Robust optimization of total joint replacements incorporating environmental variables.
Robust optimization of total joint replacements incorporating environmental variables.
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DOI：
10.1115/1.2798325
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发表时间：
1999-06
期刊：
影响因子：
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通讯作者：
P. B. Chang;Brian J. Williams;T. Santner;W. Notz;Donald L. Bartel
中科院分区：
文献类型：
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作者：
P. B. Chang;Brian J. Williams;T. Santner;W. Notz;Donald L. Bartel
用于生物力学装置优化设计的直接搜索技术计算量大，需要多次迭代才能收敛到全局解。这一点，再加上多种载荷条件和骨骼特性等环境变量的结合，使得直接搜索技术变得不可行。在这项研究中，我们引入了基于统计设计和计算机实验分析的新方法，以有效地解释环境变量。该方法使用在相对较小的训练地点收集的数据，采用了一种计算成本低廉的结构响应预测器，该预测器是由统计驱动的。通过使用该预测器代替模拟器（例如，有限元模型），可以执行足够数量的迭代，以促进复杂系统的优化。这些方法的适用性通过设计全髋关节置换术的股骨假体来证明，该假体结合了关节力取向和松质骨特性的变化。建立了弹性基础梁有限元模型来模拟结构响应。选择这些简单的模型是因为它们的计算时间短。这使我们能够通过设计和环境变量空间的详尽列举来表示实际的结构响应面，并提供了一种验证统计预测器的方法。我们仅使用16次计算机代码就能准确地预测结构响应和最佳设计。BOEF模型预测的总体趋势与之前的三维有限元计算机模拟、实验和临床结果一致，证明了髓内固定系统的重要特征被捕获。这些结果表明，基于统计的优化方法适用于计算量大的模型的优化研究。
Direct search techniques for the optimal design of biomechanical devices are computationally intensive requiring many iterations before converging to a global solution. This, along with the incorporation of environmental variables such as multiple loading conditions and bone properties, makes direct search techniques infeasible. In this study, we introduced new methods that are based on the statistical design and analysis of computer experiments to account efficiently for environmental variables. Using data collected at a relatively small set of training sites, the method employs a computationally inexpensive predictor of the structural response that is statistically motivated. By using this predictor in place of the simulator (e.g., finite element model), a sufficient number of iterations can be performed to facilitate the optimization of the complex system. The applicability of these methods was demonstrated through the design of a femoral component for total hip arthroplasty incorporating variations in joint force orientation and cancellous bone properties. Beams on elastic foundation (BOEF) finite element models were developed to simulate the structural response. These simple models were chosen for their short computation time. This allowed us to represent the actual structural response surface by an exhaustive enumeration of the design and environmental variable space, and provided a means by which to validate the statistical predictor. We were able to predict the structural response and the optimal design accurately using only 16 runs of the computer code. The general trends predicted by the BOEF models were in agreement with previous three-dimensional finite element computer simulations, and experimental and clinical results, which demonstrated that the important features of intramedullary fixation systems were captured. These results indicate that the statistically based optimization methods are appropriate for optimization studies using computationally demanding models.