×

Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design. (English) Zbl 1443.62532

Summary: Fused deposition modeling (FDM) is a growing 3D printing technique widely practiced around the world in various industrial applications because of its ability to create complex 3D objects and geometries. Reduction of build time and feedstock material consumption without compromising the mechanical performance is the major concern in most industrial applications affecting the cost and the functionality of the manufactured part. One of the key issues of FDM process is how to select the correct parameters to reduce the build time and to reduce feedstock material consumption while maintaining high dynamic mechanical properties. In this study, influence of critical FDM parameters – layer thickness, air gap, raster angle, build orientation, road width, and number of contours – are studied using Q-optimal response surface methodology. Their effects on build time, feedstock material consumption and dynamic flexural modulus are critically examined. Mathematical models have been formulated to develop a functional relationship between the processing conditions and the process quality characteristics. Analysis of variance (ANOVA) technique was employed to check the adequacy and significance of mathematical models. Moreover, the optimal setting of process parameters was determined. A confirmation test was also conducted in order to verify the developed models and the optimal settings. The results show that Q-optimal design is a very promising method in FDM process parameter optimization. The results also confirm the adequacy of the developed models.

MSC:

62P30 Applications of statistics in engineering and industry; control charts
62K20 Response surface designs
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Masood, S. H., Introduction to advances in additive manufacturing and tooling, (Hashmi, S., Comprehensive Materials Processing (2014), Science Direct Elsevier), 1-2
[2] Mohamed, O. A.; Masood, S. H.; Bhowmik, J. L., Optimization of fused deposition modeling process parameters for dimensional accuracy using I-optimality criterion, Measurement, 81, 174-196 (2016)
[3] Masood, S. H.; Alamara, K.; Hasan, J., Developments in Scaffold design and manufacturing in tissue engineering, (Proceedings of International Conference on Applications and Design in Manufacturing Engineering (ICADME 2009). Proceedings of International Conference on Applications and Design in Manufacturing Engineering (ICADME 2009), Penang, Malaysia 11-13 Oct (2009)), pp 4I-1 to 4I-6
[4] Masood, S., Application of fused deposition modelling in controlled drug delivery devices, Assembly Autom., 27, 215-221 (2007)
[5] Grimm, T., Fused deposition modeling: a technology evaluation, Time-Compression Technol., 11, 1-6 (2003)
[6] Thrimurthulu, K.; Pandey, P. M.; Venkata Reddy, N., Optimum part deposition orientation in fused deposition modeling, Int. J. Mach. Tools Manuf., 44, 585-594 (2004)
[7] Nancharaiah, T.; Raju, D. R.; Raju, V. R., An experimental investigation on surface quality and dimensional accuracy of FDM components, Int. J. Emerg. Technol., 1, 106-111 (2010)
[8] Masood, S. H.; Mau, K.; Song, W., Tensile properties of processed FDM polycarbonate material, Materials Science Forum, 2556-2559 (2010), Trans Tech Publ
[9] Sahu, R. K.; Mahapatra, S.; Sood, A. K., A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic, J. Manuf. Sci. Prod., 13, 183-197 (2013)
[10] Kumar, G. P.; Regalla, S. P., Optimization of support material and build time in fused deposition modeling (FDM), Appl. Mech. Mater., 110, 2245-2251 (2012)
[11] Nancharaiah, T., Optimization of process parameters in FDM process using design of experiments, Int. J. Emerg. Technol., 2, 1, 100-102 (2011)
[12] Rayegani, F.; Onwubolu, G. C., 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 (2014)), 1-11
[13] Myers, R. H.; Montgomery, D. C.; Anderson-Cook, C. M., Response Surface Methodology: Process and Product Optimization Using Designed Experiments (2009), John Wiley & Sons · Zbl 1269.62066
[14] Ahmed, W.; Jackson, M. J., Emerging Nanotechnologies for Manufacturing (2009), William Andrew
[15] Coleman, S.; Greenfield, T.; Stewardson, D.; Montgomery, D. C., Statistical Practice in Business and Industry (2008), John Wiley & Sons · Zbl 1166.62090
[16] Montgomery, D. C., Design and Analysis of Experiments (2008), John Wiley & Sons
[17] ASTMD5418-07, Standard Test Method for Plastics: Dynamic Mechanical Properties: In Flexure (Dual Cantilever Beam) (2007), ASTM International: ASTM International West Conshohocken
[19] Mohamed, O. A.; Masood, S. H.; Bhowmik, J. L., Optimization of fused deposition modeling process parameters: a review of current research and future prospects, Adv. Manuf., 3, 42-53 (2015)
[20] Vining, G., Statistical Process Monitoring and Optimization (1999), CRC Press
[21] Dodson, B.; Hammett, P.; Klerx, R., Probabilistic Design for Optimization and Robustness for Engineers (2014), John Wiley & Sons
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.