A Model Developed for Predicting Uniformity of Kyphoplasty Balloon Wall Thickness Based on the Orthogonal Test

Autor: Guanghui Dai, Qingqing Zhang, Guobao Jin
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Advances in Materials Science and Engineering, Vol 2020 (2020)
Druh dokumentu: article
ISSN: 1687-8434
1687-8442
DOI: 10.1155/2020/1643080
Popis: In order to optimize the wall thickness distribution of medical balloon, kyphoplasty balloon was chosen as the research object, the uniformity of wall thickness distribution was taken as the evaluation index, and the influence of stretch blow molding process on the uniformity of kyphoplasty balloon was investigated. In this paper, 16 sets of orthogonal test schemes were studied by selecting four main parameters such as forming temperature, forming pressure, stretching distance, and holding time of stretch blow molding process based on the L16(44) Taguchi method orthogonal table. The statistical analysis showed that the forming temperature was an utmost parameter on the uniformity, while an optimal scheme was obtained and an optimal balloon with the uniformity of 95.86% was formed under the scheme. To further quantify the relationship between the uniformity and the parameters, artificial neural network (ANN) and nonlinear regression (NLR) models were developed to predict the uniformity of the balloon based on orthogonal test results. A feed-forward neural network based on backpropagation (BP) was made up of 4 input neurons, 11 hidden neurons, and one output neuron, an objective function of the NLR model was developed using second-order polynomial, and the BFGS method was used to solve the function. Adequacy of models was tested using hypothesis tests, and their performances were evaluated using the R2 value. Results show that both predictive models can be used for predicting the uniformity of the balloon with a higher reliability. However, the NLR model showed a slightly better performance than the ANN model.
Databáze: Directory of Open Access Journals
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