MIT uses artificial intelligence to automate the 3D printing material recognition process
Artificial intelligence, more precisely machine learning, is finding its way into various applications in the additive manufacturing industry. This time around, researchers at MIT have used the data-driven nature of machine learning to automate the process of discovering new 3D printing materials. Machine learning optimized material performance factors such as toughness and compressive strength using an algorithm that quickly outperformed traditional 3D printing material formulation methods. As a result of this study, the researchers created a free, open source materials optimization platform called AutoOED, which allows other researchers to do their own material optimization.
The algorithm was able to suggest new chemical formulations that a human probably would not have considered. “Material development is still a very manual process. A chemist walks into a laboratory, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But instead of having a chemist who can only do a few iterations over a period of days, our system can do hundreds of iterations over the same period, ”explains Mike Foshey, co-lead author of the paper and mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
3D printing materials
Six chemicals were identified for use in the formulations, then the goal of the algorithm was set to identify the best performing material that would be controlled for toughness, stiffness and strength. With regard to the automation of the material identification process, this MIT methodology enables the steps of dosing, mixing, 3D printing, post-processing and testing to be completed without human intervention. However, manual labor would be required to transfer materials between different steps in the sample preparation pipeline – however, Foshey believes that robots could be integrated to eliminate human labor in future versions of this system. After testing 120 formulations, the study resulted in 12 optimized formulations – with the researchers concluding that their research methodology could be generalized for application to other material design systems, enabling automated discovery in other material sciences.
The AutoOED platform mentioned above contains the optimization algorithm from this study and is currently available as a software package. This study was supported by the German company BASF, the largest chemical producer in the world. You can learn more about this study on 3D printing materials HERE.
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