Critical evaluation into the practical utility of the Design of Experiments

• Autorzy: Sharma Mithun , Sharma Shilpi

Identyfikatory

DOI

10.2478/emj-2021-0021

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Six-Sigma; design of experiments; screening; Taguchi screening; full factorial

PL

Six-Sigma; projektowanie eksperymentów; klasyfikowanie; Taguchi klasyfikacja; silnia

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2021
• Tom: Vol. 13, no. 3
• Strony: 50--65
• Opis fizyczny: Bibliogr. 54 poz., tab., wykr.

Twórcy

autor

Sharma Mithun

• O.P. Jindal Global University, India

• https://orcid.org/0000-0002-0108-6331

autor

Sharma Shilpi

• O.P. Jindal Global University, India

• https://orcid.org/0000-0003-4316-8551

Bibliografia

• Andersson, P. M., Lundstedt, T. & Abramo, L. (1996). Synthesis and optimisation of 1-pyrrolemethane sulfonate by means of experimental design. Journal of Chemometrics, 10, 379-384.
• Antony, J. (2014). Some Useful and Practical Tips for Making Your Industrial Experiments Successful. In J. Antony (Ed.), Design of Experiments for Engineers and Scientists. Second Edition (pp. 113-123). Elsevier. doi: 10.1016/B978-0-08-099417-8.00008-0
• Antony, J., & Roy, R. K. (1999). Improving the process quality using statistical Design of experiments: A case study. Quality Assurance, 6, 87-95.
• Arnoldsson, K. C., & Kaufmann, P. (1994). Lipid class analysis by normal phase high performance liquid chromatography, development and optimisation using multivariate methods. Chromatographia, 38, 317-324.
• Beijersten, I., & Westerlund, D. (1995). Derivatisation Of Dipeptides With 4-Fluoro-7-Nitro2,1,3 Benzoxadiazole For Laser-Induced Fluorescence And Separation By Micellar Electrokinetic Chromatography. Journal of Chromatography, 716, 389-399.
• Bisgaard, S. (1991). Teaching Statistics to Engineers. The American Statistician, 45(4), 274-283.
• Bisgaard, S. (1992). Industrial use of statistically designed experiments: Case study references and some historical anecdotes. Quality Engineering, 4, 547-562.
• Box G. E. P., Bisgaard, S., & Fung, C. (1988). An explanation and critique of Taguchi’s contributions to quality engineering. Quality and Reliability Engineering International, 4, 123-131.
• Box, G. E. P. (1988). Signal-to-noise ratios, performance criteria, and transformation. Technometrics, 30, 1-40.
• Box, G. E. P. (2001). Statistics for discovery. Journal of Applied Statistics, 28(3-4), 285-299.
• Box, G. E. P., Hunter, W. G. J., & Hunter, S. (1987). Statistics for experimenters: an introduction to Design.
• Brady, J. E., & Allen, T. T. (2006). Six Sigma literature: A review and agenda for future research. Quality and Reliability Engineering International, 22, 335-367.
• Bucher, R. A., & Loos, A. C. (1994). Parametric statistical analysis of electrostatic powder prepregging. Journal of Advanced Materials, 25, 44-50.
• Bzik, T. J., Henderson, P. B., & Hobbs, J. P. (1998). Increasing the precision and accuracy of top loading balances: Application of experimental design. Analytical Chemistry, 70, 58-63.
• Carlson, A. D., Hofer, J. D., & Riggin, R. M. (1997). Development of an optimised peptide map for recombinant activated human protein c by means of an experimental design strategy. Analytica Chimica Acta, 352, 221-230.
• Chapin, S. F. (1950). Research note on randomisation in a social experiment. Science, 112, 760-761.
• Chen, H. C. (1996). Optimising the concentrations of carbon, nitrogen and phosphorus in a citric acid fermentation with response surface method. Food Biotechnology, 10, 13-27.
• Czitrom, V. (1999). One factor at a time versus Designed Experiments. The American Statistician, 53(2), 126-131.
• Data analysis, and model building. New York: Wiley.
• Davim, J. P. (Ed.). (2016). Design of Experiments in Production Engineering. Springer International Publishing.
• Davis, B. L., Cavanagh, P. R., Sommer, H. J., & Wu, G. (1996). Ground reaction forces during locomotion in simulated microgravity. Aviation Space and Environmental Medicine, 67, 235-242.
• Durakovic, B. (2017). Design of Experiments Application, Concepts, Examples: State of the Art. Periodicals of Engineering and Natural Sciences, 5(3), 421-439. doi: 10.21533/pen
• Durakovic, B., & Torlak, M. (2017). Simulation and experimental validation of phase change material and water used as heat storage medium in window applications. Journal of Materials and Environmental Science, 8(5), 1837-1746.
• Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503-513.
• Gardner, R., Bieker, J., Elwell, S., Thalman, R., & Rivera, E. (2000). Solving tough semiconductor manufacturing problems using data mining. In Proceedings of IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop, 46-55.
• Goh, T. N. (2002). The role of statistical Design of Experiments in Six Sigma: Perspectives of a practitioner. Quality Engineering, 14(4), 659-671.
