Prediction of Acrylonitrile-Butadiene-Styrene Mechanical Properties through Compressed-Sensing Techniques.

ABSTRACT: One of the challenges in the plastic industry is the cost and time spent on the characterization of different grades of polymers. Compressed sensing is a data reconstruction method that combines linear algebra with optimization schemes to retrieve a signal from a limited set of measurements of that signal. Using a data set of signal examples, a tailored basis can be constructed, allowing for the optimization of the measurements that should be conducted to provide the highest and most robust signal reconstruction accuracy. In this work, compressed sensing was used to predict the values of numerous properties based on measurements for a small subset of those properties. A data set of 21 fully characterized acrylonitrile-butadiene-styrene samples was used to construct a tailored basis to dete

SUBMITTER: Poort J 

PROVIDER: S-EPMC11481090 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature