Recycled polypropylene (rPP) exhibits large property variability due to mixed origins and degradation histories, complicating nondestructive grading. In this study, we propose an interpretable Bayesian framework that links X-ray diffraction (XRD) peak features to tensile modulus for virgin/recycled PP blends subjected to xenon-arc weathering. XRD profiles were analyzed by Bayesian peak deconvolution, extracting physically interpretable descriptors from four low-angle crystalline peaks (α(110), α(040), α(130), β(300)) and a broad amorphous halo, yielding 21 explanatory variables per sample. A Bayesian finite mixture of linear regressions with probabilistic feature selection was fitted and posterior inference using replica-exchange Monte Carlo was performed to explore a highly multimodal posterior. The model selected two clusters and achieved an in-sample fit (R2 = 0.81, RMSE = 145 MPa). Replicate-holdout group k-fold cross-validation provided a conservative generalization estimate at the tensile level (R2 = 0.15, RMSE = 320 MPa, N = 120), providing a conservative lower-bound estimate due to specimen mismatch and repeated labels at 0 cycles. Clusters differed in the β(300) descriptor space, and direct comparison of cluster-specific posterior coefficient distributions indicated that the β(300) peak position provided the clearest evidence of cluster-dependent regression behavior, whereas peak broadening was relevant in both clusters. These results suggest that β(300)-related descriptors – potentially reflecting β-phase lattice strain or local disorder – may contribute to modulus beyond β fraction alone. This framework provides interpretable XRD descriptors and uncertainty-aware modulus estimates for grading heterogeneous rPP.
Hammura et al. (2026) studied this question.