  Lithium-ion battery manufacturing is a highly complicated process with
strongly coupled feature interdependencies, a feasible solution that can
analyse feature variables within manufacturing chain and achieve reliable
classification is thus urgently needed. This article proposes a random forest
(RF)-based classification framework, through using the out of bag (OOB)
predictions, Gini changes as well as predictive measure of association (PMOA),
for effectively quantifying the importance and correlations of battery
manufacturing features and their effects on the classification of electrode
properties. Battery manufacturing data containing three intermediate product
features from the mixing stage and one product parameter from the coating stage
are analysed by the designed RF framework to investigate their effects on both
the battery electrode active material mass load and porosity. Illustrative
results demonstrate that the proposed RF framework not only achieves the
reliable classification of electrode properties but also leads to the effective
quantification of both manufacturing feature importance and correlations. This
is the first time to design a systematic RF framework for simultaneously
quantifying battery production feature importance and correlations by three
various quantitative indicators including the unbiased feature importance (FI),
gain improvement FI and PMOA, paving a promising solution to reduce model
dimension and conduct efficient sensitivity analysis of battery manufacturing.
