Interface interaction mechanics analysis is of great significance for robot-assisted insertion surgery in minimally invasive surgery and therapy. Previous work indicates that the accurate modeling of soft tissue puncture forces plays a crucial role in surgical planning, robotic needle insertion, and biomechanical simulation, which can give insights useful for physicians to guide and operate assisted robots. The objective of this study is to develop a dynamic multi-component force model that integrates cutting force, stiffness resistance, and frictional interaction to characterize needle–soft tissue interaction during puncture. A dynamic force model is proposed, and a lateral periodic disturbance mechanism is introduced into the simulation framework in order to enhance the robustness and realism of the model under micro-manipulation scenarios. The model has been validated using a series of controlled puncture experiments on porcine liver and renal tissues under varying insertion angles (15°, 30°, 45°) and speeds (0.5 mm/s, 1.5 mm/s, 2.5 mm/s). Corresponding finite element simulations were also conducted using ANSYS software. The agreement between simulation and experiment has been quantitatively evaluated by comparing force–depth and force–time curves, and the statistical significance of the impact of angle and speed on puncture forces has been assessed using ANOVA and Tukey’s HSD tests. Quantitative comparison demonstrated strong consistency, with the optimal case reaching a coefficient of determination (R2) value of 0.96 and Root Mean Square Error (RMSE) below 0.13 N after incorporating a 0.05 mm lateral perturbation. Statistical analysis confirmed the impact of angle and speed on puncture force responses (p < 0.05). Furthermore, comparative analysis revealed that porcine liver exhibits more consistent biomechanical behavior than renal tissue, particularly under perturbation-enhanced simulation. This study successfully establishes a dynamic multi-component force model for soft tissue puncture, validated with high fidelity against experimental data. The incorporated lateral disturbance mechanism enhanced the model’s realism. This work can provide a reliable foundation for the future design of intelligent robot-assisted puncture systems and high-fidelity simulation-based training platforms.
Wang et al. (Wed,) studied this question.