Investigation of parameters affecting the roughness and cylindricality of holes in PLA parts made of 3D printing by fused deposition modeling process using response surface method

Authors

1 Birjand University of Technology, Birjand , Iran

2 Birjand University of Technology , Birjand , Iran

3 Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

Abstract

In this paper, the effects of input parameters on the roughness and cylindricality of a hole were investigated using fused deposition modeling (FDM). The roughness and dimensional accuracy are very important for a 3D printed part. The investigated input parameters are injection speed, solid percentage, and layer thickness. The experiments were designed using the response surface method and 51 PLA samples were printed. To measure the roughness and dimensions of the manufactured products, a roughness-testing machine and a coordinate measuring machine (CMM) were used. The basis of this study is the comparison of roughness and deviation from the center to find the optimal set of the investigated parameters to achieve higher quality.
The optimal levels of parameters for the minimum simultaneous roughness (0.016 mm) and cylindricality (5.98 microns) are a layer thickness of 0.1 mm, injection speed of 40 mm / s, and solidity percentage of 15% when the pattern is triangular. The results also suggest that the layer thickness has the strongest effect on the surface quality in comparison with the injection speed and percentage of solidity.

Keywords

References

[1]  Arumaikkannu G, Anil kumar N, Saravanan R (2008) study on the influence of rapid prototyping parameters on product quality in 3d printing. India.
[2] Stephens B, Azimi P, El Orch Z, Ramos T (2013) Ultrafine particle emissions from desktop 3D printers. Atmos Environ 79: 334-339.
[3]  Postiglione G, Natale G, Griffini G, Levi M, Turri S (2015) Conductive 3D microstructures by direct 3D printing of polymer/carbon nanotube nanocomposites via liquid deposition modeling. Composites, Part A 76: 110-114.
[4] Mohamed OA, Masood SH, Bhowmik JL (2016) Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design. Appl Math Model 40(23-24): 10052-10073.
[5] Khan S, Fayazbakhsh K, Fawaz Z, Nik MA (2018) Curvilinear variable stiffness 3D printing technology for improved open-hole tensile strength. Addit Manuf 24: 378-385.
[6] Tanikella NG, Wittbrodt B, Pearce JM (2017) Tensile strength of commercial polymer materials for fused filament fabrication 3D printing. Addit Manuf 15: 40-47.
[7] Thrimurthulu K, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tool Manufact 44(6): 585-594.
 [8] Nancharaiah T, Raju DR, Raju VR (2010) An experimental investigation on surface quality and dimensional accuracy of FDM components. Int J Emerg Technol 1(2): 106-111.
 [9] Masood SH, Mau K, Song WQ (2010) Tensile properties of processed FDM polycarbonate material. Mater Sci Forum 654: 2556-2559.
[10] Sahu RK, Mahapatra SS, Sood AK (2013) A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic. J Manuf Sci Prod 13(3): 183-197.
[11] Gurrala PK, Regalla SP (2012) Optimization of support material and build time in fused deposition modeling (FDM). Appl Mech Mater 110: 2245-2251.
[12] Nancharaiah T (2011) Optimization of process parameters in FDM process using design of experiments. Int J Emerg Technol 2(1): 100-102.
[13] Rayegani F, Onwubolu GC (2014) Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). Int J Adv Des Manuf Technol 73(1-4): 509-519.
[14] Sheoran AJ, Kumar H (2020) Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: Review and reflection on present research. Mater Today: Proc 21: 1659-1672.
[15] Yadav D, Chhabra D, Gupta RK, Phogat A, Ahlawat A (2020) Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS. Mater Today: Proc 21: 1592-1604.
[16] Yadav D, Chhabra D, Garg RK, Ahlawat A, Phogat A (2020) Optimization of FDM 3D printing process parameters for multi-material using artificial neural network. Mater Today: Proc 21: 1583-1591.
[17] Nabipour M, Behravesh AH, Akhoundi B (2017) Effect of printing parameters on mechanical strength of polymer-metal composites printed via FDM 3D printer. Modares Mechanical Engineering 17(1): 145-150. (In Persian)
[18] Golmohammadi AH, Khodaygan S (2018) Comparison of analytical and experiment design-based optimization methods to determine the optimum part build orientation in rapid prototyping processes. Modares Mechanical Engineering 18(3): 115-125. (In Persian)
[19] Moradi M, Falavandi H, Karami Moghadam M, Meiabadi MSM (2020) Experimental investigation of laser cutting post process of additive manufactured parts of Poly Lactic Acid (PLA) by 3D printers using FDM method. Modares Mechanical Engineering 20(4): 999-1009. (In Persian)
[20] Moradi M, Karami Moghadam M, Shamsborhan M, Bodaghi M, Falavandi H (2020) Post-processing of FDM 3d-printed polylactic acid parts by laser beam cutting. Polymers 12(3): 550.
[21] Moradi M, Karami Moghadam M, Shamsborhan M, Bodaghi M (2020) The synergic effects of FDM 3D printing parameters on mechanical behaviors of bronze poly lactic acid composites. J Compos Sci 4(1): 17.
[22] Moradi M, Meiabadi S, Kaplan A (2019) 3D printed parts with honeycomb internal pattern by fused deposition modelling; experimental characterization and production optimization. Met Mater Int 25(5): 1312-1325.
Journal of Solid and Fluid Mechanics
Volume 11, Issue 3
October 2021
Pages 119-133

Files

Share

How to cite

Statistics