دانلود مقاله ISI انگلیسی شماره 52528
عنوان فارسی مقاله

مدل سازی جانشین تماس مشترک دگردیس پذیر با استفاده از شبکه های عصبی مصنوعی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52528 2015 7 صفحه PDF سفارش دهید 6500 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Surrogate modeling of deformable joint contact using artificial neural networks
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Medical Engineering & Physics, Volume 37, Issue 9, September 2015, Pages 885–891

کلمات کلیدی
تماس الاستیک - مدل سازی جانشین - شبکه های عصبی - بیومکانیک - سطح پاسخ - متا مدل - تماس زانو - مشترک تیبیوفمورال
پیش نمایش مقاله
پیش نمایش مقاله مدل سازی جانشین تماس مشترک دگردیس پذیر با استفاده از شبکه های عصبی مصنوعی

چکیده انگلیسی

Deformable joint contact models can be used to estimate loading conditions for cartilage–cartilage, implant–implant, human–orthotic, and foot–ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input–output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.