Haptics enabled offline AFM image analysis

Current advancements in nanotechnology are dependent on the capabilities that can enable nano-scientists to extend their eyes and hands into the nano-world. For this purpose, a haptics (devices capable of recreating tactile or force sensations) based system for AFM (Atomic Force Microscope) is proposed. The system enables the nano-scientists to touch and feel the sample surfaces, viewed through AFM, in order to provide them with better understanding of the physical properties of the surface, such as roughness, stiffness and shape of molecular architecture. At this stage, the proposed work uses of ine images produced using AFM and perform image analysis to create virtual surfaces suitable for haptics force analysis. The research work is in the process of extension from of ine to online process where interaction will be done directly on the material surface for realistic analysis.

Artificial neural network analysis of twin tunnelling-induced ground settlements

In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.

Modelling of the stiffness evolution of truss core structures damaged by plastic buckling

A finite element study based on 1D beam element model is performed in order to investigate the mechanical behavior of an elasto-plastic beam loaded in axial compression over its buckling limit. The mode of loading is related to the damage of truss-cored beams in truss-cored laminates. The analysis takes into account the effects of geometry and material properties. The results of the FEM analysis are used for developing a simple mechanical model based on the basic Euler-Bernoulli beam theory and accounts for the beam compressibility. The model uses phenomenological functions containing parameters related to the basic material and geometrical properties. The presented model is developed in the form of closed solution which does not require complex numerical methods or extensive parametric studies. Predictions of the compressive stiffness degradation of truss-cored composites are made with the proposed model and compared with the results of FEM simulations. The error of the stiffness prediction with respect to the FEM results is within 10% over a 5 fold range of stiffness.

Workspace and sensitivity analysis of a novel nonredundant parallel SCARA robot featuring infinite tool rotation

As demonstrated by the exceptionally successful Delta robot, parallel kinematics Schönflies motion generators (PKSMG) exhibit several advantages over their serial counterparts. Despite its success, the Delta robot suffers from several shortcomings, including a bulky framework and a small workspace-to-footprint ratio. Another drawback is that the kinematic chain generating tool rotation suffers from low torsional stiffness. This letter presents a novel architecture for a nonredundant PKSMG providing infinite tool rotation and an extensive positioning workspace. The workspace and kinematic performance of the proposed architecture are analysed in detail.