Guangdong Zhang

This paper presents a Multi-Load-Case topology optimization framework for aluminum alloy wheels to overcome the limitations of empirical rear-cavity lightweight designs. To balance structural integrity and mass reduction without altering the aerodynamic outboard styling, a region-constrained topology optimization was conducted. By employing a compromise programming method based on strain energy ratios, the weighting coefficients for bending fatigue and 13° impact load cases were scientifically calibrated. Furthermore, manufacturing constraints, including a 10 mm minimum member size and draw directions, were incorporated to ensure direct compatibility with conventional casting. The results demonstrate that the optimized depth-gradient cavities successfully reduced the peak von Mises stress for bending fatigue by 19.25% (from 133.9 to 108.13 MPa) and for the 13° impact by 14.57% (from 74.63 to 63.76 MPa), alongside a slight mass reduction of 0.53%. This approach offers a mathematically rigorous and industrially viable paradigm for the multi-objective lightweight design of high-performance automotive components.

While anisotropic extrudate swell in polymer processing is fundamentally driven by physical viscoelastic recovery, this paper proposes a theoretical framework to explicitly isolate and map the purely geometric and kinematic components of this phenomenon. Serving as a mathematical proof-of-concept, a multi-velocity-centre hypothesis is proposed. By introducing a semi-empirical, lumped material-flow calibration parameter, the macroscopic diameter swell ratio is mathematically extended to the discrete local flow field of a rectangular slit die. To evaluate its validity, the analytical framework is subjected to a numerical test for kinematic consistency utilizing isothermal, inelastic power-law fluid CFD simulations, thereby separating geometric mapping from complex viscoelastic stress relaxation. Results indicate that analytical predictions show good agreement with CFD data (error < 5%) strictly within the core zone of high-aspect-ratio dies. However, due to the infinite-slit assumption, 3D flow kinematics near die edges induce velocity decay, leading to local deviations that require future empirical corrections. Although comprehensive physical extrusion experiments and non-isothermal viscoelastic coupling are required for industrial deployment, this semi-empirical kinematic mapping provides a foundational mathematical basis that could potentially inform future inverse die-profile design and shape distortion compensation.

Automotive wheels are critical components for vehicular safety, with the rim subjected primarily to radial bending loads during operation. Balancing rim thickness and structural integrity under these loading conditions is imperative. This paper develops and validates an innovative shape optimisation approach utilising the 90° wheel impact test methodology. A simplified 2D finite element model was formulated for rim optimisation to address the computational inefficiency of conventional 3D simulation-based optimisation in product development while exploiting rim geometry’s rotational symmetry. The design of experiments technique was employed to identify key stiffness-influencing factors and their interactions. Subsequent shape optimisation, guided by analytical insights, yielded an engineered rim configuration. Comparative 3D simulations of 90° impact performance demonstrated a 0.51 mm reduction in inner rim flange deformation alongside a 59 g mass reduction, achieving dual objectives of enhanced structural performance and lightweighting. This streamlined optimisation methodology significantly enhances development efficiency while providing engineers with critical insights into parametric influences on rim strength characteristics.

Raindrops on the windscreen significantly impact a driver’s visibility during driving, affecting safe driving. Maintaining a clear windscreen is crucial for drivers to mitigate accident risks in rainy conditions. A real-time rain detection system and an innovative wiper control method are introduced based on machine vision and deep learning. An all-weather raindrop detection model is constructed using a convolutional neural network (CNN) architecture, utilising an improved YOLOv8 model. The all-weather model achieved a precision rate of 0.89, a recall rate of 0.83, and a detection speed of 63 fps, meeting the system’s real-time requirements. The raindrop area ratio is computed through target detection, which facilitates the assessment of rainfall begins and ends, as well as intensity variations. When the raindrop area ratio exceeds the wiper activation threshold, the wiper starts, and when the area ratio approaches zero, the wiper stops. The wiper control method can automatically adjust the detection frequency and the wiper operating speed according to changes in rainfall intensity. The wiper activation threshold can be adjusted to make the wiper operation more in line with the driver’s habits.

Car wheels are crucial to automobile safety, requiring a rigorous test before production. A 90° impact test simulates the frontal impact and its influences on the wheel structure, replicating dynamic driving conditions. A novel modelling approach is illustrated for the 90° impact test simulation and investigates the feasibility of excluding the tyre. Firstly, the test performs and validates five impact simulations, with the maximum deformation error being 7.04%. Subsequently, with additional tests, the effectiveness of the modelling approach is evaluated. Lastly, the effect of tyres has been analysed from an energy perspective. The results indicate that the tyre significantly influences the simulation results and should be included in the finite element (FE) model, which differs from the 13° impact test.

The main challenge in a polymer coextrusion process is to have a good die design prior to the process, which can minimize the geometric errors that are caused by extrusion swell and interface motion. For this purpose, a coupling method of optimization and inverse design for a coextrusion die was studied for a medical striped catheter. In the study, the main material was thermoplastic polyurethane (TPU), and the auxiliary material was TPU filled with 30 wt% barium sulfate. An overall optimization design method was used to optimize the geometry of the extrusion die channel for the striped catheter, which had a complex geometry. In the global optimization process, the local inverse design method was used to design the inlet of the auxiliary material. The non-linear programming by quadratic Lagrangian (NLPQL) algorithm was used to obtain the optimal geometric solution of the coextrusion die runner. The experimental verification results showed that the coupling method for coextrusion die design improved the design efficiency of the coextrusion die remarkably. The value of the objective function, which was used to measure the geometric error of the product, was reduced by 72.3% compared with the initial die design.