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 ENG  Vol.12 No.1 , January 2020
Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation
Abstract: In the modern era of manufacturing, it is important to optimize every design parameter in product development stage to reduce cost, material usage and to achieve the desired efficacy level. There are various models which serve those purposes, for instance, Design of Experiment (DoE) is used to check the parameters after adopting optimization tactics which results in reduced cost or saving operating time. In this regard, this research aims to construct a DoE model on a portable workstation to optimize its design parameters. The methodology of DOE would be a 2 level 3 factors full factorial DOE which is conducted to determine the optimal value for three design parameters (factors) which are material density, the length of the table and the length of the table stand in terms of the response which is the required time of fold ability function of the portable workstation. Based upon the evaluated interactions between the parameters, the optimized parameters are chosen for responses. Here, the resultant design parameters are at their lowest level, so the goal of time efficiency in fold ability function is achieved. This similar sort of DoE can be implemented in the furniture and other manufacturing industries who wish to optimize their material usage as well as increase efficiency and reduce cycle time.
Cite this paper: Anika, N. , Tanzeem, N. and Gupta, H. (2020) Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation. Engineering, 12, 25-32. doi: 10.4236/eng.2020.121002.
References

[1]   Wang, F.K., Yeh, C.T. and Chu, T.P. (2016) Using the Design for Six Sigma Approach with TRIZ for New Product Development. Computers & Industrial Engineering, 98, 522-530.
https://doi.org/10.1016/j.cie.2016.06.014

[2]   Schuh, G., Prote, J.P., Dany, S., Molitor, M. and Pagano, L. (2018) Adaptation of a Product Maturity Model to Highly Iterative Product Development. Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 10-13 December 2017, 485-489.
https://doi.org/10.1109/IEEM.2017.8289938

[3]   Singh, B., Kumar, R. and Ahuja, N. (2005) Optimizing Drug Delivery Systems Using Systematic “Design of Experiments”. Part I: Fundamental Aspects. Critical Reviews in Therapeutic Drug Carrier Systems, 22, 27-105.
https://doi.org/10.1615/CritRevTherDrugCarrierSyst.v22.i1.20

[4]   Verma, S., Lan, Y., Gokhale, R. and Burgess, D.J. (2009) Quality by Design Approach to Understand the Process of Nanosuspension Preparation. International Journal of Pharmaceutics, 377, 185-198.
https://doi.org/10.1016/j.ijpharm.2009.05.006

[5]   Kosierb, A., Merkwirth, C., Pedrys, R. and Psonka-Antonczyk, K. (2019) Optimization of Parameters of the Linear TOF-SIMS Spectrometer by DOE Method. Vacuum, 83, 137-139.
https://doi.org/10.1016/j.vacuum.2009.01.046

[6]   Das, A.K. and Dewanjee, S. (2018) Chapter 3: Optimization of Extraction Using Mathematical Models and Computation. Computational Phytochemistry, 75-106.
https://doi.org/10.1016/B978-0-12-812364-5.00003-1

[7]   Kulkarni, S. (2016) Process Development Part 2: Exploring the Dimensional Process via the DOE. Robust Process Development and Scientific Molding, 225-267.
https://doi.org/10.3139/9781569905876.009

[8]   Antony, J. (2007) Full Factorial Designs. Design of Experiments for Engineers and Scientists.

[9]   Antony, J. (2003) A Systematic Methodology for Design of Experiments. Design of Experiments for Engineers and Scientists.
https://doi.org/10.1016/B978-075064709-0/50005-3

[10]   Kleijnen, J.P.C. (2008) Screening Designs. International Series in Operations Research and Management Science, 111.
https://doi.org/10.1007/978-0-387-71813-2_6

[11]   Antony, J. (2002) Some Key Things Industrial Engineers Should Know about Experimental Design. Logistics Information Management, 11, 386-392.
https://doi.org/10.1108/09576059810242606

 
 
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