the extrusion process is well suited for the processing of highly viscous materials, the high shear can still cause self-heating of the material and possible side reactions. In addition, the short residence time limits the number of possible reactions, and finally, the scale up to industrial pilot and plants comes with many difficulties. The nature of the reactive extrusion process involves a complex flow and a large number of parameters, dependent variables and phenomena that make it difficult to understand and therefore to control and to optimize. In response to this complexity, various strategies have been adopted for the modeling and simulation of reactive extrusion. [5] Two main methodologies can be identified. The first strategy is based on a chemical engineering approach. This method consists in considering the extruder as a succession of ideal chemical reactors, which number and nature depend on the screw profile geometry. A global balance can then lead to an approximation of flow conditions. This method has been used in several studies, and among them, Choulak et al. used it to develop a dynamic 1D model for automatic control of reactive extrusion. [7] This approach is especially well adapted for automatic control because of its fast execution due to the simplifications and its ability to perform in transient regime. However, it requires to adjust parameters to each situation so it cannot be easily used as a predictive tool or to solve scale up issues. The second strategy is a local description of the flow field based on continuum mechanics. It thus reproduces real conditions without ideal representation. The flow is indeed simulated by resolving classical mechanics equations relatively to local geometry, kinematics, and boundary conditions. These models can therefore be totally predictive and can be used for process design, but also for its optimization and predictions in case of a change in the process. Whereas it is a more flexible and accurate way to simulate the extrusion process, it requires a lot of time and computing power and is consequently not adapted for automatic process control. As the flow in the extruder is unsteady, not isothermal, and 3D, it is more accurate to use a 3D local simulation. But in some cases it appears that a 1D local description of the flow and temperature field at steady-state can be sufficient for most engineering issues. [8] In addition, it allows creating a software easily usable for process predictions without needing excessive time or computing power. We used for our simulation the LUDOVIC software developed by Vergnes et al. for twinscrew extrusion that uses this 1D local description. [9] Actually, The purpose of this paper is to combine a classical 1D twin-screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin-screw extrusion are studied for this purpose. The twin-screw extrusion simulati...
This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.
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