Material Informatics Advanced Data Analytics Engineering Design How Changing Development Practices

Engineers and scientists are referring to it as a change, a change from the conventional method of relying on trial, and, error for material selection to a much more intelligent, data-driven method, material informatics. The recent article on AZoM details the manner in which advanced data analytics enhances the methods for materials design, testing, and selection in engineering applications.

What is material informatics?

Material informatics is a term that encompasses the use of big data, machine learning, and advanced analytics to understand, predict, and even optimize the material properties. The traditional way of doing everything in the lab and manual testing is already partly replaced with the use of big data and computational models which help to simulate the behavior of materials under any condition be it stress, heat change, or chemical attack.

Material informatics is thus put to use in a brilliant, quick method of material testing. To illustrate, in a situation where an engineer is in need for a material that is physically strong, light in weight, and resistant to corrosion, material informatics would be a good choice because it allows experiments to be done via computer simulations and the actualizing of the most likely candidates afterward.

Benefits for the engineering design process

Speed is one very important factor that could be improved. A traditional material testing cycle such as prototype, test, iterate, can take a few months if not more. However, data-driven analytics allows engineers to predict the outcomes of testing via computer simulation thus it is possible to do the testing of just one or two in the real world out of the choices generated. The time used in the design phase is therefore twice or thrice as short as before and the costs cut considerably.

Point precision is another. Material informatics has the potential to reveal the compromises and risks concealed in the performance of a material which may not be obvious even in a standard test. The data models can demonstrate the behavior of a certain material subjected to tough conditions or over a long time period which is useful in preventing failure in actual use.

The engineering design process pays as much attention to the question of how to support invention. With the increasing complexity of materials – composite, alloys, nanomaterials – traditional methods of testing may no longer be able to provide the explanation needed for all the variables. It is data analytics that can take the complexity onboard and at the same time allow the engineer to experiment with new materials and configurations which would have otherwise been too time-consuming or risky to test.

Implications across industries

The shift of this magnitude has effects, that is, significant, on the entire horizon. Among other benefits, the automotive and aerospace engineering sectors will experience the development of components which are not only stronger but also lighter. Within the construction sector, material informatics can be a powerful tool in the formulation of long-lasting and sturdy building materials. Also, in renewable energy and battery technology, the better the materials, the better the performance, safety, and life-span.

Producers and R&D departments that are keen on the informatics-driven design approach are the ones to reap the most benefits. They have an opportunity to quicken the pace of product development, lower the expenses and increase the reliability of the product. As a result, in the long run, products created utilizing this data-driven method will most probably be better than those relying on trial-and-error.

Challenges and the way forward

The adoption of material informatics cannot be likened to plug-and-play.

One needs either to build or to have access to big data repositories of materials, must have the power of computing to perform the analysis, and the human resource to interpret the results by way of engineers who are proficient in data analysis and interpretation. Also, data quality and standardization are of utmost importance – incorrect data can lead to completely wrong predictions.

Nevertheless, the payoff after a long period is still very attractive. The field can develop at a very fast pace due to the increasing number of organizations which are creating material databases and sharing their research with one another. Standards and facilities will be upgraded, more people will have access to computational devices, and soon data-driven engineering will not be an exception but the rule.

Final thought

Material informatics is an example of a paradigm shift in engineering design. By utilizing data and analytics, engineers are able to make good decisions in less time. Industrial sectors that compete on the fast pace of innovation and also depend on performance, cost-efficiency, and reliability will find this approach very helpful as it provides a clear way forward without much struggle. This trend is gaining ground and it is not long before one will realize that data-driven material design has changed the way we construct everything from cars to buildings to ​‍​‌‍​‍‌​‍​‌‍​‍‌batteries.