Predicting image processing-normalized DL-EPRT performance from Rudimentary Cihal method-based counterpart

• Innate discrepancy in grain boundary dimensions accounted for in normalized EPRTs. • Intricate image processing normalized DL-EPR forecasted from inferior peers. • Cihal method predicated upon grain boundary area mimics image processing variant. • Predictive models subjected to cross-validation appear linear for SL- and DL-EPRT. Conventional DL-EPRT was extrapolated into accounting for inherent inconsistencies amongst grain boundary (GB) dimensions whilst correlating two distinct methods of i r /i a normalization, namely the approximate Cihal method (CM) and the relatively involved image processing (IP). Inasmuch as facilitating the forecast of IP- from CM-based normalizations, the full spectrum of plausible predictive models was subjected to cross validations that revealed those emanating from GB length-based endeavors to be quadratic with R 2 and mean error (E) of 0.9215 and 0.5769, respectively. Meanwhile, GB area normalizations succumbed to a linear polynomial yielding R 2 and E values of 0.8795 and 0.3664, respectively.