Research On The Molding Process Of Thermosetting Prepreg Based On Prepressing Forming And Hot Pressing Curing

Due to the high specific strength,high specific modulus,good corrosion resistance,and high fatigue strength of thermosetting composite materials,they have been widely used in aerospace,new energy vehicles,and other fields.The prepreg compression molding based on prepressing forming and hot pressing curing can realize the integrated rapid molding of components and materials simultaneously,thus it has been widely used.During the prepressing forming and hot pressing curing processes of thermosetting prepregs,the distribution law of prepreg fiber bundles,the uniformity of the internal temperature/curing degree field of the composite materials,and the detection and control of process parameters all have a significant impact on the quality of the composite materials.In the process of prepressing forming,it is necessary to study the mechanical properties and characterization methods of prepreg prepressing forming,and to study the deformation law of prepreg under variable loading rate and variable blank holder force.During the hot pressing curing process,it is necessary to establish a curing process analysis model and design effective process optimization methods.In the inspection of molding process,it is necessary to design efficient process parameter detection methods and solve the real-time detection problem of surface defects in prepressing forming bodies under the condition of very small and imbalanced samples.Therefore,in order to improve the efficiency and quality of the prepressing forming and hot pressing curing process of thermosetting composites,the above-mentioned aspects are studied in this paper.In order to characterize the prepressing forming performance of thermosetting prepreg accurately and analyze the influence of loading rate and blank holder force on the deformation of prepreg,an in-plane shear deformation analysis model based on anisotropic hyperelastic constitutive model is established.The validity of the constitutive model is verified by bias extension test,and the influence of loading rate on the in-plane wrinkles and deformation of prepreg is analyzed.A deformation analysis model of prepressing forming process is established to predict the shear angle changes and boundary contour distribution of prepressing forming bodies accurately throughout the process,and the validity of the deformation analysis model of prepreg prepressing forming is verified through prepressing forming experiments,and the regularities of the blank holder force and loading rate on the formation of boundary contour and wrinkles of the prepressing forming bodies during the prepressing forming process are further explored.In order to analyze the process of prepreg hot pressing curing molding,a finite element analysis model of the curing process is established based on the multi-physical field coupling characteristics of thermosetting prepreg during the hot pressing curing process.For the efficient optimization of the prepreg hot pressing curing process,a sample set containing process parameters and curing process parameters is obtained by using the Latin hypercube sampling method.The surrogate models based on back propagation(BP)neural network,genetic algorithm combined with BP(GA-BP)neural network,and radial basis function(RBF)neural network are designed to achieve high-precision prediction of curing molding process parameters.Meanwhile,the key process parameters that affect the objectives are determined using the global sensitivity analysis method.A multi-objective optimization method combining surrogate model and the fast and elitist Non-dominated Sorting Genetic Algorithm(NSGA-II)is designed to obtain the Pareto front including the optimal points.The validity of the optimization results is verified,and the uniformity of the temperature field and curing degree field during the curing process is improved.In order to achieve the detection and control of process parameters in the prepreg prepressing forming and hot pressing curing processes,a detection method based on image processing,deep learning,and online fiber bragg gratings sensors is designed to detect the prepreg deformation,prepressing forming defects,and curing process.In order to measure the shear angle and tensile displacement between fiber bundles at various locations of bias extension test specimens and prepressing forming bodies,the shear angle measurement is implemented based on image preprocessing and feature extraction,and the tensile displacement measurement is obtained based on image preprocessing and centroid extraction.For the real-time detection of surface defects on the prepressing forming bodies with extremely small and imbalanced samples,a defect detection model combining the YOLOv5 network with the coordinate attention mechanism module and the Ghost Bottleneck lightweight module is designed.A visual platform is built to obtain high-quality images of surface defects on the prepressing forming bodies,and a surface defect sample set that conforms to the actual situation is constructed to achieve rapid and high-precision recognition of various surface defects.In order to monitor the hot pressing curing process,fiber bragg gratings are used as temperature and strain sensors,embedded in the laminate during manufacture to obtain temperature values and curing strain values during the curing process,and the parameter detection of the hot pressing curing process is achieved.In this paper,the mechanical properties and characterization methods of prepreg prepressing forming are studied,the distribution of boundary contours and changes in shear angle at various locations of the prepressing forming bodies is predicted accurately,and the influence of the loading rate and blank holder force on the deformation of prepreg are investigated.A hot-press curing process analysis model is established,and an efficient multi-objective optimization method for curing forming process is designed.An efficient process parameter detection method is also designed for molding process,the real-time detection problem of the prepressing forming bodies surface defects under the conditions of extremely small and imbalanced samples is solved.The efficiency and quality of thermosetting composite material prepressing forming and hot pressing curing process are improved effectively.The results of this paper can be applied to the analysis of the prepressing forming process and the curing process optimization of complex products,as well as the detection of surface defects of composite materials,a corresponding methodological basis for solving the problems of composite materials forming and process detection in continuous production is provided.