Abstract
Process optimization is increasingly important as competition deepens. As such, the optimization of process parameters allows the manufacturer to improve its competitive advantage. In that context, planning experiences to test the limits of the system is progressively more important. In that context, Design of Experiments is a group of techniques that allow a better understanding of every process, when correctly applied. During the last two centuries, several methods were developed to approach Design of Experiments. In this paper, the history and methods, focusing on the most important, are examined in detail. The last chapter describes the future paths of planned experiences, addressing its connection with digital simulation, making its use a lot more accessible. The algorithmic and mathematical evolution is also described, relating it to the software that can be used to execute planned experiences.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Román-Ramírez, L.A., Marco, J.: Design of experiments applied to lithium-ion batteries: a literature review. Appl. Energy 320, 119305 (2022)
Anderson, M.J., Whitcomb, P.J.: DOE Simplified: Practical Tools for Effective Experimentation. CRC press (2017)
Vickers, A.J.: Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC Med. Res. Methodol. 5(1), 1–12 (2005)
Berger, R.W., Benbow, D.W., Elshennawy, A.K., Walker, H.F.: The Certified Quality Engineer Handbook. ASQ Quality Press (2006)
Byrne, G.: A statistical primer: understanding descriptive and inferential statistics. Evid Based Libr Inf Pract 2(1), 32–47 (2007)
Rouder, J.N., Engelhardt, C.R., McCabe, S., Morey, R.D.: Model comparison in ANOVA. Psychon. Bull. Rev. 23(6), 1779–1786 (2016)
Draper, N.R., Pukelsheim, F.: An overview of design of experiments. Stat. Pap. 37(1), 1–32 (1996)
Vanaja, K., Shobha Rani, R.H.: Design of experiments: concept and applications of Plackett Burman design. Clin Res Regul Aff, 24(1), 1–23 (2007)
Hedayat, A.S., Sloane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer Science & Business Media (1999)
Giunta, A.A., Wojtkiewicz, S.F., Eldred, M.S.: Overview of modern design of experiments methods for computational simulations. In: 41st Aerospace Sciences Meeting and Exhibit (2003). https://doi.org/10.2514/6.2003-649
Czitrom, V.: One-factor-at-a-time versus designed experiments. Am. Stat. 53(2), 126–131 (1999)
Yahiaoui, I., Aissani-Benissad, F.: Experimental design for copper cementation process in fixed bed reactor using two-level factorial design. Arab. J. Chem. 3(3), 187–190 (2010)
Grömping, U.: An algorithm for blocking regular fractional factorial 2-level designs with clear two-factor interactions. Comput. Stat. Data Anal. 153, 107059 (2021)
Han, X., Liu, M.-Q., Yang, J.-F., Zhao, S.: Mixed 2-and 2r-level fractional factorial split-plot designs with clear effects. J Stat Plan Inference 204, 206–216 (2020)
Kacker, R.N., Lagergren, E.S., Filliben, J.J.: Taguchi’s Orthogonal Arrays are Classical Designs of Experiments (1960)
Yuce, B.E., Nielsen, P.V., Wargocki, P.: The use of Taguchi, ANOVA, and GRA methods to optimize CFD analyses of ventilation performance in buildings. Build. Environ. 225, 109587 (2022)
Hasanzadeh, R., Mojaver, P., Chitsaz, A., Mojaver, M., Jalili, M., Rosen, M.A.: Biomass and low-density polyethylene waste composites gasification: Orthogonal array design of Taguchi technique for analysis and optimization. Int. J. Hydrogen Energy 47(67), 28819–28832 (2022)
Munawar, M.A., et al.: Investigation of functional, physical, mechanical and thermal properties of TiO2 embedded polyester hybrid composites: a design of experiment (DoE) study. Progress in Natural Science: Materials Int. 28(3), 266–274 (2018)
Collin, L.R.D., Pamplona, E.O.: A utilização da função Perda de Taguchi na prática do Controle Estatístico de Processo. Escola Federal de Engenharia de Itajubá-IEM/DPR (1997)
Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M., Kowalski, S.M.: Response surface methodology: a retrospective and literature survey. J. Qual. Technol. 36(1), 53–77 (2004)
Van, N.T.T., et al.: Cellulose from the banana stem: optimization of extraction by response surface methodology (RSM) and characterization. Heliyon, p. e11845 (2022)
Borrotti, M., Sambo, F., Mylona, K.: Multi-objective optimisation of split-plot designs. Econom Stat (2022)
Smucker, B.J., del Castillo, E., Rosenberger, J.L.: Model-robust designs for split-plot experiments. Comput. Stat. Data Anal. 56(12), 4111–4121 (2012)
Mandal, B.N., Parsad, R., Dash, S.: Incomplete split-plot designs: construction and analysis. Stat Probab Lett 166, 108869 (2020)
Jankovic, A., Chaudhary, G., Goia, F.: Designing the design of experiments (DOE)–an investigation on the influence of different factorial designs on the characterization of complex systems. Energy Build 250, 111298 (2021)
Trojanowski, P., Filina-Dawidowicz, L.: Diagnostic and repair centers locating methodology for vehicles carrying sensitive cargo. Transportation Research Procedia 55, 410–417 (2021). https://doi.org/10.1016/j.trpro.2021.07.004
Sousa, R.A., Varela, M.L.R., Alves, C., Machado, J.: Job shop schedules analysis in the context of industry 4.0. In: 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017, pp. 711–717 (2018). https://doi.org/10.1109/ICE.2017.8279955
Kaiser, S., Engell, S.: An integrated approach to fast model-based process design: Integrating superstructure optimization under uncertainties and optimal design of experiments. Chem Eng Sci. 118453 (2023)
Husar, J., Knapcikowa, L.: Possibilities of using augmented reality in warehouse management: a study. Acta Logistica 8(2), 133–139. https://doi.org/10.22306/al.v8i2.212
Vieira, G.G., Varela, M.L.R., Putnik, G.D., Machado, J.M., Trojanowska, J.: Integrated platform for real-time control and production and productivity monitoring and analysis. Romanian Review Precision Mechanics, Optics and Mechatronics 50, 119–127 (2016)
Arrais-Castro, A., Varela, M.L.R., Putnik, G.D., Ribeiro, R.A., Machado, J., Ferreira, L.: Collaborative framework for virtual organisation synthesis based on a dynamic multi-criteria decision model. Int. J. Computer Integrated Manuf. 31(9), 857868. https://doi.org/10.1080/0951192X.2018.1447146
Acknowledgement
The research was partially supported by the Polish National Agency for Academic Exchange within the project “Strengthening the scientific cooperation of the Poznan University of Technology and Sumy State University in the field of mechanical engineering” (agreement no. BPI/UE/2022/8–00). This work was also supported by national funds through the FCT-Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, H., Santos, A.S., Varela, L.R., Trojanowska, J., Berladir, K. (2023). Design of Experiments: An Overview and Future Paths. In: Burduk, A., Batako, A., Machado, J., Wyczółkowski, R., Antosz, K., Gola, A. (eds) Advances in Production. ISPEM 2023. Lecture Notes in Networks and Systems, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-031-45021-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-45021-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45020-4
Online ISBN: 978-3-031-45021-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)