Modelling of an Additive 3D-Printing Process Based on Design of Experiments Methodology
Abstract
Purpose: The implementation of additive manufacturing (AM) or 3D-printer manufacturing for technical prototyping, preproduction series and short production series can bring benefits in terms of reducing cost and time to market in product development. These technologies are beginning to be applied in different industrial sectors and have a great possibility of development. As these technologies are still in development, there is a need to define the capacity of the 3D machines to establish minimum standards for producing high-quality parts.
Methodology/Approach: The proposed methodology is based on a design of experiments (DOE) approach, which serves as a guide for engineers when it comes to executing any experimental study. The following steps were followed (Unzueta et al., 2019): Phase 1: define; Phase 2: measure; Phase 3: plan; Phase 4: execute experimentation; Phase 5: analyse the results; Phase 6: improve via confirmation experiments; Phases 7-8: control and standardise.
Findings: The proposed methodology is based on a design of experiments (DOE) approach, which serves as a guide for engineers when it comes to executing any experimental study. The following steps were followed (Unzueta et al., 2019): Phase 1: define; Phase 2: measure; Phase 3: plan; Phase 4: execute experimentation; Phase 5: analyse the results; Phase 6: improve via confirmation experiments; Phases 7-8: control and standardise.
Originality/Value of paper: This study uses a methodological approach to demonstrate how the 3D printing technology can be enriched with statistical testing techniques (DOE). It defines numerical prediction models to obtain high-quality parts with a new AM technology, using a planning process with a minimum amount of experimentation.References
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Lipson, H., Lyons, B., Bengio, S., Ochsendorf, J., Pyke, C., Leuthardt, E.C., and Weiland, J.D., et al., 2012. Manufacturing in Aerospace: Examples and Research. Frontiers of Engineering, [online] Available at: < https://www.nap.edu/catalog/13274/frontiers-of-engineering-reports-on-leading-edge-engineering-from-the > [Accessed 31 March 2020].
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Prasad, M.M., Krishna, N.J. and Venkatasubbareddy, O.Y., 2014. Improving the Surface Roughness of FDM Parts By using Hybrid Methods. International Journal of Engineering Research & Technology, 3(12), pp.650-654.
Rayegani, F. and Onwubolu, G.C., 2016. Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). The International Journal of Advanced Manufacturing Technology, [e-journal] 73, pp.509-519. DOI: 10.1007/s00170-014-5835-2.
Sood, A.K., Ohdar, R.K. and Mahapatra, S.S., 2010. Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Journal of Engineering Manufacture, 24(1), pp.135-145.
Tofail, S.A.M., Koumoulos, E.P., Bandyopadhyay, A., Bose, S., O`Donoghue, L. and Charitidis, C., 2018. Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Materials Today, 21(1), pp.22-37.
Unzueta, G., Orue, A., Esnaola, A. and Eguren, J.A., 2019. Metodología del diseño de experimentos. Estudio de caso, lanzador. Dyna, 94(1), pp.16-21.
Venkatasubbareddy, O.Y., Siddikali, P. and Saleem, S.M., 2016. Improving the Dimensional Accuracy And Surface Roughness of Fdm Parts Using Optimization Techniques. IOSR Journal of Mechanical and Civil Engineering, 16(053), pp.18-22.
Wiemer, H., Schwarzenberger, M., Dietz, G., Juhrisch, M. and Ihlenfeldt, S., 2017. A Holistic and DoE-based Approach to Developing and Putting into Operation Complex Manufacturing Process Chains of Composite Components. Procedia CIRP, 66, pp.147-152.
Wong, K.V. and Hernandez, A., 2012. A Review of Additive Manufacturing. International Scholarly Research Notices, [e-journal] 2012, 10p. DOI: 10.5402/2012/208760.
Chepelev, L., Giannopoulos, A., Tang, A., Mitsouras, D. and Rybicki, F.J., 2017. Medical 3D printing : methods to standardize terminology and report trends. 3D Printing in Medicine, [online] Available at: < https://doi.org/10.1186/s41205-017-0012-5 > [Accessed 31 March 2020].
Cruz, F.A., Boudaoud, H., Muller, L. and Camargo, M., 2014. Towards a standard experimental protocol for open source additive manufacturing. Virtual and Physical Prototyping, [e-journal] 9(3), pp. 37-41. DOI: 10.1080/17452759.2014.919553.
Dwivedi, G., Srivastava, S. and Srivastava, R., 2015. Analysis of Barriers to Implement Additive Manufacturing Technology in the Indian Automotive Sector. International Journal of Physical Distribution & Logistics Management Rajiv, 47(10). DOI: 10.1108/IJPDLM-07-2017-0222.
Gausemeier, J., Wall, M. and Peter, S., 2013. Thinking ahead the Future of Additive Manufacturing – Exploring the Research Landscape. [pdf] Paderborn: Heinz Nixdorf Institute. Available at: < https://dmrc.uni-paderborn.de/fileadmin/dmrc/Download/data/DMRC_Studien/DMRC_Study_Part_3.pdf > [Accessed 31 March 2020].
