Abstract
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.
Highlights
The industry adopted virtual twins in the form of simulation tools that represented the physics of materials, processes, structures, and systems from physics-based models.These computational tools transformed engineering science and technology to offer optimized design tools and became essential in almost all industries at the end of the 20th century.Despite of the revolution that Simulation Based Engineering—SBE—experienced, some domains resisted to fully assimilate simulation in their practices for different reasons: Computational issues related to the treatment of too complex material models involved in too complex processes, needing a numerical resolution difficult to attain
A model of a complex process could be envisaged with two main objectives: (i) the one related to the online process control from the collected and assimilated data; (ii) the other concerned by the offline process optimization, trying to extract the optimal process parameters enabling the target properties and performances
We showed in this paper that different machine learning techniques are relevant in the low-data limit, for constructing the model that links material properties and process parameters in reactive polymer processing
Summary
The industry adopted virtual twins in the form of simulation tools that represented the physics of materials, processes, structures, and systems from physics-based models.These computational tools transformed engineering science and technology to offer optimized design tools and became essential in almost all industries at the end of the 20th century.Despite of the revolution that Simulation Based Engineering—SBE—experienced, some domains resisted to fully assimilate simulation in their practices for different reasons: Computational issues related to the treatment of too complex material models involved in too complex processes, needing a numerical resolution difficult to attain. The industry adopted virtual twins in the form of simulation tools that represented the physics of materials, processes, structures, and systems from physics-based models. These computational tools transformed engineering science and technology to offer optimized design tools and became essential in almost all industries at the end of the 20th century. The extremely multi-parametric space defined by both the material and the process, where the processed material properties and performances strongly depend on several parameters related, for example in the case of reactive extrusion, to the nature of the reactants or the processing parameters, like the flow rate and viscosity, the processing temperature, etc
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