摘要：A methodology is implemented to find the optimum reduced weight configuration design of an operating structure of a civil aircraft vertical tail fin. FE （finite element） based topology optimization is executed to find the optimum material distribution of initial design space of rib by maximizing the stiffness. Loads pertinent to the operating and ground conditions are estimated and applied, considering the orientation of structural assembly members and built-in supports offered in the main structure. Manufacturing constraints are incorporated into the optimization loop to generate pragmatic and manufacturable design configuration. The topologically optimized configuration is then converted to CAD viable design through data reduction and smoothing by converting to ISO-surfaces. A methodology is also devised to modify the topological optimized voids and material layout precisely with splines and fillets to construct manufacturable features and avoid stress concentrations. The resulting novel design proposal is then analyzed and validated against stipulated loads, constraints and other design needs. Results validate the new design proposal as more reliable, having reduced weight and enhanced structural performance.

摘要：According to the structural characteristics of the automobile engine bracket, the finite element model of the bracket is established. As the connecting part between the engine and the body, the performance requirements of the automobile engine bracket affect the comfort and the safety of the vehicle directly. Using the RADIOSS solver, the dangerous point of the bracket is analyzed. Under the premise of ensuring its reliability, with the help of OptiStruct software to carry out the topology optimization design, to get the optimal material distribution of the bracket and the final design will meet the performance requirements.

摘要：拓扑优化由于其严格的数学理论,成为连续结构设计中决定材料分布的重要方法.然而,随着材料类型在设计域内的增加,传统拓扑优化方法的计算效率大大降低.本文提出了一种基于深度学习的拓扑优化方法,用于提高多材料结构拓扑优化设计的计算效率.首先,利用固体各向同性材料惩罚(SIMP)方法对大量多材料结构进行优化设计,构建了多材料拓扑优化数据集.其次,ResUNet被引入生成对抗式网络形成ResUNet-GAN,构建了设计参数和优化构型之间的高维映射.最后,利用预训练的ResUNet-GAN实现多材料拓扑优化设计.数值模拟验证了ResUNet-GAN成功地应用于双材料的悬臂梁、三材料悬臂梁、三材料简支梁结构的拓扑优化设计.研究表明,基于深度学习拓扑优化方法的计算效率优于传统方法,为高效拓扑优化设计的发展奠定了理论基础.

摘要：Backgrou nd Dense titanium(Ti)fusion cages have been commonly used in transforaminal lumbar interbody ***,the stiffness mismatch between cages and adjacent bone endplates increases the risk of stress shielding and cage *** The current study presents a multiscale optimization approach for porous Ti fusion cage development,including microscale topology optimization based on homogenization theory that obtains a unit cell with prescribed mechanical properties,and macroscale topology optimization that determines the layout of framework structure over the porous cage while maintaining the desired *** biomechanical performance of the designed porous cage is assessed using numerical simulations of fusion *** laser melting is employed to assists with fabricating the designed porous structure and porous *** The simulations demonstrate that the designed porous cage increases the strain energy density of bone grafts and decreases the peak stress on bone *** mechanical and morphological discrepancies between the as-designed and fabricated porous structures are also *** From the perspective of biomechanics,it is demonstrated that the designed porous cage contributes to reducing the risk of stress shielding and cage *** optimization of processing parameters and post-treatments are required to fabricate the designed porous *** present multiscale optimization approach can be extended to the development of cages with other shapes or materials and further types of orthopedic implants.