Robust Optimization of an Electromechanical Linear Actuator Under Experimental-Budget Constraints
The design optimization of electromechanical linear actuators (EMLAs) is challenged by coupled tribological, thermal, and dynamic effects. Such interactions can violate the additivity assumptions of compact orthogonal designs. Factor ranking may become unreliable when residual variance includes non-additive contributions rather than stochastic noise alone. This study presents an Extended Orthogonal Experimental Matrix (E-OEM) framework for robust factor screening under experimental-budget constraints. It preserves the reduced effort of an orthogonal design while using a latent allocation term to retain structured non-additive variation that would otherwise be treated as residual error. It combines compact experimentation, fitted probability distributions, Monte Carlo simulation, and weighted multi-criteria ranking under push force, robustness, and cost targets. The framework is demonstrated on 90 automotive EMLAs considering lubricant type, drive frequency, and operating temperature. E-OEM was benchmarked against the full-factorial experiment (27 combinations, 810 push-force measurements). Under the selected weighting strategy, both E-OEM and the benchmark identified the same nominal optimum (590 Hz, +25 °C, Addinol lubricant), with a measured push force of 77.32 ± 2.05 N (1.01% deviation from the prediction). The results show that E-OEM provides an efficient screening and decision-support approach for actuator design when full-factorial testing is constrained by time and cost.