3D Objects, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- An Introduction


Many 3D printers provide a high print resolution, suitable for developing high-fidelity prototypes from a computer aided design model. One of the most widely available printing processes is Fused Filament Fabrication (FFF) type, also known by its trademarked term, Fused Deposition Modeling (FDM) in common literature.  Intricate shapes can be printed through FDM printers such as airfoil and Moebius strips [7]. FFF prototype surfaces can be enhanced on a millimeter scale even when they have geometric textures [4]. However, it is common practice among engineers and designers to build the low-fidelity versions first, as a proof of concept. There are several low-fidelity FFF printers available in the market. They can be used with a wide range of materials. But the most frequently used materials are spools of polylactic acid (PLA) as they are less toxic [8,9,13,23]. Due to the ease in their operation, portability, and abundant materials, 3D printers are designed to have fairly good environmental features, making them practical in educational institutions [6]. However, they can be made more sustainable [11] and economical [17] through material waste reduction.

Experimental studies have showcased the properties of different materials or different colors [23] while investigating effects of individual factors on the printed object. Each study focuses on at least one parameter and one material to show its impact on the quality of the final product. Poor surface finish is often caused by tessellation of the computer aided design file and slicing processes. However, the surface roughness can be reduced by modeling a design through optimizing the parameters before fabrication [14,22,26].

Printing of the first layer is crucial, as uneven material deposition on the first layer can change the specimen height of other layers [14]. Surfaces of the printed objects, especially ones which are textured, tend to show the staircase effect, where each printed layer is distinctly visible and looks like a staircase [10]. It is an undesired side effect in low fidelity 3D printers.

Parameters such as build direction, temperature of the extruder, and layer height play a major role in showing dimensional accuracy when compared with infill pattern [3]. The quality of geometry of the product also depends on print speed and layer height [20]. The surface roughness is also affected by the wall thickness of the printed object [21]. Although part build orientation affects mechanical properties such as tensile fatigue of the PLA material [2], this study focuses on the surface quality and dimensions of the objects.

Using appropriate design rules while building prototypes can save the hassle of wasted material, time, and costs associated with them. Current design rules exist only for certain boundary conditions and does not include all types of printing processes [1]. Statistical and engineering process control can be used to detect and correct the variation in the fabrication process [15]. The cost benefits of 3D printing are industry specific. However, material costs make up to 12% of the total costs in additive manufacturing. On top of that quality assurance costs need to be considered [17].

PLA is inexpensive, but wasting it should not be encouraged. Because of poor design choices, material type, amount of infill, and several other factors, many prints fail, and many do not appear as expected by the user. In other words, they do not have good quality of print. Hence, hundreds of printed objects are discarded and can easily affect the environment, making the process less sustainable, unless properly recycled [8]. But recycling can affect the material, which could, in turn, affect the print quality made using the recycled material [8,25]. So, this study shows a way for carefully planning the 3D printing process by using the most favorable settings, to obtain the best possible results without unnecessarily wasting filaments.

Existing approaches use Analytical modeling [22], Taguchi method [3] and factorial designs [4,14,21] to determine dimensional flaws, and X-ray tomography [5,12] or scanning electron microscopy [24] to determine internal and morphological flaws. In the current investigation, the print material was chosen as PLA because it has consistently been proven to print with ease [13] and is not toxic. To reduce the waste from rejected prints, this study uses a 2k factorial design to obtain a range of optimal print settings. An X-ray tomography is also performed to determine and analyze the unevenness of the print layers and surface quality.



