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Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms.

机译:使用元启发式算法的功能渐变牙科植入物的最佳梯度材料。

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摘要

Despite dental implantation being a great success, one of the key issues facing it is a mismatch of mechanical properties between engineered and native biomaterials, which makes osseointegration and bone remodeling problematical. Functionally graded material (FGM) has been proposed as a potential upgrade to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of an FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. However, matching the properties does not necessarily guarantee the best osseointegration and bone remodeling. Little existing research has been reported on developing an optimal design of an FGM dental implant for promoting long-term success. Based upon remodeling results, metaheuristic algorithms such as the genetic algorithms (GAs) and simulated annealing (SA) have been adopted to develop a multi-objective optimal design for FGM implantation design. The results are compared with those in literature.
机译:尽管种植牙取得了巨大的成功,但面临的关键问题之一是工程材料和天然生物材料之间的机械性能不匹配,这使得骨整合和骨骼重塑成为问题。已提出功能梯度材料(FGM)作为对某些常规植入物材料(例如钛)的潜在升级,以便在义齿科中进行选择。 FGM牙科植入物的想法是,该属性将以某种模式变化,以匹配宿主骨骼中不同区域所需的生物力学特性。但是,匹配属性并不一定保证最佳的骨整合和骨骼重塑。关于开发FGM牙科植入物以促进长期成功的最佳设计的现有研究报道很少。基于重塑结果,已采用元启发式算法(例如遗传算法(GA)和模拟退火(SA))为FGM植入设计开发了多目标优化设计。将结果与文献中的结果进行比较。

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