• Gremyr, I., Arvidsson, M., & Johansson, P. (2003). Robust Design Methodology: Status in the Swedish Manufacturing Industry. Qualitative Reliability Engineering International, 19, 285-293.
• Hahn, G. J. (2005). Six Sigma: 20 Key Lessons Learned. Quality and Reliability Engineering International, 21, 225-233.
• Hecht, E. S., Oberg, A. L., & Muddiman, D. C. (2016). Optimising Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments. Journal of American Society of Mass Spectrometry, 27, 767-785.
• Hibbert, D. B. (2012). Experimental design in chromatography: a tutorial review. Journal of Chromatography, 910, 2-13.
• Ilzarbe, L., Álvarez, M. J., Viles, E., & Tanco, M. (2008). Practical applications of design of experiments in the field of engineering: a bibliographical review. Qualitative Reliability Engineering International, 24, 417-428.
• Kackar, R. N., & Shoemaker, A. C. (2021). Robust Design: A cost-effective method for improving manufacturing processes. AT&T Technical Journal, 65, 39-50.
• Kenett, R. S., & Steinberg, D. M. (2006). New frontiers in the Design of experiments. Quality Progress, 39(8), 61-65.
• Lye, L. M. (2005). Tools and toys for teaching design of experiments methodology. In 33rd Annual General Conference of the Canada, Toronto, Ontario, Canada.
• Mager, P. P. (1997). How design statistics concepts can improve experimentation in medicinal chemistry. Medicinal Research Reviews, 17, 453-475.
• Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons, Inc.
• Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., & Kowalski, S. M. (2004). Response surface methodology: A retrospective and literature survey. Journal of Quality Technology, 36(1), 53-77.
• Nair, V. N. (1992). Taguchi’s parameter design: A panel discussion. Technometrics, 34, 127-161.
• Okatia, V., Behzadmehra, A., & Farsad, S. (2016). Analysis of a solar desalinator (humidification–dehumidification cycle) including a compound system consisting of a solar humidifier and subsurface condenser using DoE. Desalination, 397, 9-21.
• Paulo, F., & Santos, L. (2017). Design of experiments for microencapsulation applications: A review. Materials Science and Engineering: C, 77(August), 1327-1340.
• Puente-Massaguer, E., Lecina, M., & Gòdia, F. (2020). Integrating nanoparticle quantification and statistical design of experiments for efficient HIV-1 virus-like particle production in High Five cells. Applied Microbiology and Biotechnology, 104, 1569-1582. doi: 10.1007/s00253-019-10319-x
• Robinson, T. J., Borror, C. M., & Myers, R.H. (2004). Robust parameter design: A review. Quality and Reliability Engineering International, 20, 81-101.
• Schlueter, A., & Geyer, P. (2018). Linking BIM and Design of Experiments to balance architectural and technical design factors for energy performance. Automation in Construction, 86(February), 33-43.
• Setamanit, S. (2018). Evaluation of outsourcing transportation contract using simulation and design of experiment. Polish Journal of Management Studies, 18(2), 300-310.
• Sukthomya, W., & Tannock, J. (2005). The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling. Neural Computing and Applications, 14, 337-344. doi: 10.1007/s00521-005-0470-3
• Tukey, J. W. (1947). Non-parametric estimation II. Statistically equivalent blocks and tolerance regions – the continuous case. Annals of Mathematical Statistics, 18, 529-539.
• Wald, A. (1943). An extension of Wilks’ method for setting tolerance limits. Annals of Mathematical Statistics, 14, 45-55.
• Wesling, P., & Emamjomeh, A. (1994). T.A.B. Inner-lead bond process characterisation for single point laser bonding. IEEE Transactions on Components Packaging & amp. Manufacturing Technology Part A, 17, 142-148.
• Yang, G. C. C., & Tsai, C. M. (1998). A study on heavy metal extractability and subsequent recovery by electrolysis for a municipal incinerator fly ash. Journal of Hazardous Materials, 58, 103-120.
• Yip, H. M., Wang, Z., Navarro-Alarcon, D., Li, P., Cheung, T. H., Greiffenhagen, Ch., & Liu, Y. (2020). A collaborative robotic uterine positioning system for laparoscopic hysterectomy: Design and experiments. International Journal of Medical Robotics and Computer Assisted Surgery, 16(4), e2103. doi: 10.1002/rcs.2103
• Yondo, R., Andrés, E., & Valero, E. (2018). A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses. Progress in Aerospace Sciences, 96, 23-61. doi: 10.1016/j.paerosci.2017.11.003
• Yoo, K. S. (2020). Application of Statistical Design of Experiments in the Field of Chemical Engineering: A Bibliographical Review. The Korean Society of Industrial and Engineering Chemistry, 31(2), 138- 146. doi: 10.14478/ACE.2020.1018
• Yu, P., Low, M. Y., & Zhou, W. (2018). Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends in Food Science & Technology, 71(January), 202-215.
• Zheng, H., Clausen, M. R., Dalsgaard, T. K., Mortensen, G., & Bertram, H. C. (2013). Time-saving design of experiment protocol for optimisation of LC-MS data processing in metabolomic approaches. Analytical Chemistry, 85, 7109-7116.