کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7179738 | 1467733 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multi-objective optimal design of cycloid speed reducer based on genetic algorithm
ترجمه فارسی عنوان
طراحی بهینه ی چند منظوره کاهش سرعت سیکلوئید بر اساس الگوریتم ژنتیک
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
کاهش سرعت سیکلوئید، الگوریتم ژنتیک، طراحی مطلوب چند هدفه راندمان انتقال، جلد،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
چکیده انگلیسی
The design of cycloid speed reducer has to satisfy various constraints, e.g. the geometrical, the kinematical and the mechanical, while delivering excellent performance, high efficiency, long life, great load capacity, etc. This invokes the need of an optimal design methodology to achieve these objectives collectively. Taking K-H-V cycloid speed reducers as the research subject, an optimization methodology based on genetic algorithm is presented. The goal of the optimization is to simultaneously minimize volume and maximize efficiency. The volume and the efficiency of the reducer are simultaneously taken as objective functions. The design variables including the short width coefficient, the diameter of the pin, the width of the cycloid gear, etc. are defined. The constraint conditions are established for multi-optimizations. In order to elaborate the proposed optimization methodology, the XW3-type cycloid speed reducer is chosen and utilized as an example for optimization. A comparison study on the single optimization is also carried out in this work. The research results are satisfactory and can help designers to employ for minimum material and cost by fulfilling high efficiency and other performance requirements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanism and Machine Theory - Volume 102, August 2016, Pages 135-148
Journal: Mechanism and Machine Theory - Volume 102, August 2016, Pages 135-148
نویسندگان
Jian Wang, Shanming Luo, Deyu Su,