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Measure of polymer performance based on correlated physical parameters
Author(s) -
Gowtham Guttikatte Kariappa,
Somashekarappa Hanumanthappa,
Bharath Karthik,
Somashekar Rudrappa
Publication year - 2021
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.50705
Subject(s) - measure (data warehouse) , dopant , context (archaeology) , biological system , multivariate statistics , materials science , joint (building) , monte carlo method , computer science , statistical physics , statistics , mathematics , doping , physics , data mining , engineering , structural engineering , optoelectronics , paleontology , biology
We introduce a novel measure of performance of polymer composites based on physical parameters whose behavior depends on levels of dopant concentration used during their preparation. The performance measure is based on a joint analysis of measurements of the physical parameters exhibiting non‐trivial correlations that vary across different levels of dopant concentrations. In contrast to traditional multivariate analysis, we treat data from parameter measurements as being obtained from functional parameters, and develop the performance measure based on the joint average function of parameters that encodes the correlation structure. An optimal level of dopant concentration is then ascertained with respect to the performance measure. While the proposed measure is general enough to be applicable to any chosen physical parameters, we demonstrate its utility in the context of assessing performance using microstructural and nonlinear optical parameters. Computing of the measure and optimal dopant concentration are carried out using Monte Carlo sampling, which further facilitates uncertainty quantification.