Abstract
Developing optimal flame retardant polymer compositions that meet all aspects of a given application is energy and cost-intensive. To reduce the number of measurements, we developed an artificial neural network-based system to predict the flammability of polymers from small-scale test data and structural properties. The system can predict ignition time, peak and total heat release, and mass residue after the burning of reference and flame retarded epoxy resins. Total heat release was predicted most accurately, followed by the peak heat release rate. We ranked the input parameters according to their impact on the output parameters using a sensitivity analysis. This ranking allowed us to establish a relationship between the input and output parameters taking into account their physical content.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
