The transition toward more sustainable engineering solutions is driving the development of structural systems that combine reduced weight with improved mechanical efficiency. Hybrid composite-metal joints have emerged as a promising solution, particularly for high-performance applications such as automotive and aerospace engineering, although challenges remain in simultaneously optimizing their structural performance and environmental sustainability. This work investigates two alternative joint configurations: a conventional design and a reinforced solution incorporating additively manufactured pyramidal lattice inserts. An integrated assessment of mechanical performance and environmental impact is presented by combining experimental characterization with a Life Cycle Assessment using a cradle-to-gate approach. The assessment is based on primary experimental data complemented by background inventory data from the Ecoinvent database. The reinforced configuration exhibited an approximately 80
Bio-inspired geometries provide efficient and multifunctional design solutions; however, translating these complex natural forms into manufacturable digital models remains a significant challenge within conventional Computer-Aided Design workflows. This study proposes a parametric geometric modeling framework for generating bio-inspired geometries with integrated surface textures specifically intended for additive manufacturing. The proposed methodology consists of three sequential stages: (1) generation of biomimetic geometries from mathematical parametric surfaces, (2) creation of complex texture patterns through parametric formulations, and (3) integration of both components into a unified high-resolution geometric model suitable for fabrication. The framework is validated through representative case studies, including the development of a bio-inspired motorboat hull and multiple textured biomimetic surfaces. The resulting geometries were computationally generated, implemented in Python code, and successfully manufactured using fused deposition modeling, demonstrating the capability of the proposed approach to efficiently generate diverse design alternatives while preserving geometric fidelity and manufacturability. Unlike conventional sequential modeling approaches, the proposed method integrates global shape generation and local surface texture within a single mathematical formulation, enabling rapid morphological exploration during conceptual design and supporting Design for Additive Manufacturing. The proposed framework contributes to computational design by providing a unified mathematical strategy for texture-driven bio-inspired geometric modeling that expands the design space while reducing the complexity associated with traditional CAD modeling of organic geometries.
Parallel-shaft-planetary gear transmission systems are widely used in helicopter main reducers. When the load is not evenly shared among gear pairs, local overload, vibration, noise and reduced fatigue life may occur. To study this issue, a 36-degree-of-freedom bending-torsion coupled dynamic model of a parallel-shaft-planetary gear transmission system with spline-shaft connections is developed using the lumped-parameter method. The model includes time-varying mesh stiffness, mesh damping, backlash, equivalent mesh error, eccentricity error, support stiffness and spline-shaft torsional stiffness. To evaluate the load-sharing behaviour of different branches, load-distribution coefficients are defined for the input-stage gear pairs, planetary external gear pairs and planetary internal gear pairs. The effects of excitation parameters and operating conditions, such as rotational speed, acceleration and load torque, are then analysed through MATLAB-based dynamic calculation and ADAMS multibody simulation. The results show that the input-stage gear pairs are more prone to load imbalance than the planetary-stage gear pairs. This is mainly related to the dual-input structure, phase difference and high-speed excitation. Increasing speed and acceleration tends to worsen load-sharing performance, while increasing load torque improves relative load sharing within the studied range. The results also indicate that backlash and mesh stiffness need to be properly optimized rather than simply increased. Excessive backlash can strengthen impact excitation, while overly large mesh stiffness may make the system more sensitive to dynamic errors. This study demonstrates good numerical consistency between the analytical dynamic model and the multibody simulation. Experimental validation will be carried out in future work.
Fused Deposition Modeling (FDM) three-dimensional (3D) printing has made significant progress in the fabrication of complex structures and additive manufacturing processes. However, improper printing parameter settings can lead to defects, which are often complex in shape and prone to variation. Timely detection of these defects and the ability to halt the printing process are crucial for improving efficiency and reducing costs. Therefore, this study first analyzes six common surface defects in FDM 3D printing, which exhibit distinct multi-scale characteristics. A publicly accessible defect dataset is constructed by collecting and processing defect images. To address the challenges of multi-scale defect detection, an improved YOLOv8-based detection model is proposed. This model integrates Deformable Convolutional Networks (DCNv2), Multi-Scale Dynamic Attention (MSDA), and a lightweight Adaptive Downsampling (ADown) module, aiming to optimize both detection accuracy and computational efficiency to ensure high performance in real-time applications. Experimental results demonstrate that the proposed model achieves a mean Average Precision at an Intersection-over-Union threshold of 0.5 (mAP0.5) of 88.5
This simulation based-study presents an integrated computational framework that links injection moulding process optimisation with the thermo-mechanical performance of a glass fibre-reinforced PA66 automotive throttle cable bracket. Unlike conventional approaches that optimise moulding parameters solely based on flow-related responses, this work establishes a quantitative process–performance relationship by coupling mould-flow behaviour with structural response under realistic service conditions. Finite element analysis (FEA) was conducted to evaluate the influence of mechanical loading (50–150 N) and temperature (23–120 °C) on component performance. It was found that temperature is the dominant factor, with stress increasing significantly from 4.041 MPa at 23 °C to 104.600 MPa at 120 °C due to temperature-independent properties with stresses from constrained thermal expansion effects. The effects of the processing settings on shear stress at the end of fill, filling time and weld line were further studied using a Taguchi L27 orthogonal array. The channel shape was shown to be the major element affecting the flow behaviour. It was observed that an elliptical channel reduced the shear stress to 0.38 MPa and fill time to 4.135 s. Lower shear stress and better flow uniformity reduce fibre degradation. This helps to improve structural integrity at higher temperatures. Injection pressure was the sole statistically significant factor impacting the weld line angle. This study presents a performance-driven optimisation technique for injection-moulded composite components by directly correlating moulding parameters to thermo-mechanical performance. This approach could identify processing parameters that enhance both manufacturability and in-service reliability, providing practical insights for the design of high-performance polymer components for automotive applications.
