The continuous implementation of shape memory alloys’ (SMAs’) actuation capabilities in various applications from aerospace to biomedical tools has attracted researchers’ interests into design optimization of active systems. Traditional methods of optimization have mostly relied on several iterations of altering and testing different possible design of prototypes seeking the best configuration. This trial and error experimentation method is usually expensive and time consuming. In the recent years the availability of computational analysis has facilitated the optimization process by avoiding the developments of many prototypes in the whole design space. In this work an automated design optimization frameworks is presented especially for the systems including active components. Design exploration of a recently proposed medical device was considered as a case study to elaborate this iterative technique. SMA activated needle is an innovative medical tool to be used in needle-based surgeries aiming the enhancement of the needle tip placement inside the tissue. Different configurations have been assessed by altering the design variables in the assigned domain seeking the maximum needle tip deflection to assure the maximum flexibility of the structure where all the analyses were constrained to the stress level of SMAs to be in the safe range preventing plasticity. A commercially available finite element package was used for the iterative assessments in the optimization approach. The challenging part in any analysis of active components is the incorporation of a suitable material model. For this purpose three experimental setups were developed to get the material properties of SMAs through different responses of the wires. These material properties along with the implementation of Brinson model led to the generation of the isothermal stress strain curves which were defined as material model of the active components in the FE analyses. The FE model was then linked to the iterative engine of direct optimization to iterate through the whole domain and determine the best configuration. The Design of Experiments (DOE) and the Multi-Objective Genetic Algorithm (MOGA) were used for the case study optimization. Both the design optimization and the design sensitivity studies were described. The results showed the length of the needle and the offset between the neutral axis of needle and the actuator were the most sensitive variables. The best five configurations with the maximum tip deflection was also presented.

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