An expanded and adaptable portfolio of materials will drive the 3D printing market over the next few years.
Materials informatics is an new field involving data-centric approaches to materials R&D which is impacting numerous sectors, including that of 3D printing. It is already proving to be a natural collaboration between these two emerging technologies.
IDTechEx forecasts the market to be worth $18,4-billion by 2030 for 3D printing materials alone, and the market activity from major companies clearly demonstrates this opportunity as they position themselves to get a significant market share.
There have been announcements from chemical giants including BASF, Evonik, Mitsubishi Chemical, DSM, and many many more over recent years.
The polymer market is expected to see a period of consolidation, inevitable with a maturing technology, but that does not mean the material market’s evolution is anywhere near complete.
The metal additive manufacturing market is anticipated to grow to $15,5-billion by 2030 after a period of decline brought upon by the Covid-19 pandemic. There is a large amount of change in this field; within the powder supply chain, there have been multiple targeted acquisitions and expansions, and there is still a large amount of technology innovation and progression typified by the binder jetting and bound metal developments.
Beyond metals and polymers, there is a huge amount of attention going into ceramics, composites, multi-material solutions, and more, all with their own challenges and disruptors.
One of the key barriers to adoption across the field has been the range of materials available and their properties. Designers are used to having a huge range of materials to choose from, and having that selection dramatically shrunk has hindered the market impact. Materials still need to be engineered for each printing process and application, for which the race is on.
Materials informatics (MI) is based on using data infrastructures and leveraging machine learning solutions for the design of new materials, the discovery of materials for a given application, and/or the optimisation of how they are processed.
MI can accelerate the “forward” direction of innovation (properties are realized for an input material), but the idealised solution is to enable the “inverse” direction (materials are designed given desired properties or processing criteria).
This is not straightforward and is still at a nascent stage. In many cases, the data infrastructure is not comprehensive, and MI algorithms are often too immature for the given experimental data.
The challenge is not the same as in other AI-led areas (such as autonomous cars or social media), the players are often dealing with sparse, high-dimensional, biased, and noisy data; leveraging domain knowledge is an essential part of most approaches.
3D printing has presented an obvious target for the use of materials informatics and is already producing some very promising results.