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 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.