Niloy Khutia

Understanding compressive deformation behavior of porous Ti using finite element analysis

In the present study, porous commercially pure (CP) Ti samples with different volume fraction of porosities were fabricated using a commercial additive manufacturing technique namely laser engineered net shaping (LENS™). Mechanical behavior of solid and porous samples was evaluated at room temperature under quasi-static compressive loading. Fracture surfaces of the failed samples were analyzed to determine the failure modes. Finite Element (FE) analysis using representative volume element (RVE) model and micro-computed tomography (CT) based model have been performed to understand the deformation behavior of laser deposited solid and porous CP-Ti samples. In vitro cell culture on laser processed porous CP-Ti surfaces showed normal cell proliferation with time, and confirmed non-toxic nature of these samples.

Material parameter optimisation of Ohno-Wang kinematic hardening model using multi objective genetic algorithm

Ohno-Wang hardening model is an advanced constitutive model to evaluate the cyclic plasticity behaviour of material. This model has capability to simulate uniaxial and biaxial ratcheting response of the material. But, it is required to determine large number of material parameters from several experimental responses in order to simulate this phenomenon. Material parameters for constitutive models are generally determined manually through trial and error approach which is tedious and less accurate. Due to arbitrariness and complexity of cyclic loading, advanced constitutive material models become non-linear and multimodal in functional and parameter space. To overcome this problem, an automated parameter optimisation approach using genetic algorithm has been proposed in the present work to identify Ohno-Wang material parameters of 304LN, stainless steel for uniaxial simulation. Optimisation by this approach has improved the model prediction in uniaxial low cycle and ratcheting fatigue simulations after comparison with the experimental response.

Pore Geometry Optimization of Titanium (Ti6Al4V) Alloy, for Its Application in the Fabrication of Customized Hip Implants

The present study investigates the mechanical response of representative volume elements of porous Ti-6Al-4V alloy, to arrive at a desired range of pore geometries that would optimize the reduction in stiffness necessary for biocompatibility with the stress concentration arising around the pore periphery, under physiological loading conditions with respect to orthopedic hip implants. A comparative study of the two is performed with the aid of a newly defined optimizing parameter called pore efficiency that takes into consideration both the stiffness quantity and the stress localization around pores. To perform a detailed analysis of the response of the porous structure over the entire spectrum of loading conditions that a hip implant is subjected to in vivo, the mechanical responses of 3D finite element models of cubic and rectangular parallelepiped geometries, with porosities varying over a range of 10% to 60%, are simulated under representative compressive, flexural as well as combined loading conditions. The results that are obtained are used to suggest a range of pore diameters that lower the effective stiffness and modulus of the implant to around 60% of the stiffness and modulus of dense solid implants while keeping the stress levels within permissible limits.

Design of patient specific dental implant using FE analysis and computational intelligence techniques

Display Omitted Genetic algorithm is successfully used for designing dental implant to achieve the desired microstrain and implant stress.Hybridization of desirability function with the ANN converted the FEA findings to make the objective.It is seen that the optimum value of microstrain differed from the desirable value with improved bone quality.The optimum solutions lead to a guideline for developing patient specific implant development.The FEA based validation of the optimum solutions shows variation of the result well within accepted limit. Genetic algorithm is employed for optimum designing of patient specific dental implants with varying dimension and porosity. It is generally recommended that, the micro strain at the bone implant interface should be around 15003000. The porous dental implant needs to be designed in such a way that the micro stain remains within the above range, and a value close to 2500 micro strain is most desired. In this design problem, the most important constraint is that the implant stress should be limited within 350MPa as titanium alloy was considered as implant material. The above attributes are to be achieved per the varying bone conditions of the patients to design a patient specific prosthesis. This design problem is expressed as an optimization problem using the desirability function, where the data generated by finite element analysis is converted to an artificial neural network model. The output of the neural network model is converted within a range of 01 using desirability function, where the maximum value is reached at the most desired micro strain of 2500. This hybrid model of neural network and desirability function is used as the objective function for the optimization problem using genetic algorithm. Another neural network model describing the implant stress is used as the constraint. The optimum solutions achieved from ANN and GA are validated again through finite element method. Without doing stress analysis by FEM, the ANN models are used for measuring the fitness of the members of the population during optimization. This would predict the optimum dimension of dental implant made of Titanium alloy with most favorable porosity percentage for better ossiointegration for a patient per bone condition.