Method for identifying the impact load condition of thin-walled structure damage based on PSO-BP neural network

Abstract

Thin-walled structures (TWS) were widely used in engineering equipment, and may be subjected to impact loads to produce different degrees of structural damage during application. However, it is a difficult problem to determine the impact load conditions for these structural damages. In this study, we developed a novel method of identifying the impact load condition of the thin-walled structure damage, which is based on particle swarm optimization-backpropagation (PSO-BP) neural network. First, the known impact position and velocity are applied to the finite element model (FEM) of the TWS to produce permanent plastic deformation, and to fit the characteristic shape of the deformation is needed by invoking the multivariate polynomial function. Then, the method is devoted to build a basic data set. With impact position and velocity as input and function coefficients as output, a model of extended PSO-BP neural network is established. Besides, the basic sample set is expanded to solve the lack of samples. Ultimately, utilizing the expanded total sample set as training data, function coefficients, impact position and velocity will be outputted. On the basis of the known functional coefficients of deformed surfaces, a model of predictive PSO-BP neural network is established and predicted. Furthermore, we predicted the collision position and velocity using a conventional BP neural network in the same way. Finally, the predicted impact position and velocity is compared with the analysis results of the FEM, which verifies that the PSO-BP neural network algorithm has high accuracy.

Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China

“…64 The BP neural was used are now also widely used in different research areas like the electrical resistivity distribution and Mechanical Engineering. 65 , 66 Combining remote sensing data with sample plot survey data to build a carbon storage inversion model, or a non-parametric estimation method based on machine learning method to estimate forest carbon storage, which can have good development and application prospects. 67 Therefore, this paper will use the FLUS model which is based on an ANN, and the InVEST model to predict ecosystem carbon storage in the Qiantang River source region in 2030, which can provide a case study for how machine learning can be combined with other models for further analysis of ecosystem services.…”

Overview of Friction and Wear Performance of Sliding Bearings

“…Figure 12 shows the double-input single-output BP neural network model. It has become a trend in wear prediction to use intelligent algorithms to optimize the neural network model to improve the prediction accuracy [93,94]. oatings 2022, 12, x FOR PEER REVIEW intelligent algorithms to optimize the neural network model to improve t accuracy [93,94].…”