In order to improve the lightweight level of the automotive floor, reduce material application cost, and improve integrated process manufacturing performance through structural design and optimization, this article proposes a design method to link conceptual design and detailed design and optimize the composite floor by combining free size optimization and size optimization methods. The basic theory of composite mechanics is expounded from the stress-strain theory of single-layer plates, and the stiffness and strength theory of laminated plates, which provides theoretical support for the structural design, material design, and allowable value design of composites. The mechanical properties of CFRP were tested to obtain the basic material parameters of CFRP T300/5208. With the material parameters, the CFRP floor super layers are established in Optistruct software. The shape of the floor super layers is optimized by using the free size optimization method, with the body-in-white (BIW) lightweight coefficient as the objective and the BIW performance as the constraints. The BIW lightweight coefficient is reduced from 4.35 to 4.20 after free size optimization, and the layer blocks shape is obtained and clipped based on engineering application. With the floor mass as the objective and the BIW performance as the constraints, the size optimization of the floor layer blocks thickness is optimized. Then the number of floor layers is obtained, and the CFRP floor is established in Fibersim software. Use the simulation analysis method to compare and verify the performance of the floor before and after optimization. The results show that the failure index of the floor is far less than the failure standard, while the mass of the CFRP floor is reduced by 6.8 kg compared with the original steel floor, which an improvement rate reaching 27.5%. The design and optimization methods presented in this article provide a reference for the design and application of the CFRP floor.

A method for parameter identification of the magnetorheological damper (MRD) model with an improved firefly algorithm (IFA) is proposed, and a semi-active seat control system with three-degree-of-freedom (3-DOF) is established by combining with a quarter car model to investigate the ride comfort. The dynamic characteristics of the MRD were analyzed by experimental method. Combined with the IFA, the parameters of the MRD phenomenon model were identified, and the forward model of the MR damper was constructed. The semi-active control model of a 3-DOF seat suspension was established. The MRD controller and suspension system controller were designed. The passive control, PID control, and Fuzzy-PID control on the vibration reduction of the semi-active seat suspension were compared and analyzed, under different road excitation. The simulation results show that the semi-active seat suspension controlled by the PID and Fuzzy-PID can effectively reduce the seat acceleration and dynamic stroke, which significantly improve the ride comfort and operation safety compared to the passive seat suspension.

Nowadays, parallel mechanisms are widely used in many fields because of their excellent structural performance. In order to improve the comprehensive performance of 6-UPS parallel mechanism, this article proposes a multi-objective optimization design method for parallel mechanism based on the Taguchi method and entropy-weighted gray relational analysis (EGRA) method. By establishing a parametric model of the 6-UPS parallel mechanism, taking the peak force on the drive pair of the drive branch chain of the mechanism, the minimum value of the projection angle of the body-fixed coordinate system (BCS) relative to the inertial coordinate system (ICS), and the minimum value of the average projected angle of the BCS relative to the ICS as the objective functions, the relationship between the design variables and the objective function is investigated under the condition that the constraints are satisfied. Using the optimization method proposed in this article, the multi-objective optimization problem is transformed into a single-objective optimization problem based on gray relational grade (GRG). Compared with the non-optimized 6-UPS parallel mechanism, the simulation results show that the peak force on the drive pair of the drive branch chain is reduced by 17.73%, and the minimum value of the projected angle and the minimum value of the average projected angle of the BCS relative to the ICS are increased by 27.36% and 36.17%, respectively, which effectively improves the load-bearing capacity and motion range of the 6-UPS parallel mechanism and verifies the reliability of the optimized design method.

In order to improve the lightweight level, crash safety performance and optimization design efficiency of body-in-white (BIW), this article proposes a lightweight multi-objective optimization design method for mixed-material body. The implicit parametric model of the BIW is created by using SFE-CONCEPT software, and the validity and correctness of the model are verified by tests. Material, shape and dimension parameters are introduced as design variables for design of experiments (DOEs), and 26 important design variables are screened out by combining contribution analysis with nonlinear main effects analysis. The approximate model method is used to fit the Kriging surrogate model and the RBF surrogate model, and it is found that the RBF surrogate model can better reflect the relationship between nonlinear crash performance and optimization variables. A hybrid method combined entropy weighted grey relational analysis (EGRA) with modified non-dominated sorting genetic algorithm (MNSGA-II) is proposed to carry out the lightweight multi-objective optimization of BIW in front crash and side impact, which improves the population diversity of multi-objective optimization problems and quantifies the comprehensive performance of each scheme. Comparing and analyzing the optimization platform recommending scheme, the technique for order preference by similarity to an ideal solution (TOPSIS) method preferring scheme and the EGRA method optimum scheme, it is found that EGRA method can obtain the optimal compromise scheme, and the performance improvement of the BIW are more obvious and the improvement rates are also more balanced. The results verify the feasibility of the ranking method, avoid the blindness of optimal solution selection, and establish an objective evaluation method of multi-objective optimization results. The optimization results show that the improvement rates of the BIW lightweight coefficient, the average value of the maximum acceleration of the B-pillars on both sides during the front crash, and the maximum intrusion displacement of the B-pillar chest during the side impact have reached 11.5%, 6.5%, and 6.8%, respectively. Other performance response improvement rates are also above 3.3%, the lightweight and crash safety performance are significantly improved.

Supplementary pollination is essential for achieving optimal yields (> 70 t ha-1) and quality in greenhouse tomato production. Colony collapse disorder affects natural pollinators, creating an urgent need for supplementary mechanical pollination alternatives, driving significant research and application development. This review systematically analyzes the current research and applications of three mechanical pollination approaches: vibrator-based, ultrasonic, and airflow methods. Vibration systems typically achieve 60-85% pollen release efficiency and fruit set rates comparable to bumblebee pollination. Ultrasonic devices enhance pollen shedding by approximately 10-25% over conventional vibrators. Airflow-based methods provide 30-50% wider pollen coverage and 2-4 times higher operational efficiency, but with lower pollen deposition precision. Cost comparisons indicate that device prices range from $80-300 for vibrators, $300-$1200 for ultrasonic tools, and $150-$1500 for airflow devices. Despite rapid progress, research gaps remain in pollen release mechanisms, pollen transport modelling, and intelligent control algorithms. Future studies should prioritize mechanistic modelling of tomato pollination, the development of energy efficient and cost-effective pollination devices, and the integration of intelligent robotic systems for fully automated mechanical pollination.