Prediction of Acrylonitrile−Butadiene−Styrene Mechanical Properties through Compressed-Sensing Techniques
article
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 determine the minimal subset of properties to
measure to achieve high reconstruction accuracy for the remaining nonmeasured properties. The
analysis showed that using only six measured properties, an average reconstruction error of less
than 5% can be achieved. In addition, by increasing the number of measured properties to nine,
an average error of less than 3% was achieved. Compressed sensing enables experts in academia
and industry to substantially reduce the number of properties that must be measured to fully and accurately characterize plastics, ultimately saving both costs and time. In future work, the method should be expanded to optimize not only individual properties but also entire tests used to simultaneously measure multiple properties. Furthermore, this approach can also be applied to recycled
materials, of which the properties are more difficult to predict.
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 determine the minimal subset of properties to
measure to achieve high reconstruction accuracy for the remaining nonmeasured properties. The
analysis showed that using only six measured properties, an average reconstruction error of less
than 5% can be achieved. In addition, by increasing the number of measured properties to nine,
an average error of less than 3% was achieved. Compressed sensing enables experts in academia
and industry to substantially reduce the number of properties that must be measured to fully and accurately characterize plastics, ultimately saving both costs and time. In future work, the method should be expanded to optimize not only individual properties but also entire tests used to simultaneously measure multiple properties. Furthermore, this approach can also be applied to recycled
materials, of which the properties are more difficult to predict.
Topics
TNO Identifier
1005334
Source
Journal of Chemical Information and Modeling, 64, pp. 7257-7272.
Pages
7257-7272