Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher scales. The neural network outputs (elastic properties of the microfibril) are used as inputs for the homogenisation computation to determine the properties of mineralised collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modelling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modelling. Good agreement was obtained between our predicted results and literature data.Comment: 2

Micromechanics as a testbed for artificial intelligence methods evaluation

Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites

The macroscopic response of short fiber reinforced composites (SFRCs) is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path-dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of SFRCs, given the microstructural parameters and the strain path. Micromechanical mean-field simulations are conducted to create a database for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations

Development and analysis of computer vision system for micromechanics

Summary: In micromechanics the best technologies are MicroElectroMechanical Systems (MEMS) and MicroEquipment Technology (MET). The MEMS used the electronic technology to produce mechanical components. Due to the advantages of the MET such as the development of low-cost micro devices, the possibility of using various manufacturing materials, the possibility of producing three-dimensional microcomponents it will be very useful to automatize all processes of mechanics production and develop different technological innovations. The automation and robotics are two closely related technologies since automation can be defined as a technology that is related to the use of mechanical-electrical systems based on computers for the operation and control of production. The field of micromechanics has been involved in different applications that cover almost all areas of science and technology, an example of this is the management of microdevices for the autofocus of digital cameras whose objective is image processing (recognizing and locate objects). The use of computer vision systems can help to automate the work of MEMS and MET systems, so the study of image processing using a computer is very important. The objective was to design a computer vision system that allows the movement of the lens to focus the work area, for the monitoring of the micromachine tool in manufacturing processes and assembly of microcomponents in real time using previously developed image recognition algorithms. The developed algorithms use the criterion of improving the contrast of the input image. We describe our approach and obtained results. This approach can be used not only in micromechanics but in nanomechanics to

Geometric Morphology of Granular Materials

We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to extract dilated contours. A constrained Delaunay tesselation of the contour points results in a triangular mesh. This mesh is the basic ingredient of the Chodal Axis Transform, which provides a morphological decomposition of shapes. Such decomposition allows for grain separation and the efficient computation of the statistical features of granular materials.Comment: 6 pages, 9 figures. For more information visit http://www.nis.lanl.gov/~bschlei/labvis/index.htm