  1. Adam G A O, Zimmer D (2015) On design for additive manufacturing: evaluating geometrical limitations, Rapid Prototyping journal, 21/6:662-670. DOI 10.1108/RPJ-06-2013-0060
  2. Afrose F M, Masood S H, Iovenitti P, Nikzad M, Sbarski I (2015) Effects of part build orientations on fatigue behavior of FDM-processed PLA material, Progress in Additive Manufacturing 1: 21. DOI: 10.1007/s40964-015-0002-3
  3. Alfaghani A, Qattawi A, Alrawi B, Guzman A (2017) Experimental Optimization of Fused Deposition Modelling Processing Parameters: a Design-for-Manufacturing Approach, Procedia Manufacturing, Open Journal of Applied Sciences, 7, 291-318. DOI 10.4236/ojapps.2017.76024
  4. Armillotta A (2006) Assessment of surface quality on textured FDM prototypes, Rapid Prototyping Journal 12/1:35-41. DOI 10.1108/13552540610637255
  5. Babout L (2006) X-Ray Tomography Imaging: A Necessary Tool for Material Science. Automatyka 10:117–124
  6. Bill V, Fayard A (2017) Building an Entrepreneurial and Innovative Culture in a University Makerspace. URL https://peer.asee.org/27985, accessed 17 July 2017
  7. Boschetto A, Veniali F (2010) Intricate Shape Prototypes Obtained by FDM, International Journal of Material Forming 3/1:1099-1102. DOI 10.1007/s12289-010-0963-1
  8. Cruz Sanchez F A, Lanza S, Boudaoud H, Hoppe S, Camargo M (2015) Polymer Recycling and Additive Manufacturing in an Open Source Context: Optimization of Processes and Methods. pp 1591–1600
  9. Cuiffo M, Snyder J, Elliott A, Romero N, Kannan S, Halada G P (2017) Impact of The Fused Deposition (Fdm) Printing Process on Polylactic Acid (PLA). Chemistry and Structure Appl Sci 7:579. DOI 10.3390/app7060579
  10. Di Angelo L, Di Stefano P, Marzola A (2017) Surface quality prediction in FDM additive manufacturing, International Journal of Advanced Manufacturing Technology 93: 3655. DOI 10.1007/s00170-017-0763-6
  11. Freitas D, Almeida H A, Bártolo H, Bártolo P J (2016) Sustainability in extrusion-based additive manufacturing technologies. Progress in Additive Manufacturing 1:65–78. DOI 10.1007/s40964-016-0007-6
  12. Gajdoš I, Slota J (2013) Influence of Printing Conditions on Structure in FDM Prototypes. Tehnički vjesnik 20:231–236.
  13. Garlotta D (2001) A Literature Review of Poly(Lactic Acid). Journal of Polymers and the Environment 9:63–84. DOI 10.1023/A:102020082
  14. Galantucci L M, Bodi I, Kacani J, Lavecchia F (2015) Analysis of dimensional performance for a 3D open-source printer based on fused deposition modeling technique, Procedia CIRP 28:82-87. DOI 10.1016/j.procir.2015.04.014
  15. Huang T, Wang S, He K (2015) Quality Control for Fused Deposition Modeling Based Additive Manufacturing: Current Research and Future Trends, The First International Conference on Reliability Systems Engineering. DOI 10.1109/ICRSE.2015.7366500
  16. Jensen M, Wilhjelm J E (2007) X-Ray Imaging: Fundamentals and Planar Imaging. URL-http://www2.compute.dtu.dk/courses/02511/docs/X-RayAndCT.pdf, accessed 17 July 2017
  1. Lindermann C, Jahnke U, Moi M, Koch R (2012) Analyzing Product Lifecycle Costs for a Better Understanding of Cost Drivers in Additive Manufacturing, 23rd Annual International Solid Freeform Fabrication Symposium. pp 177-188
  2. Mitra A (2012) Fundamentals of Quality Control and Improvement, seventh edn. John Wiley & Sons, Inc., Hoboken, New Jersey
  3. Montgomery DC (2013) Design and Analysis of Experiments, eighth edn. JohnWiley & Sons, Inc., Hoboken, New Jersey
  4. Polak R, Sedlacek F, Raz K (2017) Determination of FDM Printer Settings with Regard to Geometrical Accuracy, Proceedings of the 28th DAAAM International Symposium. pp 561-566
  5. Pérez M, Medina-Sánchez G, Garcia-Collado A, Gupta M, Carou D 2018 Surfaace Quality Enhancement of Fused Deposition Modeling (FDM) Printed Samples Based on the Selection of Critical Printing Parameters, Materials 11:1382
  6. Rahmati S, Vahabli E (2015) Evaluation of analytical modeling for improvement of surface roughness of FDM test part using measurement results, International Journal of Advanced Manufacturing Technology 79:823-829. DOI 10.1007/s00170-015-6879-7
  7. Redwood B, Schöffer F, Garret B (2017) The 3D Printing Handbook: Technologies, Design and Applications, first edn. 3D Hubs, Amsterdam
  8. Valerga AP, Batista M, Puyana R, Sambruno A, Wendt C, Marcos M (2017) Preliminary Study of PLA Wire Colour Effects on Geometric Characteristics of Parts Manufactured by FDM. Procedia Manufacturing 13:924–931. DOI 10.1016/j.promfg.2017.09.161
  9. Wittbrodt B, Pearce J M (2015) The Effects of PLA Color on Material Properties of 3-D Printed Components. Additive Manufacturing 8:110–116. DOI 10.1016/j.addma.2015.09.006

6 thoughts on “Optimizing 3D Prints- An Introduction”

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s