Artificial Intelligence and 3D Printing

(Photo Credit above: Dr. Tim Anderson)

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Artificial Intelligence (AI) is the leading field of science nowadays.
Machines can be programmed to learn and complete tasks without human supervision. 

In other words, artificial intelligence is a self-learning system that can work with specific problems and make independent intelligent decisions (Chace, 2018). AI is applied to various fields of modern technologies and can be implemented to the manufacturing industry. The most innovative way of production is additive manufacturing, especially 3D printing.

The best advantage of 3D printing is unsupervised complex manufacturing. Various polymer, metal, and biomaterials are used in engineering applications mainly to create products with unique shapes, multifunctional compositions, reliability, and high quality. The 3D printing includes various techniques, but the most useful and time-tested method is Stereolithography (SLA). 


Figure 1. Schematic of an SLA 3D printer (Varotsis, 2018).

SLA is the 3D printing process in which ultraviolet laser shoots on the surface of a tank filled with the photopolymer liquid. Energy, transferred by the laser to the material, activates curing reaction, which leads to the solidifying of the pattern traced on the photopolymer. Next, the build platform moves on the distance equal to the thickness of the one layer (Varotsis, 2018). The next layer is cured joining the previous layer. This procedure is repeated until the object is finished. However, every manufacturing method has its own problems and the risk of product failure.


What can cause failure during the SLA 3D printing process?
There are several reasons including:

  • Photopolymer material failure 
  • The UV-laser wavelength or scanning intensity change
  • Curing (photopolymerization) reaction violation

These problems can be solved with the help of artificial intelligence.


Material, which is used in SLA, is called photopolymer and the name of the curing reaction is photopolymerization.

Figure 2. Polymerization (MIT, 2018).

Steps of reaction (Terselius, 1998):

  1. Radical formation: Radicals are formed under the exposure of the UV light
  2. Propagation: energized photoinitiators create potential bonds
  3. Termination: ends of the polymer chains face each other resulting in the rapid growth of the polymer chain

In the end, active groups are not able to create new bonds anymore, which means that the polymer chain grows process is terminated. 


Figure 3. The relation between laser intensity, voxel size, and success of polymerization (Ligon, 2017).

Resin needs the right amount of energy to achieve solidification. If the material receives not enough UV energy or the laser is spending less time for the curing process, then the print will not have appropriate characteristics to meet the application requirements (Jennings, 2018). The most optimal solution is to decrease the speed of printing by modifying laser settings (Jennings, 2018).

The second problem is associated with the lack of an appropriate amount of energy needed for the curing process (Jennings, 2018). However, an  AI system can modify the settings of the laser by increasing energy gradually to avoid abrupt changes during the 3D printing process.

Strength of the photopolymer can be increased by adjusting the laser in two ways (Ligon, et al., 2017): 

  • Lowering the penetration depth
  • Increasing the amount of energy
Figure 4. Representation of gel curing profiles (Jim H. Lee, et al., 2001).

The penetration depth is the depth to which laser penetrates the photopolymer material layer and defined by (Ligon, et al., 2017):

Dp=Penetration depth  ε=Molar extinction coefficient  I=Photoinitiator concentration 

Penetration depth is reduced by adding light absorbers which change the formula (Ligon, et al., 2017):

I;A=Extinction coefficient  I=Photoinitiator concentration  A=Concentration of the absorber

UV absorbers increase the building time, but they improve resolution and strength. Penetration depth reduction is extremely important for the improvement of the resolution allowing the creation of thinner layers.

Critical exposure Ec is the energy needed to start the solidification reaction, which is defined as (Ligon, et al., 2017):

Ec=Critical exposure E0=Energy amount on the surface Cd=Curing depthDp=Penetration depth 

Formulas can be used to create an equation system for further implementation in the program of adjustment of the UV laser settings to control the material curing reaction. The aim is to achieve the right amount of energy which is needed to obtain successful solidification and meet the optimal properties of the final product.


