIGHT optimization platform, where the pitches in different radius were optimized and studied to find the best scheme. Design of experiment was selected as optimal method. Design variables were auto-chosen in the design space and optimal process was auto-executed. Besides, the effect of parameters to objective was gained. The final result showed that this method which can improve the efficiency of ducted propeller and realize the motivation provided a new idea for propeller design.

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Muni, Dhruv. "Optimization of Coil Spring using Design of Experiments (DOE)." International Journal for Research in Applied Science and Engineering Technology 8, no. 9 (September 30, 2020): 821–26. http://dx.doi.org/10.22214/ijraset.2020.31589.

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Lendrem, Dennis, Martin Owen, and Sonya Godbert. "DOE (Design of Experiments) in Development Chemistry: Potential Obstacles." Organic Process Research & Development 5, no. 3 (May 2001): 324–27. http://dx.doi.org/10.1021/op000025i.

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Barad, Miryam. "Design of Experiments (DOE)—A Valuable Multi-Purpose Methodology." Applied Mathematics 05, no. 14 (2014): 2120–29. http://dx.doi.org/10.4236/am.2014.514206.

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Onyeogaziri, Favour Chinyere, and Christos Papaneophytou. "A General Guide for the Optimization of Enzyme Assay Conditions Using the Design of Experiments Approach." SLAS DISCOVERY: Advancing the Science of Drug Discovery 24, no. 5 (February 25, 2019): 587–96. http://dx.doi.org/10.1177/2472555219830084.

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Many factors must be considered during the optimization of an enzyme assay. These include the choice of buffer and its composition, the type of enzyme and its concentration, as well as the type of substrate and concentrations, the reaction conditions, and the appropriate assay technology. The process of an enzyme assay optimization, in our experience, can take more than 12 weeks using the traditional one-factor-at-a-time approach. In contrast, the design of experiments (DoE) approaches have the potential to speed up the assay optimization process and provide a more detailed evaluation of tested variables. However, not all researchers are aware of DoE approaches or believe that it is easy to employ a DoE approach for the optimization of an assay. In order to facilitate enzyme assay developers to use DoE methodologies, we present in detail the steps required to identify in less than 3 days (1) the factors that significantly affect the activity of an enzyme and (2) the optimal assay conditions using a fractional factorial approach and response surface methodology. This is exemplified with the optimization of assay conditions for the human rhinovirus-3C protease, and the methodology used could be employed as a basic guide for the speedy identification of the optimum assay conditions for any enzyme.

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Molnar, T., V. Baranyai, S. Kemény, Gy Bánhegyi, and József Szabó. "Adjusting the Flexibility of Fabric Reinforced Composite Laminates Using Experimental Design." Materials Science Forum 812 (February 2015): 181–87. http://dx.doi.org/10.4028/www.scientific.net/msf.812.181.

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The objective of our work is to improve the mechanical stiffness of fiber reinforced laminates. The stiffness can be characterized by flexural and tensile moduli or their derivation. We applied design of experiments (DOE) to achieve our goals, because to solve the existing analytical and numerical models is complicated.We examined the effects of the following parameters: a) composition of reinforce materials (solely carbon, or carbon and glass combination), b) modulus of resin, c) mass ratio of resin-reinforcement, d) order of layers.The samples manufactured on the basis of DOE were investigated mechanically (flexural and tensile moduli measurements) and morphologically (scanning electron microscopy). We compared the measured modulus results to calculated values.

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Yin, Xu, and Ai Min Ji. "Optimization Method Based on a Mix Strategy." Advanced Materials Research 816-817 (September 2013): 1154–57. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.1154.

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To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

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Feng, Chang-Xue (Jack), and Andrew Kusiak. "Robust Tolerance Synthesis With the Design of Experiments Approach." Journal of Manufacturing Science and Engineering 122, no. 3 (May 1, 1999): 520–28. http://dx.doi.org/10.1115/1.1285860.

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Design of tolerances impacts quality, cost, and cycle time of a product. Most literature on deterministic tolerance design has focused on developing exact and heuristic algorithms to minimize manufacturing cost. Some research has been published on probabilistic tolerance synthesis and optimization. This paper presents the design of experiments (DOE) approach for concurrent selection of component tolerances and the corresponding manufacturing processes. The objective is to minimize the variation of tolerance stackups. Numerical examples illustrate the methodology. The Monte Carlo simulation approach is used to obtain component tolerances and tolerance stackups. Process shift, the worst case and root sum square tolerance stackup constraints, and setup reduction constraints have been incorporated into the proposed methodology. Benefits of the proposed DOE approach over exact algorithms are discussed. [S1087-1357(00)00202-1]

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Rodolfo, Carlos, Dalinda Eusébio, Cathy Ventura, Renato Nunes, Helena F. Florindo, Diana Costa, and Ângela Sousa. "Design of Experiments to Achieve an Efficient Chitosan-Based DNA Vaccine Delivery System." Pharmaceutics 13, no. 9 (August 31, 2021): 1369. http://dx.doi.org/10.3390/pharmaceutics13091369.

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In current times, DNA vaccines are seen as a promising approach to treat and prevent diseases, such as virus infections and cancer. Aiming at the production of a functional and effective plasmid DNA (pDNA) delivery system, four chitosan polymers, differing in the molecular weight, were studied using the design of experiments (DoE) tool. These gene delivery systems were formulated by ionotropic gelation and exploring the chitosan and TPP concentrations as DoE inputs to maximize the nanoparticle positive charge and minimize their size and polydispersity index (PDI) as DoE outputs. The obtained linear and quadratic models were statistically significant (p-value < 0.05) and non-significant lack of fit, with suitable coefficient of determination and the respective optimal points successfully validated. Furthermore, morphology, stability and cytotoxicity assays were performed to evaluate the endurance of these systems over time and their further potential for future in vitro studies. The subsequent optimization process was successful achieved for the delivery systems based on the four chitosan polymers, in which the smallest particle size was obtained for the carrier containing the 5 kDa chitosan (~82 nm), while the nanosystem prepared with the high molecular weight (HMW) chitosan displayed the highest zeta potential (~+26.8 mV). Delivery systems were stable in the formulation buffer after a month and did not exhibit toxicity for the cells. In this sense, DoE revealed to be a powerful tool to explore and tailor the characteristics of chitosan/pDNA nanosystems significantly contributing to unraveling an optimum carrier for advancing the DNA vaccines delivery field.

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Zohra Kessaissia, Fatma, Abdallah Zegaoui, Rachid Taleb, Chahinez Fares, and Michel Aillerie. "Design of experiments approach for modeling the electrical response of a photovoltaic module." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1140. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1140-1147.

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In the current paper, modeling and evaluation of the significant effect of independent variables on the behavior of the electrical response of a multi- crystalline photovoltaic (PV) module using design of experiments (DoE) approach is simulated. The main purpose of this contribution is to evaluate the maximum power response dependence within the indoor conditions of both variations of solar irradiation and surface temperature and checking the pertinent one on the defined response. The DoE approach is used for estimating both main and combined effect of the two independents considered variables. Multiple linear regression was been introduced to justify the relationship between the independent input variables and dependent output variable, also to determine which input factor is the most significant on the output variable. The DoE model can be used for predicting the response variable at different operating condition in a considered domain study. In addition, DoE approach based on statistical tool for analyzing the accuracy of the predictive model, then the significance of coefficients in the predictive model using statistical and graphical analysis. Therefore, an ANOVA Table can summarize the results, detect the parameters influences on responses variations and determine the best predictive model then reproduce the most possible the experimental data.

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Journal articles on the topic 'Design of Experiement (DOE)'

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