
Modeling forces applied to scissors during cutting of biological materials is useful for surgical simulation. Previous approaches to haptic display of scissor cutting are based on recording and replaying measured data. This paper presents an analytical model based on the concepts of contact mechanics and fracture mechanics to calculate forces applied to scissors during cutting of a slab of material. The model considers the process of cutting as a sequence of deformation and fracture phases. During deformation phases, forces applied to the scissors are calculated from a torque-angle response model synthesized from measurement data multiplied by a ratio that depends on the position of the cutting crack edge and the curve of the blades. Using the principle of conservation of energy, the forces of fracture are related to the fracture toughness of the material and the geometry of the blades of the scissors. The forces applied to scissors generally include high-frequency fluctuations. We show that the analytical model accurately predicts the average applied force. The cutting model is computationally efficient, so it can be used for real-time computations such as haptic rendering. Experimental results from cutting samples of paper, plastic, cloth, and chicken skin confirm the model, and the model is rendered in a haptic virtual environment.
Equipment Design, Surgical Instruments, Models, Biological, Elasticity, Equipment Failure Analysis, Surgery, Computer-Assisted, Hardness, Touch, Computer-Aided Design, Humans, Computer Simulation, Stress, Mechanical
Equipment Design, Surgical Instruments, Models, Biological, Elasticity, Equipment Failure Analysis, Surgery, Computer-Assisted, Hardness, Touch, Computer-Aided Design, Humans, Computer Simulation, Stress, Mechanical
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 48 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