Gibson, I., Rosen, D. and Stucker, B., 2015. Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. Second Edition. New York, NY: Springer. DOI: 10.1007/978-1-4939-2113-3.
Guo, N. and Leu, M.C., 2013. Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 8(3), pp.215-243.
Harris, R., 2019. Additive manufacturing research group. Loughborough University. Available at: < https://www.lboro.ac.uk/research/amrg/about/the7categoriesofadditivemanufacturing/ > [Accessed 29 May 2019].
ISO (International Organization for Standardization), 2012. ISO 527-2 Plastics — Determination of Tensile Properties — Part 2: Test Conditions for Moulding and Extrusion. Geneva: ISO.
Ituarte, I.F., Coatanea, E., Salmi, M., Tuomi, J. and Partanen, J., 2015. Additive Manufacturing in Production: A Study Case Applying Technical Requirements. Physics Procedia, 78, pp.357-366.
Khajavi, S.H., Partanen, J. and Holmström, J., 2014. Additive manufacturing in the spare parts supply chain. Computers in Industry, [e-journal] 65(1), pp.50-63. DOI: 10.1108/RPJ-03-2017-0052.
Kumbhar, N.N. and Mulay, A.V., 2018. Post Processing Methods used to Improve Surface Finish of Products which are Manufactured by Additive Manufacturing Technologies : A Review. Journal of The Institution of Engineers (India): Series C, 99(4), pp.481-487.
Li, Q., Kucukkoc, I. and Zhang, D.Z., 2017. Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, pp.1339-1351.
Lipson, H., Lyons, B., Bengio, S., Ochsendorf, J., Pyke, C., Leuthardt, E.C., and Weiland, J.D., et al., 2012. Manufacturing in Aerospace: Examples and Research. Frontiers of Engineering, [online] Available at: < https://www.nap.edu/catalog/13274/frontiers-of-engineering-reports-on-leading-edge-engineering-from-the > [Accessed 31 March 2020].
Moreau, C., 2018. Annuaire Des Statistiques Des Hydrocarbures En Côte d’Ivoire. [pdf] Ministère Du Pétrole,De L’énergie Et Du Développementdes Énergies Renouvelables. Available at: < http://dghstatistiques.ci/assets/documents/annuaire/Annuaire-DGH-2018-v3.pdf > [Accessed 31 March 2020].
Narang, R. and Chhabra, D., 2017. Analysis of Process Parameters of Fused Deposition Modeling (FDM) Technique. International Journal on Future Revolution in Computer Science & Communication Engineering, 3(10), pp.41-48.
Office of Technology Transition, 2019. Additive Manufacturing: Building the Future. Washington: Office of Technology Transition.
Prasad, M.M., Krishna, N.J. and Venkatasubbareddy, O.Y., 2014. Improving the Surface Roughness of FDM Parts By using Hybrid Methods. International Journal of Engineering Research & Technology, 3(12), pp.650-654.
Rayegani, F. and Onwubolu, G.C., 2016. Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). The International Journal of Advanced Manufacturing Technology, [e-journal] 73, pp.509-519. DOI: 10.1007/s00170-014-5835-2.
Sood, A.K., Ohdar, R.K. and Mahapatra, S.S., 2010. Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Journal of Engineering Manufacture, 24(1), pp.135-145.
Tofail, S.A.M., Koumoulos, E.P., Bandyopadhyay, A., Bose, S., O`Donoghue, L. and Charitidis, C., 2018. Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Materials Today, 21(1), pp.22-37.
Unzueta, G., Orue, A., Esnaola, A. and Eguren, J.A., 2019. Metodología del diseño de experimentos. Estudio de caso, lanzador. Dyna, 94(1), pp.16-21.
Venkatasubbareddy, O.Y., Siddikali, P. and Saleem, S.M., 2016. Improving the Dimensional Accuracy And Surface Roughness of Fdm Parts Using Optimization Techniques. IOSR Journal of Mechanical and Civil Engineering, 16(053), pp.18-22.
Wiemer, H., Schwarzenberger, M., Dietz, G., Juhrisch, M. and Ihlenfeldt, S., 2017. A Holistic and DoE-based Approach to Developing and Putting into Operation Complex Manufacturing Process Chains of Composite Components. Procedia CIRP, 66, pp.147-152.
Wong, K.V. and Hernandez, A., 2012. A Review of Additive Manufacturing. International Scholarly Research Notices, [e-journal] 2012, 10p. DOI: 10.5402/2012/208760.
Authors
Eguren, J. A., Esnaola, A., & Unzueta, G. (2020). Modelling of an Additive 3D-Printing Process Based on Design of Experiments Methodology. Quality Innovation Prosperity, 24(1), 128–151. https://doi.org/10.12776/qip.v24i1.1435
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