Effect of Zr content on phase stability, deformation behavior, and Young's modulus in Ti-Nb-Zr alloys

Ti alloys have attracted continuing research attention as promising biomaterials due to their superior corrosion resistance and biocompatibility and excellent mechanical properties. Metastable beta-type Ti alloys also provide several unique properties such as low Young's modulus, shape memory effect, and superelasticity. Such unique properties are predominantly attributed to the phase stability and reversible martensitic transformation. In this study, the effects of the Nb and Zr contents on phase constitution, transformation temperature, deformation behavior, and Young's modulus were investigated. Ti-Nb and Ti-Nb-Zr alloys over a wide composition range, i.e., Ti-(18-40)Nb, Ti-(15-40)Nb-4Zr, Ti-(16-40)Nb-8Zr, Ti-(15-40)Nb-12Zr, Ti-(12-17)Nb-18Zr, were fabricated and their properties were characterized. The phase boundary between the beta phase and the alpha '' martensite phase was clarified. The lower limit content of Nb to suppress the martensitic transformation and to obtain a single beta phase at room temperature decreased with increasing Zr content. The Ti-25Nb, Ti-22Nb-4Zr, Ti-19Nb-8Zr, Ti-17Nb-12Zr and Ti-14Nb-18Zr alloys exhibit the lowest Young's modulus among Ti-Nb-Zr alloys with Zr content of 0, 4, 8, 12, and 18 at.%, respectively. Particularly, the Ti-14Nb-18Zr alloy exhibits a very low Young's modulus less than 40 GPa. Correlation among alloy composition, phase stability, and Young's modulus was discussed.Web of Science132art. no. 47

Simulation of hyperelastic materials in real-time using Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

Multiple Shape Registration using Constrained Optimal Control

Lagrangian particle formulations of the large deformation diffeomorphic metric mapping algorithm (LDDMM) only allow for the study of a single shape. In this paper, we introduce and discuss both a theoretical and practical setting for the simultaneous study of multiple shapes that are either stitched to one another or slide along a submanifold. The method is described within the optimal control formalism, and optimality conditions are given, together with the equations that are needed to implement augmented Lagrangian methods. Experimental results are provided for stitched and sliding surfaces

Some New Concepts of Shape Memory Effect of Polymers

In this study some new concepts regarding certain aspects related to shape memory polymers are presented. A blend of polylactic acid (PLA) (80%) and polybutylene succinate (PBS) (20%) was prepared first by extrusion, then by injection molding to obtain the samples. Tensile, stress-relaxation and recovery tests were performed on these samples at 70 °C. The results indicated that the blend can only regain 24% of its initial shape. It was shown that, this partial shape memory effect could be improved by successive cycles of shape memory tests. After a fourth cycle, the blend is able to regain 82% of its shape. These original results indicated that a polymer without (or with partial) shape memory effect may be transformed into a shape memory polymer without any chemical modification. In this work, we have also shown the relationship between shape memory and property memory effect. Mono and multi-frequency DMA (dynamic mechanical analyzer) tests on virgin and 100% recovered samples of polyurethane (PU) revealed that the polymer at the end of the shape memory tests regains 100% of its initial form without regaining some of its physical properties like glass transition temperature, tensile modulus, heat expansion coefficient and free volume fraction. Shape memory (with and without stress-relaxation) tests were performed on the samples in order to show the role of residual stresses during recovery tests. On the basis of the results we have tried to show the origin of the driving force responsible for shape memory effect