Performance Optimization Of Bicomponent Degradable Ureteral Stents

The bicomponent biodegradable ureteral stent tube is a common surgical medical device,one end of which is placed in the bladder and the other end is placed in the renal pelvis to duct and support the ureter and divert urine in body.After recovery of ureter,biodegradable ureteral stents can be completely degraded by a series of chemical reactions.This feature avoids the damage to body caused by extraction operation and solves related problems caused by indwelling ureter.Bicomponent stent tube is formed by weaving and post-processing of different materials.This stent tube has good performance in mechanical robustness,biocompatibility and controllable biodegradable.Compression performance and tensile performance are the most significant properties of ureteral stents.Due to the stent tube plays a supporting role in the human body and different postures will cause the stent tube to be compressed with different strengths,so compression performance of tube is particularly important.The stent tube suffers axial tension during the process of implantation.Stent tube could come away and bring about damage for patients while tube cannot meet the requirements of tensile performance.This thesis is committed to analyze the mechanical properties of biodegradable ureteral stents and find the optimal parameter of mechanism model via intelligent optimization algorithm(1)The design and preparation of the bicomponent biodegradable ureteral stent tube was studied from three aspects: the selection of raw materials,the braiding molding and the posttreatment process.We discussed the current status of intelligent optimization algorithm and the basic principle of gray wolf optimization algorithm.We analyzed the disadvantages of the gray wolf optimization algorithm mathematically which provides a theoretical basis for the improvement of this algorithm.(2)In order to balance exploration and exploitation of grey wolf optimizer(GWO),a dimension learning-based hunting(DLH)search strategy and a nonlinear control parameter strategy were combined to improve the algorithm.In addition,an improved location update method was been proposed,which solve the problem of premature convergence.To verify the superiority of this novel algorithm,we used multiple test functions for analysis and comparison.Moreover,this novel algorithm named improved exploration-enhanced grey wolf optimizer(IEE-GWO)was utilized to improve the accuracy of the mechanism model.(3)On the one hand,we discussed and analyzed the axial stretching mechanism model of ureteral stent.On the other hand,a nonlinear random parameter control strategy was proposed to balance exploration and exploitation of algorithm.Moreover,the influence of different degrees of nonlinear control strategies on the algorithm was further analyzed and a novel algorithm named improved random grey wolf optimizer(IR-GWO)was proposed.We used unimodal function and multimodal function to verify the superiority of the algorithm.This algorithm further improves the accuracy of the axial stretch model.