Evaluation of optimally windowed chirp signals in industrial rheological measurements: method development and data processing

Abstract

The optimally windowed chirp (OWCh) methodology offers an alternative to traditional discrete frequency sweeps, acquiring complete rheological spectra in seconds while preserving data density and accuracy. For thermorheologically simple materials, OWCh accelerates data collection, enabling rapid creation of time–temperature superposition (tTS) master curves, potentially saving hours of instrument time. For mutating materials, such as those undergoing curing, OWCh facilitates detailed rheological characterization of viscoelastic properties throughout these transition events. We implemented OWCh within an industrial analytical research framework using commercially available rheometers. This integration is enhanced by two custom Python packages, piblin and hermes-rheo, which streamline and automate analysis of rheological datasets. For thermorheologically simple materials, this framework reduces tTS master curve data collection time by 40% while increasing data density by an order of magnitude. For mutating materials, we leverage the mutation number to design OWCh waveforms, effectively probing the characteristic timescale of fast thermomechanical transitions during curing experiments.