Modelling the mechanical behaviour of biological tissues is of vital importance for clinical applications. It is necessary for surgery simulation, tissue engineering, finite element modelling of soft tissues, etc. The theory of linear elasticity is frequently used to characterise biological tissues; however, the theory of nonlinear elasticity using hyperelastic models, describes accurately the nonlinear tissue response under large strains. The aim of this study is to provide a review of constitutive equations based on the continuum mechanics approach for modelling the rate-independent mechanical behaviour of homogeneous, isotropic and incompressible biological materials. The hyperelastic approach postulates an existence of the strain energy function--a scalar function per unit reference volume, which relates the displacement of the tissue to their corresponding stress values. The most popular form of the strain energy functions as Neo-Hookean, Mooney-Rivlin, Ogden, Yeoh, Fung-Demiray, Veronda-Westmann, Arruda-Boyce, Gent and their modifications are described and discussed considering their ability to analytically characterise the mechanical behaviour of biological tissues. The review provides a complete and detailed analysis of the strain energy functions used for modelling the rate-independent mechanical behaviour of soft biological tissues such as liver, kidney, spleen, brain, breast, etc.

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Tumor ablation techniques such as heating or freezing have a significant influence on the histology of liver tissue. However, only for temperatures above body temperature an influence on the mechanical properties of hepatic tissues was noticeable. Freezing up to -20 °C did not affect the liver mechanics.

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Because of increased productivity demands in manufacturing the attention in burr problems is steadily turned to the avoidance of burr. With this initial point this paper contributes to the analysis and simulation of burr separation at workpiece material AISI 1045 steel (C45E). At first 12 known crack initiation criteria are investigated whether they are suitable to simulate crack initiation during burr formation. It is shown that eight criteria are suitable for the prediction of the crack, whereas the criterion from Cockcroft and Latham has to be pointed out. The implementation of the failure conception of ductile damage from Lemaitre and Sievert indicates the applicability for burr formation simulation with simultaneous simulation of segmented chips. In first calculation, a sliding of the chip could be simulated. At the end the hypothesis of the 'hydrostatic bowl', an area of highly negative pressure in the stage of burr formation, is used to predict the location of cr ack initiation. The established values correlate better to the experimental tests than these, which are determined by means of fracture criteria

Techniques for intraoperative radiation therapy (IORT), the applications of tumor bed radiation immediately after surgery or utilising intracavitary access, have evolved in recent years. They are designed to substitute or complement conventional external beam radiation therapy in selected patients. IORT has become an excellent treatment option because of good long-term therapy outcomes. The combination of IORT with external beam radiation therapy has the potential to improve local control. The purpose of this paper is to present IORT techniques using gamma and electronic sources, as well as more conventional nuclide-based approaches and to evaluate their effectiveness. Common techniques for radiation of tumor cavities are listed and compared. Radionuclide IORT methods are represented by balloon and hybrid multi-catheter devices in combination with appropriate afterloaders. Electron beam therapy dedicated for use as intraoperative radiation system is reviewed and miniature x-ray sources in electronic radiation therapy are presented. These systems could further simplify IORT, because they are easy to use and require no shielding due to their relatively low photon energies. In combination with additional imaging techniques (MRI, US, CT and NucMed) the application of these miniature x-ray sources or catheter-based nuclide therapies could be the future of IORT.

Machine learning, big data and deep learning are today’s catchphrases for how to improve reliability and productivity of your manufacturing equipment. Production companies implement a large number of sensors to record every activity within their production lines and learn as much as possible about their running processes in order to predict shifting product properties and to prevent stoppage due to failure. The successful application of machine learning algorithms to predict machine and process behavior depends on a reliable and balanced database. Since the foremost goal of every manufacturing business is to make sound parts and to avoid defects, there is a large amount of data available for smoothly running processes but only very little for failure production. One approach to solve this imbalance would be to link the production line data with simulation data. Simulation models allow for computing failure parts with no additional costs and therefore enable the exploration of the entire parameter space. We conducted press-hardening experiments with a variation of process parameters for a structural car body part on the press hardening line at Fraunhofer IWU. As an evaluation criterion, we measured the hardness of the final part at critical spots. In order to expand the experimental data, we applied FE simulations to the entire press hardening process chain. The paper explains limitations of the model and elaborates on its parameterization. As a final task, we applied a basic machine-learning algorithm to both experimental and numerical data as well as to their combination in order to evaluate the data space expansion through simulations. The results obtained through machine learning indicate significant differences in the prediction of part quality for solely experimental data and its combination with simulation data. This is especially true for press hardening because of non-linear system behavior and a large amount of uncertain and hard-to-identify parameters. We found that the most challenging parts of uniting measured and simulated data is not only to create simulations with appropriate accuracy, which allow for a meaningful extrapolation of the parameter space, but also to compare simulation and production data based on the same criterion and to have stable simulation models for the entire parameter range.