Physics-guided digital-twin-ready surrogate framework for predictive design of double-bridge compliant mechanisms in advanced manufacturing systems

Abstract

The integration of physics-based modeling and data-driven prediction is creating new opportunities for predictive design, optimization, and the deployment of digital twins in advanced manufacturing systems. In compliant mechanisms, particularly double-bridge configurations used in precision positioning and surface engineering applications, accurate prediction and optimization of amplification ratio remain challenging due to coupled geometric interactions and nonlinear design trade-offs. This study presents a Physics-Guided Digital-Twin-Ready Framework for the predictive design and multi-objective optimization of double-bridge compliant mechanisms. A physics-consistent dataset comprising 8,000 design samples was generated using Latin Hypercube Sampling, analytical compliance modeling, constraint-based filtering, and response-space stratified sampling. The resulting dataset provides balanced coverage of amplification ratios within the range of 5–50, enabling robust learning across diverse design regimes. Machine-learning models, including Random Forest and Extreme Gradient Boosting (XGBoost), were developed to predict amplification ratio from geometric and material parameters. The models achieved excellent predictive performance, with coefficients of determination (R 2 ) exceeding 0.99, mean absolute errors below 0.93, and root mean square errors below 0.65. Uncertainty quantification was incorporated through ensemble variance estimation, yielding prediction intervals with less than 5% relative uncertainty in well-sampled regions. SHAP-based explainability and sensitivity analyses revealed that amplification behavior is primarily governed by geometric parameters, particularly beam lengths and flexure thickness, whereas material stiffness has comparatively lower influence. NSGA-II-based multi-objective optimization identified Pareto-optimal solutions that balance amplification ratio and equivalent stiffness, highlighting the inherent trade-off between displacement amplification and structural rigidity. The developed surrogate models enable rapid design exploration, uncertainty assessment, and optimization, while achieving computational speed-ups of approximately 10 3 –10 7 times compared with finite-element-based evaluation workflows, depending on the evaluation method. The primary contribution of this work is the integration of analytical compliance modeling, physics-consistent dataset generation, uncertainty-aware machine learning, explainable artificial intelligence, and multi-objective optimization within a unified predictive framework. The proposed methodology should be interpreted as a digital-twin-ready surrogate architecture rather than a fully implemented digital twin, as real-time sensing, and online model updating are beyond the scope of the present study. Nevertheless, the framework provides a scalable foundation for future integration with experimental measurements, multi-fidelity datasets, and digital-twin-enabled manufacturing environments.