Physics-Based Optimization of Tuned Mass Dampers for Chatter Suppression in Machining Operations

Tuned Mass Dampers (TMDs) are widely used to suppress vibrations in machining operations, yet their design remains confined to analytical tuning rules targeting either resonance attenuation or chatter stability. This paper presents a physics-based evolutionary optimization framework for TMD design that directly optimizes the frequency response function (FRF) characteristics governing regenerative chatter. Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to optimize the TMD natural frequency and damping ratio, enabling a systematic search beyond classical Den Hartog and Sims tuning methods. Simulation results for a TMD-equipped boring bar demonstrate that GA-optimized designs increase the critical depth of cut by 91% over Sims tuning (6.7 mm to 12.8 mm), double the settling time improvement of Den Hartog (60.8% vs. 30.4%), and reduce vibration amplitude to 0.5% of the undamped level—13 times lower than Den Hartog. The proposed framework provides a flexible, process-independent approach for enhancing machining stability, validated through PSO convergence.