CorrosionOptiMap links additive manufacturing process parameters to corrosion of stainless steel

Rapid implementation of Additive Manufacturing (AM) for corrosion-resistant components demands understanding complex process-structure-property relationships without costly characterization. Here we introduce CorrosionOptiMap, a physics-informed process-to-property framework that maps laser powder bed fusion (LPBF) parameters directly to corrosion potential (E_corr), corrosion rate (CR), and pitting potential (E_pitting). Engineered physics descriptors feed Pareto-selected stacking ensembles with an ElasticNet meta-learner, trained on potentiodynamic data from 77 SS316L specimens, the largest experimental LPBF corrosion dataset. CorrosionOptiMap attains R²>0.89 across targets and cuts MAE/RMSE by 50–60% versus raw-parameter models, while SHAP reveals a hierarchy: surface morphology governs CR, porosity and spatters control E_pitting, and microstructural homogeneity drives E_corr. High-throughput “corrosivity maps” evaluate 6,200 parameter combinations and enable multi-objective Pareto optimization, reducing experimental burden 8̃0 × and guiding practical trade-offs. SEM on 35 prints validates surface area driven kinetics for CR. Embedding physics descriptors into ensemble learning yields explainable, scalable predictions that accelerate optimization of corrosion-resistant AM components.