Abstract
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.
Highlights
Medical balloon is a device used for interventional surgery and has been widely used in many medical fields [1]. e balloon has extremely demanding performance requirement, and great attention has been paid to the balloon forming process investigation to form high-quality products
Results and Discussion. e predictive models of the wall thickness uniformity were developed as a function of forming temperature, forming pressure, stretching distance, and holding time. ere are two different techniques—artificial neural network (ANN) and nonlinear regression (NLR)—that can be used to develop these models for the estimation of the wall thickness distribution of the balloon
To optimize the wall thickness distribution of medical balloon, the influence of stretch blow molding process on the wall thickness uniformity of kyphoplasty balloon was investigated. e 16 sets of orthogonal test schemes were researched based on the L16(44) Taguchi method orthogonal table by selecting the four main parameters—forming pressure (P), forming temperature, stretching distance, and holding time of the process
Summary
Medical balloon is a device used for interventional surgery and has been widely used in many medical fields [1]. e balloon has extremely demanding performance requirement, and great attention has been paid to the balloon forming process investigation to form high-quality products. Lalli [8] researched the relationship between wall thickness and four main stretch blow molding parameters and expounded the relationship between burst pressure, wall thickness, and compliance. Fu et al [9] analyzed the influence of process parameters on wall thickness through numerical simulation based on the orthogonal design method and established the regression model of wall thickness. There are some researches on the forming processes for optimizing balloon characteristics such as pressure, wall thickness, and compliance. Another important performance of the balloon, the uniformity of wall thickness distribution, is rarely studied. Another important performance of the balloon, the uniformity of wall thickness distribution, is rarely studied. is performance limits burst pressure and affects flexibility. e reason is that the thicker the balloon wall, the greater the bursting pressure, and the worse the flexibility, and if the balloon is too thin, the burst pressure will be low
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