ARTIFICIAL INTELLIGENCE AND 3D PRINTING : A COMBINATION OF FUTURE?

ARTIFICIAL INTELLIGENCE AND ADDITIVE MANUFACTURING

Anyone who has been working with 3d printers, will surely accept how heartbreaking it is when you come back to the printer after leaving it to print for hours expecting your completed model, instead you see a failed print !!    

Just imagine , what if there was a magical hot end that would predict what could go wrong and accordingly adjust its own temperature and redefine other parameters to give a perfect print with no defects? Sounds amazing right. This is what applying Artificial intelligence and machine learning algorithms to additive manufacturing technologies will give us.
At the beginning these fields might sound like two different unconnectable dots, but we all know that artificial intelligence is a promising field with a vast range of applications. Let us understand the role of Artificial intelligence in the field of additive manufacturing in this blog.

APPLICATIONS OF AI IN AM

Application of the AI algorithms into the AM field can be discussed based on the implementation of the technology at various stages of the printing process. In that order first comes the preparation of the model for printing. Based on the success ratio of the settings used in the previous models, the AI algorithm will be able to come up with a customized print setting for your printer and the model.

DEFECT DETECTION: Porosity or irregular surface finish, a technology to predict these beforehand and change the settings to avoid those issues will be a major life savior. It will save a lot of cost ,material, time and the effort put into manual inspection of parts and postprocessing. By understanding the relation between the thermal history of the model and the surface defects that occur during printing , ML based models can predict defects by constantly observing data from the in situ infrared thermal cameras.

Porosity defects in titanium 3D parts.

CLOSED LOOP CONTROL: We all understand the basic definition of the term closed loop control system i.e., to have a device that automatically regulates its process parameters and other variables to a desired state by itself without any human interaction and by creating its own feedback. Using ML based algorithms, researchers are trying to modify the parameters in real time while the print is still in between printing from data it receives through sensors.

NEW MATERIALS:AI is enabling the automation of new material discovery processes. While a human can come up with a formulation and test them through a few iterations, the machine can come up with multiple materials and do a lot more iterations at the same time and give accurate combinations and formulations using the ML algorithms that the human cannot even think of.

MIT's new material formulation technology using AI.

GENERATIVE DESIGN: One major advantage of AM is to easily make complex designs.to exploit this feature of additive manufacturing in the right way, the designs need to be aptly designed and one such great concept is the idea of generative designs. By feeding in a few basic constraints regarding the strength , weight , material , cost or any other needed specification we can get the software to create multiple design solutions. All this happens based on the concept of cloud computing and ML algorithms.

FUSION 360's generative design

WORKFLOW MANAGEMENT: We will be able to manage the entire workflow, from request management, printability analysis and machine analytics to production scheduling, post-processing management and communicating with suppliers. AI plays a major role in automating this whole process.

By trying to understand this concept deeper , my interpretation is that,
We are able to get all the different varieties of data that we need through various sensors. This collected data is then evaluated using AI and ML algorithms. The game changer is that these algorithms are able to correctly identify the hidden relationships between various parameters that are not easily recognizable to humans. This is precisely where the advantage of artificial intelligence lies: it is able to process very large volumes of data quickly, a task that is far too tedious for a human.

Although we are now able to automate certain steps, we will still need human intervention for a few other individual tasks like post-processing. So, We are still a long way from using artificial intelligence to control the entire additive manufacturing process. But we can surely see this as a promising technology in automating the process and making it more easier and reliable for industries to adapt than just seeing AM as a prototyping process.