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The aim of this paper is to present an original and efficient approach for indentifying material parameter in biomechanics. A new method named GAO (Genetic algorithms & Analytical Optimization) addresses the parameter identification problem that is formulated as a non-linear least-squares problem. To evaluate GAO technique, the identification problem of 7 material parameters of a specific biomechanical law is approached by multiple algorithms (genetic algorithms and gradient-based methods). This comparative demonstrates the rapidity and the efficiency of GAO method in parameter estimation. It also explains the behaviour of genetic algorithms, their efficient operators, and the advantages that GAO method brings to genetics algorithms leading to successful parameter identification. [ABSTRACT FROM AUTHOR]

A deeper understanding of biomechanical behaviour of human hands becomes fundamental for any human hand-operated activities. The integration of biomechanical knowledge of human hands into product design process starts to play an increasingly important role in developing an ergonomic product-to-user interface for products and systems requiring high level of comfortable and responsive interactions. Generation of such precise and dynamic models can provide scientific evaluation tools to support product and system development through simulation. This type of support is urgently required in many applications such as hand skill training for surgical operations, ergonomic study of a product or system developed and so forth. The aim of this work is to study the contact behaviour between the operators' hand and a hand-held tool or other similar contacts, by developing a novel and precise nonlinear 3D finite element model of the hand and by investigating the contact behaviour through simulation. The contact behaviour is externalised by solving the problem using the bi-potential method. The human body's biomechanical characteristics, such as hand deformity and structural behaviour, have been fully modelled by implementing anisotropic hyperelastic laws. A case study is given to illustrate the effectiveness of the approach. [ABSTRACT FROM PUBLISHER]

Most of the electromagnetic devices, especially electrical machines, have the disadvantage to be exposed to high vibrations caused by magnetic forces. The aim of this study is to propose a methodology to optimize the cylindrical stators generally used in electrical machines regarding the vibration phenomena. Techniques for vibration reduction require knowledge of the proper frequencies, which depend on mechanical shapes and dimensions as well as material properties such as mass density, Young's modulus and Poisson's ratio. This paper proposes a new approach which is based on the identification of mass density (lamination stacking factor) and Young's modulus in the goal to minimize the vibratory behavior of electrical machines. In this goal, we have used artificial intelligent and finite element method (FEM) analysis to solve the magneto-mechanical inverse problem (IP). In the proposed approach, a Multilayer Perceptron Neural Network (MLPNN) is used as a forward model in order to decrease the FEM time consuming. Thus, a Genetic Algorithm (GA) is used to solve the IP in a reasonable time of running. An example study of an induction machine proves that the developed approach may be applied in both design and identification applications. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous machines (PMSM) have high efficiency and torque density, and have already been employed in hybrid electric vehicles. However, one of their disadvantages is the inherent cogging torque, which is a kind of torque ripple and it would be better to minimize. This torque, sometimes, can be an important source of noise and vibrations. In this paper, the effect of the geometric characteristics of the stator on the vibratory behavior of electrical machines is illustrated. The optimum geometry for obtaining a minimum vibration level has been reached. For this purpose, an approach by using the Artificial Intelligent (AI) and the Finite Element Method (FEM) is proposed to solve the magneto-mechanical problem of geometrical parameters identification in the optimization process. The obtained results by Genetic Algorithm (GA) method and those obtained with Gauss method of second order (BFGS) have been compared. [ABSTRACT FROM AUTHOR]

The integration of ergonomics factors into product design process has turned out to be fundamental nowadays, in order to provide information about use, comfort and security requirements. Human doings undergo simulation thanks to our MANERCOS human digital model. However, it fails to show us overall biomechanics implications because its simulation functions are unable to assess biomechanical parameters as stress and strain levels. Finite element modeling of a human body with its organs, bones, muscles, ligaments and tendons answers to this issue. Our objective is to develop and integrate such a modeling approach into MANERCOS ergonomics software by using the FER finite element code. We have carried out a geometric model and a mesh network device thanks to MANERCOS geometric data. The human body characteristics, considering a mechanical point of view, have been modeled by implementing anisotropic hyperelastic behaviour laws. [ABSTRACT FROM AUTHOR]

The article discusses the generalized form of general algorithm optimisation (GAO) parameter identification method in biomechanics. It presents the solution to a nonlinear least squares problem using the GAO method. It demonstrates the method's efficiency by describing the nonlinear elasticity of the human intervertebral disc relative to the seven material parameters of the law. It concludes that GAO has a good gain of central processing unit (CPU) time compared to genetic algorithm (GA).