دانلود مقاله ISI انگلیسی شماره 78434
ترجمه فارسی عنوان مقاله

تجزیه مشهای اسمبلی اسکن شده مبتنی بر شناخت دوره تناوب و کاربرد آن در مدل سازی شبیه سازی ترکیب حرکتی

عنوان انگلیسی
Decomposing scanned assembly meshes based on periodicity recognition and its application to kinematic simulation modeling
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
78434 2013 14 صفحه PDF
منبع

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

Journal : Computer-Aided Design, Volume 45, Issue 4, April 2013, Pages 829–842

ترجمه کلمات کلیدی
اسکن X-ray CT - اسمبلی؛ شناخت دوره تناوب؛ سنتز هندسی؛ شبیه سازی ترکیب حرکتی
کلمات کلیدی انگلیسی
X-ray CT scanning; Assembly; Periodicity recognition; Geometric synthesis; Kinematic simulation
پیش نمایش مقاله
پیش نمایش مقاله  تجزیه مشهای اسمبلی اسکن شده مبتنی بر شناخت دوره تناوب و کاربرد آن در مدل سازی شبیه سازی ترکیب حرکتی

چکیده انگلیسی

In this paper, we focus on CT scanned meshes of gear assemblies as examples, and propose beneficial methods for establishing such advanced inspections. We first propose a method that accurately decomposes the mesh into partial meshes, each of which corresponds to a single gear, using periodicity recognitions. The key idea is first to accurately recognize the periodicity of each gear, then to extract sets of topologically connected mesh elements where periodicities are valid, and finally to interpolate points in plausible ways from an engineering viewpoint to the area where surface meshes are not generated, especially the contact area between parts in the CT scanning process. We also propose a method for creating kinematic simulation models which can be used for a gear teeth contact evaluation using extracted partial meshes and their periodicities. Such an evaluation of teeth contacts is one of the most important functions in kinematic simulations of gear assemblies for predicting the power transmission efficiency, noise and vibration. The characteristics of the proposed method is that (1) it can robustly and accurately recognize periodicities from noisy scanned meshes, (2) it can estimate the plausible boundaries of neighboring parts without any previous knowledge from single-material CT scanned meshes, and (3) it can efficiently extract partial meshes from large scanned meshes containing millions of triangles in a few minutes. We demonstrate the effectiveness of our method on a variety of artificial and real CT scanned meshes.