This study presents a supervised learning framework for predicting the maximum membrane displacement of a shape memory alloy (SMA) antagonistic micro pump subjected to material and geometric uncertainties. SMA micro pumps are indeed promising candidates for precise fluid control in microfluidics, but their nonlinear and hysteretic thermomechanical behavior makes parametric analyses and uncertainty evaluation using finite element analysis (FEA) methods particularly computationally expensive. To overcome this difficulty, a high-fidelity dataset comprising 500 design points was generated by three-dimensional simulation using a uniform randomized experimental design. Eight input parameters were investigated: five properties of the NiTi material (Young’s modulus E, Poisson’s ratio \(\nu \) , hardening parameter h, yield strength R, and martensite end temperature Mf) and three geometric dimensions (thickness \(e_{mm}\) , membrane radius \(R_{mm}\) and spacer length \(L_{mm}\) ). After preprocessing, five regression algorithms XGBoost, Gradient Boosting, LightGBM, Random Forest and Support Vector Regression were trained and optimized using RandomizedSearchCV and 5-fold cross-validation. Among these, XGBoost offered the best predictive performance, with \(R^2 = 0.9547\) , \(RMSE = 0.0444\) , \(MSE = 0.0020\) , and \(MAE = 0.0323\) across the entire test set. SHAP analysis showed that membrane thickness and radius are the most influential factors corroborating the physical consistency of the learned correlations. Learning curves and residual diagnostics confirmed the robustness of the XGBoost model. The proposed surrogate model significantly reduces computation time relative to direct finite element methods based uncertainty quantification methods or reliability based design optimization while maintaining high predictive accuracy. These results demonstrate that machine learning based surrogate modeling constitutes an efficient and reliable complement to FEA for the design of SMA micro pumps in advanced microsystems.
