Integrated Modeling and Data-Driven Analysis of Bread Machine Electromechanical System with Hydration-Dependent Viscoelastic Load

Electromechanical systems operating under viscoelastic loads require precise modeling due to the highly nonlinear behavior of the load. An automatic bread machine is a practical example where dough represents a dynamic viscoelastic load sensitive to hydration. As found in this paper, increasing the water content leads to a decrease in the torque and the required mechanical power. An integrated approach combining MATLAB/Simulink and Simscape modeling, experimental measurements, and a PCA-based regression model is presented. The tests were conducted with three types of flour (type 500, type 1850, and rye–wheat) at hydrations of 52%, 58%, and 63% with over 6000 measurements recorded for each combination. The regression models achieve moderate predictability (R2 = 0.64–0.96) model performance that varies across flour types. Increasing the dough hydration from 52% to 63% reduces the torque by approximately 22–46% across the tested flour types, while the angular velocity rises slightly (from about 147.9 to 151.9 rad/s). A descriptive decrease in energy consumption of up to around 6% was observed within the sampled batches with the system efficiency remaining within a narrow range around η ≈ 0.67. Within the studied levels (52–63%), the minimum load was observed at 58%. The proposed integrated model reliably describes the interaction between the electric motor, the mechanical gear, and the viscoelastic load, and it offers a basis for energy optimization and the implementation of low-cost sensor systems for intelligent control in the bread-making process.