Background[edit | edit source]
This page is dedicated to the literature review of current methods to identify existing and new 3D printing material.
References[edit | edit source]
A novel approach to obviousness: An algorithm for identifying prior art concerning 3-D printing materials[edit | edit source]
Pearce, J. M. (2015). A novel approach to obviousness: An algorithm for identifying prior art concerning 3-D printing materials. World Patent Information, 42, 13–18. https://doi.org/10.1016/j.wpi.2015.07.003
- Algorithm to identify possible 3D materials given input (all natural chemicals and compounds, all man-made chemicals and functional agent), resulting in numerous combinations of mixtures
- Case 1: Algorithm can help narrow search for 3D-printable materials with specific desired properties according to business case situation
- Case 2: Algorithm can prevent patents from being placed on any possible outputs, which represent 'obvious' inventions and can be classified as prior art
Accelerated discovery of 3D printing materials using data-driven multiobjective optimization[edit | edit source]
Erps, T., Foshey, M., Luković, M. K., Shou, W., Goetzke, H. H., Dietsch, H., Stoll, K., von Vacano, B., & Matusik, W. (2021). Accelerated discovery of 3D printing materials using data-driven multiobjective optimization. Science Advances, 7(42), eabf7435. https://doi.org/10.1126/sciadv.abf7435
- Use of multiobjective optimization algorithm which determines how to mix the primary material formulations to develop better performing materials
A review on Machine Learning in 3D printing: Applications, Potential, and Challenges[edit | edit source]
Goh, G. D., Sing, S. L., & Yeong, W. Y. (2021). A review on machine learning in 3D printing: Applications, potential, and challenges. Artificial Intelligence Review, 54(1), 63–94. https://doi.org/10.1007/s10462-020-09876-9
- ML algorithms can be applied to search for new 3D printing materials with specific performance features (i.e. high compression and tensile properties, strong crack resistance and toughness, short setting time and high setting strength) by training the ML models to detect features and patterns from a large database of available material properties
- Training a large data set can be computationally expensive and time-consuming
Constitutive parameter identification of 3D printing material based on the virtual fields method[edit | edit source]
Dai, X., & Xie, H. (2015). Constitutive parameter identification of 3D printing material based on the virtual fields method. Measurement, 59, 38–43. https://doi.org/10.1016/j.measurement.2014.09.033
Materials discovery and design using machine learning[edit | edit source]
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017). Materials discovery and design using machine learning. Journal of Materiomics, 3(3), 159–177. https://doi.org/10.1016/j.jmat.2017.08.002
