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In this study, the bone screwing process carried out with M3.5 cortex screw for stabilization after reduction of femur shaft fracture was investigated both experimentally and numerically. The numerical analyses were performed based on the finite element method using Deform-3D software. The friction, material model, the loading and boundary conditions were exactly identified for finite element analyses. An analytic model and software were developed, which calculate the process parameters such as screwing power and thrust power, heat transfer coefficients in order to determine the temperature distributions occurring in the screw and bone material (sawbones) during screwing process. As a result, the screwing moment and thrust force values decrease with increase of spindle speed. On the contrary, temperature values of screw and sawbones increase with increase of spindle speed. A good consistency between the results obtained from both experimental and numerical simulations was found during the bone screwing process.

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Metal drilling and cutting processes are used commonly in many areas of the industry. When these processes investigated analytically, it is seen that they have a very complicated structure. This complicated structure can find easy solutions with finite element-based simulation tools. In metal removal operations, it is very difficult to calculate the cutting parameters such as cutting tool stresses, residual stresses, cutting tool-chip interface temperature and shear angle both experimentally and analytically. But, these operations could be easily calculated by computer-supported simulation tools. In these types of numeric simulations, using finite element method, it is needed to identify clearly the friction, material model load and limiting conditions. In this paper, the cutting parameters as cutting and thrust power, heat transfer coefficient and friction coefficient at chip-tool interface which is needed to find the temperature distribution on drilling bit and workpiece material were calculated using analytic models.