Data-based engineering science and technology of polymers and filled polymers – Data-BEST

The polymer industry is typically facing these new challenges for the production of new materials with new functional and integrated properties in different domains of applications. Nowadays, although new technologies are emerging (3D printing) polymer processing is still based on conventional approaches in terms of modeling, simulation and scale-up for the production. So, can we imagine new original and realistic ways based on the recent developments on artificial intelligence ?

Traditionally, when a material in its in-service conditions was considered, a constitutive equation was selected (among different possibilities) guided by the accumulated experience and/or the experimental observations, and then calibrated in order to identify all the parameters that it contains. This procedure faces two main difficulties: (i) In general establishing a constitutive equation needs time and the intuition and accumulated knowledge of experienced specialists; and (ii) Even when accurate constitutive equations could be derived by spending a sufficient material and intellectual effort, the recent explosion of material proposals requires a new procedure to describe material properties in its in-service conditions easier and faster.

In the opinion of the applicants, Industry 4.0 needs a change of paradigm in the description of materials and processes as just described. It is also important to note that here we are not replacing or claiming the replacement of experienced specialists, because deciding the data to be collected, the one that describes the state of the material in the process, etc … needs a strong and mature experience and knowledge, that is DATA-DRIVEN IS NOT ONLY DATA, IT IS DATA IN THE RIGHT HANDS, and only in these circumstances data become information to finish as knowledge.

The biggest challenge and main originality that the present research project proposal embraces, could then be formulated as follows:

- Can simulation proceed directly from data by circumventing the necessity of establishing a constitutive model in the traditional sense? Of course balance equations are kept, whereas the effort in establishing mathematical expressions of (too) complex constitutive equations is relaxed.
- How data-based modeling can enrich the establishment of the models (viewed as mathematical objects) describing the behavior? It is important to note that the main advantage of models is their capability of predicting outside the domain that served for their establishment (the so-called extrapolation) where data-based modeling encounter difficulties to make the job.
- How models can help data to be consistent with principles (convexity of potentials, …), filtering measurements noise, determine the quantities to be measured (electrical conductivity, shear and elongational viscosities, tensile-strength properties, ….) or announcing the necessity of using internal variables, …
The main project tasks/objectives to be accomplished are:

- To propose data-based model learners able to discriminate conservative and dissipative behaviors in the case of polymers and reinforced polymers;
- To couple model learners with rheometers for simple flows;
- To define strategies for collecting data from more complex flows exhibiting shear and elongational behaviors;
- To describe the mechanisms of mixing and dispersions of a minor phase in terms of reinforcement and specific properties (electromagnetic shielding for example)
- To address the issue related to objectivity (frame indifference) in a data-driven framework;
- To verify the data-driven approach by emulating the real scenario from model-based simulations;
- To validate the approach at the laboratory scale and at the different scales of processing.

These objectives will be achieved in the framework of the developments of new materials that constitutes a major advantage of data-driven approaches:
- Polymer melt processing
- Reactive polymers extrusion
- Filled polymers with conductive fillers