Influence of cavity type and size of composite restorations on cuspal flexure

Objectives: The present study examines the influence of cavity type, and size of composite restorations on cuspal flexure due to polymerization shrinkage. Methods: Thirty healthy premolars were selected and divided into two experimental groups. Group 1: Mesial, mesiodistal, and MOD cavities were successively prepared in each tooth by means of the SONICSYS approx system (KaVo ®) using a nº 2 (small) diamond tip. Group 2: The same cavity preparation sequence was followed but a nº 3 (medium) tip was used. Cavity preparations were performed and afterwards restored using the same adhesive system and resin composite. Cuspal displacement was measured 15 min after completion of each type of restoration. Results: Under the experimental conditions used, polymerization shrinkage of composite restorations resulted in an inward deflection of the cusps in all the situations evaluated. The Friedman global test found significant differences according to the cavity type and size (p0.05). Conclusions: The present study demonstrates that significantly higher cuspal deflection is observed in MOD restorations, showing that the degree of dental removal influences the cuspal flexure from polymerization shrinkage of composite restorations

Auxetic orthotropic materials: Numerical determination of a phenomenological spline-based stored density energy and its implementation for finite element analysis

Abstract Auxetic materials, which have negative Poisson’s ratio, show potential to be used in many interesting applications. Finite element analysis (FEA) is an important phase in implementing auxetic materials, but may become computationally expensive because simulation often needs microscale details and a fine mesh. It is also necessary to check that topological aspects of the microscale reflects not only micro but macromechanical behavior. This work presents a phenomenological approach to the problem using data-driven spline-based techniques to properly characterize orthotropic auxetic material requiring neither analytical constraints nor micromechanics, expanding on previous methods for isotropic materials. Hyperelastic energies of auxetic orthotropic material are determined from experimental data by solving the equilibrium differential functional equations directly, so no fitting or analytical estimation is necessary. This offers two advantages; (i) it allows the FEA study of orthotropic auxetic materials without requiring micromechanics considerations, reducing modeling and computational time costs by two to three orders of magnitude; (ii) it adapts the hyperelastic energies to the nature of the material with precision, which could be critical in scenarios where accuracy is essential (e.g. robotic surgery)

A new reliability-based data-driven approach for noisy experimental data with physical constraints

Data Science has burst into simulation-based engineering sciences with an impressive impulse. However, data are never uncertainty-free and a suitable approach is needed to face data measurement errors and their intrinsic randomness in problems with well-established physical constraints. As in previous works, this problem is here faced by hybridizing a standard mathematical modeling approach with a new data-driven solver accounting for the phenomenological part of the problem, with the aim of finding a solution point, satisfying some constraints, that minimizes a distance to a given data-set. However, unlike such works that are established in a deterministic framework, we use the Mahalanobis distance in order to incorporate statistical second order uncertainty of data in computations, i.e. variance and correlation. We develop the underlying stochastic theoretical framework and establish the fundamental mathematical and statistical properties. The performance of the resulting reliability-based data-driven procedure is evaluated in a simple but illustrative unidimensional problem as well as in a more realistic solution of a 3D structural problem with a material with intrinsically random constitutive behavior as concrete. The results show, in comparison with other data-driven solvers, better convergence, higher accuracy, clearer interpretation, and major flexibility besides the relevance of allowing uncertainty management with low computational demand