This paper develops a material-structure integrated design and optimization method based on a multiscale approach for the lightweight design of CFRP car doors. Initially, parametric modeling of RVE is implemented, and their elastic performance parameters are predicted using the homogenization theory based on thermal stress, exploring the impact of RVE parameters on composite material performance. Subsequently, a finite element model of the CFRP car door is constructed based on the principle of equal stiffness, and a parameter transfer across microscale, mesoscale, and macroscale levels is achieved through Python programming. Finally, the particle generation and updating strategies in the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm are improved, enabling the algorithm to directly solve multi-constraint and multi-objective optimization problems that include various composite material layup process constraints. Case study results demonstrate that under layup process constraints and car door stiffness requirements, plain weave, twill weave, and satin weave composite car doors achieve weight reductions of 15.85%, 14.54%, and 15.35%, respectively, compared to traditional metal doors, fulfilling the requirements for a lightweight design. This also provides guidance for the lightweight design of other vehicle body components.

Carbon fiber composites, characterized by their high specific strength and low weight, are becoming increasingly crucial in automotive lightweighting. However, current research primarily emphasizes layer count and orientation, often neglecting the potential of microstructural design, constraints in the layup process, and performance reliability. This study, therefore, introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic (CFRP) drive shafts. Initially, parametric modeling of the microscale cell was performed, and its elastic performance parameters were predicted using two homogenization methods, examining the impact of fluctuations in microscale cell parameters on composite material performance. A finite element model of the CFRP drive shaft was then constructed, achieving parameter transfer between microscale and macroscale through Python programming. This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was enhanced for particle generation and updating strategies, facilitating the resolution of multi-objective reliability optimization problems, including composite material layup process constraints. Case studies demonstrated that this approach leads to over 30% weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Lightweight thin-walled energy-absorbing structures play a critical role in passive safety systems for automotive and aerospace engineering applications, yet simultaneously achieving high specific energy absorption and stable crushing behavior remains a persistent challenge. Inspired by the topology of natural honeycombs, this study proposes a novel modular assembled multi-cell carbon fiber reinforced polymer (CFRP) structure (MAMCS), fabricated via a cost-effective modular assembly strategy based on a wrapping process. Quasi-static axial crushing experiments combined with validated finite element simulations were employed to systematically investigate the effects of inner layup configurations ([0°/90°], [30°/−60°], [45°/−45°]), cell number, and inner sub-cell size on crushing behavior. Among the investigated layup configurations, the [0°/90°] inner layup exhibited superior mean crushing force (MCF) and specific energy absorption (SEA). Multi-cell architectures significantly enhanced load-bearing capacity and crushing stability through mechanical interactions among internal sub-cells. Parametric analyses further revealed that enlarging the inner sub-cell size elevates both MCF and SEA, although at the expense of a higher peak crushing force (PCF). A TOPSIS-based multi-criteria decision-making framework was applied to identify a preferred configuration that achieves a favorable balance between peak load mitigation and energy absorption efficiency. The proposed MAMCS, characterized by its simple modular assembly, cost-effective fabrication, and superior crashworthiness performance, offers a promising bio-inspired design strategy for developing high-performance lightweight energy-absorbing structures in axial impact applications.