Jiao Jia^1,*, Xin He², Zhenchen Liu³, Shiqing Wu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3215-3231, 2025, DOI:10.32604/cmes.2025.066489 - 30 June 2025

Abstract As primary load-bearing components extensively utilized in engineering applications, beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying diverse mechanical performance requirements. Combining topology optimization with fully coupled homogenization beam theory, we provide a highly efficient design tool to access desirable periodic microstructures for beams. The present optimization framework comprehensively takes into account for key deformation modes, including tension, bending, torsion, and shear deformation, all within a unified formulation. Several numerical results prove that our method can be used to handle kinds of microstructure design for beam-like structures, e.g., More >

Hsien-Tsung Lu^1,2, Ching-Chi Hsu^3,*, Qi-Quan Jian³, Wei-Ting Chen⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1883-1898, 2025, DOI:10.32604/cmes.2025.057675 - 27 January 2025

Abstract Reconstruction of a traumatic distal femur defect remains a therapeutic challenge. Bone defect implants have been proposed to substitute the bone defect, and their biomechanical performances can be analyzed via a numerical approach. However, the material assumptions for past computational human femur simulations were mainly homogeneous. Thus, this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique. A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique. An… More > Graphic Abstract

Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829 - 22 March 2021

Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D More >