In order to improve the crashworthiness of the crash box, the cattle horn is selected as a bionic object. A crash box filled with negative Poisson's ratio honeycomb based on horn structure was designed by extracting its excellent structural characteristics of impact resistance. The axial deformation modes and energy absorption characteristics of bionic crash box are investigated by means of finite element method, and compared with square crash box, diagonal crash box and side-to-side crash box. Under axial impact, the horn-bionic crash box shows significantly better load-carrying and energy-absorbing capacity than the square crash box and the diagonal crash box. On this basis, the thickness parameters, multi-objective optimization design and structure improvement design of horn-bionic crash box are studied successively in this work. The results show that the improved bionic crash box based on horn structure has a typical progressive shrinkage deformation mode. Its SEA reaches 29.9 kJ/kg, which is 2.5 times that of the square crash box, and its PCF is also greatly reduced from 542.4 kN to 391.1 kN. Therefore, the methods of structural improvement such as pre-deformed treatment, constraint beam adjustment and shortening the NPR core can effectively reduce the PCF of the crash box, and improve its energy absorption capacity, so as to obtain better comprehensive crashworthiness. Introducing the key characteristics of the horn structure into the design of the car crash box can effectively improve structural crashworthiness and provide new design guidance for the other energy-absorption components of vehicle.

In this study, the thermal stability and combustion performance of basalt fiber reinforced polypropylene (BFRPP) composite and pure polypropylene (PP) were compared. The results show that the basalt fiber has no positive effect on increasing the initial decomposition temperature of PP, but it could reduce the maximum thermal decomposition rate and increase the temperature of the maximum thermal decomposition rate. Adding basalt fiber to PP could slightly reduce the limiting oxygen index. At the same oxygen concentration, the BFRPP burned significantly more slowly than the PP. In addition, during the combustion, it was observed that the BFRPP showed a better anti-melt dripping effect than the PP. The results from the cone calorimeter test show that, under the same external heat flux, the time-to-ignition (TTI) of BFRPP was less than that of PP. This indicated that BFRPP was easier to ignite than PP. It was also found that the reciprocal of the square root of the TTI of both has a linear relationship with external heat flux. BFRPP has a lower peak heat release rate and total heat release than PP. Moreover, BFRPP produced less smoke than PP when burned.

Bionic and gradient designs offer promising applications in honeycomb structures. The intersection unit of the beetle elytra is extracted as to enhance regular hexagonal honeycomb (RHH), and several bionic honeycombs are proposed. Finite element (FE) modeling approach is verified through formulae and experiments. Curves of force and displacement of bionic honeycombs present two prominent stages under medium- and low-speed impacts in plateau stage, and several bionic honeycombs exhibit a zero or negative Poisson’s ratio. The specific energy absorption (SEA) of INT_6, whose crashworthiness is best in bionic honeycombs. A gradient design of INT_6 is implemented to further increase its crashworthiness. Multi-objective optimization design (MOD), which aims to simultaneously increase the SEA and reduce the peak crushing force, is adopted to determine the optimal parameters of each layer of graded INT_6. The SEA of the optimized graded INT_6 increase 142%, and its peak crushing force decrease by 25.4% compared with those of the INT_6.

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function $J\left({\psi }_{ij}\right)$—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems.

In this work, the mechanical and dynamic thermomechanical properties of PEEK based on FDM are experimentally investigated and evaluated comprehensively. The tensile failure mechanism of PEEK prepared by FDM and extrusion modeling (EM) was analyzed by fracture morphology observation. By conducting a differential scanning calorimetry (DSC) test, the crystallinity of PEEK prepared by FDM and EM was measured. The dynamic thermomechanical properties of PEEK were tested and analyzed by dynamic mechanical analysis (DMA). For FDM-prepared PEEK samples, the yield strength and elongation were 98.3 ± 0.49 MPa and 22.86 ± 2.12%, respectively. Compared with the yield strength of PEEK prepared by EM, the yield strength of PEEK prepared by FDM increased by 65.38%. The crystallinity of FDM-prepared and EM-prepared samples was calculated as 34.81% and 31.55%, respectively. Different processing methods resulted in differences in the microscopic morphology and crystallinity of two types of PEEK parts, leading to differences in mechanical properties. The internal micropores generated during the FDM processing of PEEK significantly reduced the elongation. Moreover, according to the DMA results, the glass transition activation energy of PEEK was obtained as ΔE = 685.07 kJ/mol based on the Arrhenius equation. Due to the excellent mechanical properties of PEEK prepared by FDM processing, it is promising for high-performance polymer applications in different fields.