Possible concept of AI implementation in SLA includes layer scanning system, collection of the information, analysis, and solution to fix the failure without interruption of the 3D printing process (Bharadwaj, 2018). 

The failure is fixed at the earliest stage. The machine identifies divergence from the design and solves the problem as soon as the failure starts to appear. Therefore, the sensitivity of the scanning system and the reaction of the machine defining the errors should be developed.

The other way to fix production failure is to create a 3D printer that could remove material from the failed region. 

Next, AI should analyze the problem and find another way to build the part without changing final product properties.

The possible set of equipment for the creation of such technology includes:

  • SLA 3D printer
  • Sensors
  • Scanning cameras
  • Focused laser beam
  • Machine learning algorithm
  • Software

The software for the machine learning system is created with the machine coding which is very primitive, but complex at the same time. Machine code is the native code that a machine can read and execute to complete the specific task (Rouse, 2018). 

Additive manufacturing is based on the adding material layer by layer. 3D printers do not remove material layers. However, the failure happens in the printed layer which can be removed, predicted or enhanced.

In the first case, the material should be removed with high accuracy. The tool which can be used for such an application is the focused laser beam (Peels, 2017). Technology requires a laser that can move in three-dimensional space. The laser should be parallel to the printed layers to cut each layer from the side. Problematic layers should be analyzed by the scanning system and removed, interrupting the printing for a very short period of time, then the printing process should be continued. Despite the fact that the print can fail many times, the product will be successfully finished, and the machine will learn a lot from the problematic print at the end. The learning algorithm is enclosed, and the program will be repeated until reaching a successful result.

AI systems should be created to analyze large sets of data and make an instant decision, whereas humans are not able to react faster than the computer, just in a few seconds.

The system should repeat the process of material removing before the machine will learn how to fix the problem. This procedure improves the fixing process with prediction analysis and decreases the probability of the failure.


Machine learning improves the printing quality reducing risks of failure and manufacturing waste. The recycling in the field of additive manufacturing should be minimized with zero waste production with the implementation of AI. Also, there are a lot of possible ways that can be developed to protect the printing data and digital security system due to AI technologies.


Chace, C., 2018. Artificial Intelligence and the Two Singularities. 1st ed. Boca Raton: CRC Press.

Varotsis, A. B., 2018. Introduction to SLA 3D Printing. [Online]
Available at:

[Accessed 12 June 2018].

Terselius, B., 1998. Introduction to Polymer Science. 1st ed. Kristianstad: Arkitektkopia S. Niklasson AB.

Jennings, A., 2018. 3D Printing Troubleshooting Guide: 41 Common Problems. [Online]
Available at:
[Accessed 6 October 2018].

Ligon, S. C. et al., 2017. Polymers for 3D Printing and Customized Additive Manufacturing. Chemical Reviews, 117(15), pp. 10212-10290.

Bharadwaj, R., 2018. Artificial Intelligence Applications in Additive Manufacturing (3D Printing). [Online]
Available at:
[Accessed 12 September 2018].

Rouse, M., 2018. Machine code (machine language). [Online]
Available at:
[Accessed 17 September 2018].

Peels, J., 2017. Comparison of Metal 3D Printing — Part Two: Directed Energy Deposition. [Online]
Available at:
[Accessed 6 August 2018].


Figure 1. Schematic of an SLA 3D printer (Varotsis, 2018).

Figure 2. Polymerization (MIT, 2018).

Figure 3. Relation between laser intensity, voxel size, and success of polymerization (Ligon, 2017).

Figure 4. Representation of gel curing profiles. Laser penetrates deeply but only lightly cross-links the gel. (Jim H. Lee, et al., 2001).

About the Author:

Reino Iuganson graduate student from Arcada university with a Bachelor’s Degree in Materials Engineering (Helsinki, Finland). He started his work with the injection molding industry in 2016 at the Plastoco company, then developed a prototype for a product design project in collaboration between Arcada university and Laerdal medical company using CAD/CAM and 3D printing. He is currently working on the project Artificial Intelligence in 3D printing.

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